Functional Neuroradiology: Principles and Clinical Applications 3031109082, 9783031109089

Table of contents :
Preface
Acknowledgments
Contents
Contributors
Part I: Diffusion and Perfusion Imaging: Physical Principles
1: Physical Principles of Diffusion Imaging
Introduction
Brownian Motion and Molecular Diffusion
Diffusion Encoding and the Stejskal-Tanner Equation
Diffusion Anisotropy
Calculating the ADC
Isotropic DWI and Trace ADC
DWI of the Normal Brain
Current DWI Techniques
Conclusion
References
2: Physical Principles of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MRI
Introduction
T1-Weighted Dynamic Contrast-Enhanced MRI
DCE-MRI Acquisition
DCE-MRI Data Analysis
DCE Pharmacokinetic Modeling
Standardization Efforts and Variability of DCE-MRI
T2/T2*-Weighted Dynamic Susceptibility Contrast MRI
DSC-MRI Acquisition
DSC-MRI Data Analysis
Arterial Input Function
Absolute Quantification
Leakage Effects of GBCAs
Standardization Efforts and Variability of DSC-MRI
Conclusion
References
3: Physical Principles of Non-gadolinium Perfusion Technique (Arterial Spin Labeling)
Introduction
Methodology
Spin Preparation Schemes
PASL
CASL
PCASL
VS-ASL
Imaging Acquisition Schemes
CBF Quantification
Other ASL Considerations
Crusher Gradients
Post-Labeling Delay Time
Higher Field Strength Magnets
Coils
Post-Processing Filters
Vascular Territory Mapping
Conclusions
References
Part II: Diffusion and Perfusion Imaging: Clinical Applications
4: Clinical Applications of Diffusion
Introduction
Basic Mechanisms Underlying Diffusion Changes in Pathologic States
Basic Principles of Diffusion-Weighted Imaging Map Interpretation
Diffusion-Weighted Imaging of Ischemic Arterial Infarcts
Diffusion Characteristics of Ischemic Lesions at Different Stages
Reliability and Pitfalls of Diffusion-Weighted Imaging in Identification of Acute Ischemic Lesions
Reversibility of DWI Positive Lesions in Ischemic Stroke and the Use of DWI for Determination of Infarct Core
Use of DWI for Predicting Hemorrhagic Transformation of Ischemic Infarcts
Correlation of DWI Lesion Volume with Clinical Outcome
Thrombolysis/Thrombectomy and DWI/FLAIR and DWI/T2W Mismatch
Lesion Segmentation and Artificial Intelligence (AI) in Stroke
Transient Ischemic Attacks
Venous Infarctions and Other Vascular Vasogenic Edema Syndromes
Venous Infarctions
Posterior Reversible Encephalopathy Syndrome (PRES)
Hyperperfusion Syndrome Following Carotid Endarterectomy
Transient Global Amnesia
Hemiplegic Migraine
SEIZURES
Metabolic, Toxic, and Drug-Induced Encephalopathies
Hypoglycemia
Heroin Inhalation Leukoencephalopathy (HIL)
Metronidazole-Induced Encephalopathy
Methotrexate-Induced Leukoencephalopathy
Carbon Monoxide Poisoning
Osmotic Myelinolysis Syndromes
Other Syndromes Associated with Alcoholism
Other Drugs and Metabolic Conditions Associated with Restricted Diffusion
Inflammatory Demyelinating Lesions
Cerebral Infections
Abscesses
Meningitis
Encephalitis
Creutzfeldt-Jacob Disease
Cerebral Neoplasms
Tumor Grading, Gliomas, and Intracranial Metastases
Evaluation of Treatment Response and Distinguishing Pseudoresponse and Pseudoprogression
Lymphoma
Extra-Axial Masses
Traumatic Brain Injury
Head and Neck Applications
Characterization of Benign Versus Malignant Head and Neck Lesions
Prediction and Monitoring of Treatment Response
Other Head and Neck Applications
Spine Applications
Conclusion: Future Applications of Diffusion Imaging in the Brain
References
5: Clinical Applications of MR Perfusion Imaging
Introduction
Principles
Dynamic Susceptibility Contrast Perfusion MRI
Dynamic Contrast-Enhanced Perfusion MRI
Arterial Spin Labeled Perfusion MRI
Clinical Applications
Cerebrovascular Disorders
Acute Ischemia
MR Perfusion and Acute Ischemic Syndrome
Luxury Perfusion and Reperfusion Injury
Subacute and Chronic Ischemia
Perfusion Patterns in Stenotic-Occlusive Disease
Cerebrovascular Reserve (CVR)
Territorial Mapping and Collateral Evaluation in Stenotic-Occlusive Disease
Moyamoya
Interventions
Hyperperfusion
Spasm
AVM
Neoplastic Disorders
Diagnosis and Grading
Differentiation of Neoplastic from Non-neoplastic Lesions
Intra-Axial Neoplasms
ASL Perfusion Imaging
Extent of Infiltration: Glioma Versus Metastasis Versus Lymphoma
Extra-Axial Neoplasms
Pre-treatment Planning
Biopsy Guidance
Prognosis
Radiogenomics
Post-Treatment Planning
Response to Therapy and Recurrence/Progression
Post-Treatment Imaging of Brain Tumors: Tumor Recurrence, Pseudoprogression and Radiation Necrosis
Pseudoresponse
Conclusion
References
6: Clinical Application of Perfusion and Diffusion in Stroke
Introduction
Magnetic Resonance Imaging
FLAIR, GRE, and SWI
Diffusion-Weighted Imaging (DWI)
Magnetic Resonance Angiography (MRA)
Perfusion-Weighted Magnetic Resonance Imaging (PWI)
Arterial Spin Labeling (ASL)
Computed Tomography Imaging
CT Angiography (CTA)
CT Perfusion (PCT)
Revascularization Trials
Hemorrhagic Stroke
Conclusion
References
7: Clinical Applications of Dynamic Contrast-Enhanced (DCE) Permeability Imaging
Introduction
T2*-Weighted DSC MRI and T1-Weighted DCE MRI (Combined Approach)
Permeability in Gliomas
Permeability and Glioma Biology and Grade
Permeability for Guiding Stereotactic Biopsy and Radiosurgery
Therapeutic Monitoring
Pseudoprogression
Perfusion and Permeability Monitoring of Novel Antiangiogenic Agents
Tumor and Nontumoral Lesions
Extra-Axial Neoplasms (Meningioma, Schwannoma)
Challenges and Future Perspectives
Conclusion
References
8: CNS Tumor Surveillance and Functional MR Perfusion Imaging
Introduction
Differentiating Gliomas from Non-neoplastic Tumefactive Lesions
Differentiating Gliomas from Metastasis and Lymphoma
World Health Organization Glioma Classification
Assessment of Tumor Grade
Assessment of IDH1/2 Mutation Status
Assessment of Copy Number Variation (CNV)
Assessment of CDKN2A/B Homozygous Deletion (p16 Loss)
Assessment of Epidermal Growth Factor Receptor (EGFR) Anomalies
Assessment of MGMT Methylation
Assessment of Low-Grade Glioma Dedifferentiation
Biopsy Guidance
Predicting Outcome and Treatment Response
Assessment of Treatment Response
Pseudoprogression
Radiation Necrosis
Pseudoresponse
Limitations
References
Part III: Magnetic Resonance Spectroscopy and Chemical Exchange Saturation Transfer Imaging
9: Magnetic Resonance Spectroscopy: Physical Principles
Introduction
Basic Principles of In Vivo MR Spectroscopy
Chemical Shift and J-Coupling
Contrast Mechanism: Echo Time (TE) and Repetition Time (TR)
Main Metabolites of the 1H In Vivo Proton Spectrum
N-Acetyl-Aspartate (NAA) and N-Acetylaspartylglutamate (NAAG)
Creatine (Cr)
Choline (Cho)
Myo-Inositol (mI)
Glutamate (Glu) and Glutamine (Gln)
Lactate (Lac)
Lipids and Macromolecules (LipMM)
Other Metabolites Detectable with 1H MRS
Alanine (Ala)
Aspartate (Asp)
γ(gamma)-Aminobutyric Acid (GABA)
Glucose (Glc)
Glutathione (GSH)
Glycine (Glyc)
Scyllo-Inositol (sI)
Taurine
Additional Peaks
Data Acquisition
Acquisition Methods
2D or 3D Chemical Shift Imaging (CSI)
Editing Techniques
Signal-to-Noise Ratio (SNR) Considerations
Selecting the Region of Interest (ROI)
When to Use What Method?
How to Acquire Good-Quality Spectra
Processing of MR Spectra
Documentation and Storage
Quantitation
Safety
MR Spectroscopy at 3 T
Conclusion
References
10: Magnetic Resonance Spectroscopy: Clinical Applications
Introduction
In Vivo 1H MRS
Magnetic Resonance Spectrum
Chemical Shift
Signal Intensities
Spin–Spin Coupling
Effect of the Choice of Acquisition Parameters on Appearance of the Spectra
1H MRS Localization Techniques
Single-Voxel MRS
MR Spectroscopic Imaging
Acquisition of MR Spectra
Assessment of Metabolite Concentrations
Neurochemical Profile of the Brain Tissue
Applications of In-Vivo 1H MRS in Neuroradiology
Age-Related and Regional Differences in Metabolite Concentrations
Age-Related Differences
Regional, Sex-Related and Hemispheric Differences in 1H MR Spectra
Hypoxic-Ischemic Encephalopathy
Stroke
Neurodegenerative Diseases
Dementias
Huntington’s Disease
Amyotrophic Lateral Sclerosis
Infectious Diseases
Bacterial Infections
Viral Infections
Demyelinating Disorders
Multiple Sclerosis
Acute Disseminated Encephalomyelitis
Epilepsy
Metabolic Disorders
Creatine and NAA Deficiency Syndromes
Phenylketonuria
Succinate Dehydrogenase Deficiency
Maple Syrup Urine Disease
Nonketotic Hyperglycinemia
Canavan Disease
Mitochondrial Disorders
Neurodegenerative White Matter Disorders
Hepatic Encephalopathy
Brain Tumors
Differentiation Between Tumors and Non-neoplastic Lesions
Classification of Brain Tumors
Tumor Grading
Meningiomas
Solitary Metastases
IDH Mutation and 2-HG
Post-Treatment Evaluation
Conclusion
References
11: Chemical Exchange Saturation Transfer (CEST) Imaging
Introduction to Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI)
Neurooncological Applications of CEST MRI
Endogenous CEST Contrasts in Neurooncological Applications
Assessment of Histopathologic Tumor Subtypes with CEST
Therapy Response Assessment, Prognostication, and Outcome Prediction with CEST
Exogenous CEST Contrasts: Glucose-Enhanced MRI of Brain Tumors
pH-Sensitive CEST MRI in Neurooncological Applications
Assessment of Ischemic and Hemorrhagic Stroke Using CEST MRI
pH-Sensitive APT Imaging of Acute Ischemic and Hemorrhagic Stroke
Intracerebral Hemorrhage
CEST MRI of Neurodegenerative Diseases
Alzheimer’s Disease
Parkinson’s Disease
Multiple Sclerosis
Imaging of Other Neurological Disorders Using Glutamate-Sensitive CEST (GluCEST) MRI
Epilepsy
Encephalitis
Conclusion
References
Part IV: Multimodal Functional Neuroradiology
12: Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses
Introduction
Magnetic Resonance Spectroscopy
Metabolic Markers for Intra-axial Brain Tumors
Differential Diagnosis, Preoperative Tumor Grading, and Biopsy Guidance
Therapeutic Monitoring
Diffusion Imaging
Differential Diagnosis, Preoperative Tumor Grading, and Biopsy Guidance
Therapeutic Monitoring and Prognostication
Perfusion Magnetic Resonance Imaging
Role of Perfusion Imaging in Preoperative Tumor Grading and Biopsy Guidance
Therapeutic Monitoring and Prognostication
Intra-axial Brain Mass Diagnostic Strategy
High-Grade Gliomas
Low-Grade Gliomas
Primary Central Nervous System Lymphoma
Brain Metastases
Tumefactive Demyelinating Lesions
Encephalitis
Brain Abscesses
Pitfalls and Special Circumstances
Pediatric Brain Tumors
Post-treatment Imaging
Current Challenges and Future Directions
References
13: Functional Neuroimaging of Epilepsy
Introduction
Positron Emission Tomography (PET)
Focal Cortical Dysplasia
Tuberous Sclerosis Complex
Mesial Temporal Sclerosis
Epileptogenic Tumors
Magnetic Source Imaging or Magnetoencephalography
Focal Cortical Dysplasia
Tuberous Sclerosis Complex
Diffusion Tensor Imaging
Mesial Temporal Sclerosis
Focal Cortical Dysplasia
Single Photon Emission Computed Tomography (SPECT) in Epilepsy
Ictal SPECT: Practical Issues
Ictal and Interictal SPECT
Interictal SPECT Versus Interictal PET
Invasive Functional Assessment: Depth Electrode and Grid Electrode Placement
Conclusion
References
14: Functional Neuroradiology of Traumatic Brain Injury
Introduction
Diffusion Tensor Imaging (DTI)
Proton Magnetic Resonance Spectroscopy (1H-MRS)
Brain Perfusion
Resting-State Functional MRI
Conclusion
References
15: Functional Neuroradiology of Multiple Sclerosis: Non-BOLD Techniques
Introduction
Diffusion-Weighted Imaging/Diffusion Tensor Imaging
Brain
Spine
Magnetic Resonance Spectroscopy Imaging
Perfusion-Weighted Imaging
Dynamic Susceptibility Contrast
Dynamic Contrast Enhanced
Arterial Spin Labeling
Susceptibility-Weighted Imaging
Evolving Techniques
Magnetization Transfer Imaging (MTI)
New Markers of Neuroinflammation
Leptomeningeal Enhancement and New MRI Contrast Agents
PET Markers of Neuroinflammation
CEST-APT
References
DWI
MRS
PWI
SWI
MTR
New Markers of Neuroinflammation
16: Functional Neuroradiology of Psychiatric Diseases
Introduction
Autism
Schizophrenia
Obsessive-Compulsive Disorder
Mood Disorders
Post-traumatic Stress Disorder
Conclusion
References
17: Neuroimaging of Pain
Introduction
Clinical Utility of Functional Neuroimaging
The Specific Problem of Clinical Pain Assessment
Classifications of Neuroimaging
Applying Neroimaging to Clinical Conditions
Molecular Measurement by MR Spectroscopy
Neuroimaging Studies of Pain Utilizing MR Spectroscopy (MRS)
Chronic Low Back Pain
Complex Regional Pain Syndrome Type I (CRPS Type I)
Neuropathic Pain
Chronic Fatigue Syndrome (CFS)
Temporomandibular Disorders and Orofacial Pain
Fibromyalgia (FM)
Temporomandibular Disorder (TMD)
Irritable Bowel Syndrome (IBS)
Vulvodynia (VVS)
Evoked Pain in Healthy Control Subjects
Basal Cerebral Blood Flow by PET, SPECT, and Resting State fMRI (rs-fMRI)
Fibromyalgia (FM)
Chronic Fatigue Syndrome (CFS)
Basal Cerebral Blood Flow by Arterial Spin Labeling (ASL)
Evoked Pain in Healthy Control Subjects
Chronic Low Back Pain
PET Evaluation of Glucose Metabolism and Ligand Binding
Fibromyalgia (FM)
Irritable Bowel Syndrome (IBS)
Temporomandibular Disorder (TMD)
Vulvodynia (VVS)
Anatomic Measurements of Gray and White Matter by Voxel-Based Morphometry (VBM)
Voxel-Based Gray Matter Morphometry (VBM) in Fibromyalgia (FM)
Evaluation of Gray Matter in Chronic Fatigue Syndrome (CFS)
Evaluation of Gray Matter in IBS
Evaluation of Gray Matter in Temporomandibular Disorder (TMD)
Diffusion-Weighted Imaging (DWI)
Diffusion Tensor Imaging (DTI)
DTW and DTI Applications to White Matter Evaluation in Pain
Neuropathic Pain
Temporomandibular Disorder (TMD)
Fibromyalgia (FM)
White Matter Perfusion Using Contrast-Based and ASL Imaging in Fibromyalgia (FM)
Evaluation of White Matter in Chronic Fatigue Syndrome (CFS)
Evaluation of White Matter in IBS
Evaluation of Gray Matter in VVS
Anatomical Measures of Gray and White Matter
Conclusion: Future Considerations
References
18: Quantitative and Physiological Magnetic Resonance Imaging in Glioma
Introduction
Characterization Of Glioma
Histopathological and Molecular Characterization
IDH1 and IDH2 Mutations
1p/19q Codeletion
ATRX and TP53 Mutations
O-6-Methylguanine-DNA Methyltransferase (MGMT) Promoter Methylation Status
Physiological and Ultrastructural MRI Techniques for Glioma Characterization
Diffusion MRI
Perfusion MRI
Magnetic Resonance Spectroscopy
MRI to Detect MGMT Promoter Methylation
Imaging Biomarkers of Malignant Transformation and Progression
Risk Factors
Contrast Enhancement on Conventional MRI
Tumor Volume and Growth Kinetics
Quantitative Imaging Biomarkers
Advanced MRI in Treatment Planning
Evaluation of Therapeutic Response
MRI Biomarkers of Early Treatment Response
Quantitative and Physiological Imaging to Discriminate Progression Versus “Treatment Effect”
Diffusion Imaging to Discriminate Between PD and PsP
Diffusion MRI to Differentiate PD from PsP
Perfusion Imaging as a Discriminator of PD from PsP
MRS as a Discriminator Between PD and PsP
Antiangiogenic Therapy and Non-enhancing Tumor
Immunotherapy Response Assessment
Impression and Conclusions
References
Part V: BOLD Functional MRI: Physical Principles
19: Principles of BOLD Functional MRI
Introduction
Physiological Changes
Functional Imaging Contrasts
T1-Weighted Perfusion fMRI
CBV-Weighted fMRI
T2* and T2-Weighted BOLD fMRI
Intravascular BOLD Component
Extravascular BOLD Component
Spin-Echo Versus Gradient-Echo BOLD
BOLD Versus CBF
Contrast-to-Noise Ratio and Temporal Resolution
Conclusion
References
20: fMRI Scanning Methods
Introduction
Magnetic Resonance Physics
Acquisition Schemes
Spin Echo
Gradient Echo
Echo Planar Imaging
Applications
BOLD
Optimization of BOLD MRI
Z-Shim EPI
Arterial Spin Labeling
Simultaneous Multislice (SMS) or Multiband Imaging
Multicontrast EPI (EPIMix)
Conclusion
References
21: Experimental Design and Data Analysis for fMRI
Introduction
Basic Types of Neuroimaging Inference
Forward Inference
Reverse Inference
Connectivity
Manipulation of the Cognitive Process
Properties of the BOLD fMRI System That Impact Experimental Design
Different Temporal Structures of BOLD fMRI Experiments
Data Preprocessing
Distortion Correction
Slice Acquisition Correction
Motion Correction
Spatial Normalization
Spatial Smoothing
Statistical Analysis
Conclusion
References
22: Challenges in fMRI and Its Limitations
Introduction
MR Physics-Based Limitations in fMRI
Physics-Based Limitations on Spatial Resolution
SNR and Field Strength
Static Field Inhomogeneities
The Effect of Acquisition TR
Physiological Factors Influencing Spatial Resolution
Physiological-Based Limitations/Constraints in fMRI
Blood Oxygenation Changes and Localization
Functional Spatial Limitations
Brain System Dependent Limitations
The Draining Vein Problem
The Initial Dip
Subject Movement
Other Physiological Changes Associated with Brain Activation
Threshold Effects and Localization
Temporal Resolution of the BOLD Response
Pulse Sequences for fMRI: Spatial/Temporal Resolution
Imaging Approaches to Other Physiological Measurements
Arterial Spin Labeling
Sensitivity
Conclusion
References
23: Neurovascular Uncoupling in Functional MRI
Definition of Terms
Introduction
Control of Cerebral Blood Flow
Neurovascular Coupling and Functional Magnetic Resonance Imaging
Assessing the Cerebrovascular Autoregulatory Reserve by Performing Cerebrovascular Reactivity Measurements
Exhausted Cerebrovascular Reserve Associated with Steal Physiology
The Consequences of Exhausted Cerebrovascular Reserve Associated with Steal Physiology on Neuronal Network Mapping Using Task-Based BOLD MRI and BOLD-CVR
Distinguishing Hemodynamics from Embolic Neurological Events
The Long-Term Consequences of Chronic Steal Physiology on the Brain: “The Neurovascular Uncoupling Syndrome”
Conclusion
References
24: Functional Connectivity MR Imaging
Introduction
Standard Methods
Image Acquisition
Seed-to-Whole Brain Connectivity Maps
Preprocessing
Removal of Physiologic Noise
Temporal Filtering
Removal of Time Courses Believed to Be Associated with Noise Sources
Seed Region Definition
Anatomically Defined or Atlas-Based Seed Regions
Task-Based Functionally Defined Seed Regions
Postprocessing
Direct Correlation of Regional Time Courses (Seed-to-Seed)
Data-Driven Approaches
Independent Components Analysis
Other Data-Driven Methods for Parcellation of the Brain
Assessment of Network Properties After Node Identification
Relating Function to Connectivity
Clinical Applications
Conclusion
References
25: Clinical Challenges of Functional MRI
Introduction
Mapping Cognitive Function with Activation Versus Disruption
Technical Considerations
Field Strength
Scan Sequence and Susceptibility
Study and Task Design
Task Selection
Practice Effects
Task Difficulty
Task Choice
Special Task Considerations
Head Motion
Iatrogenic Considerations
Analysis
Within Subject vs. Group Analysis
Dependent Measures
Reproducibility
Conjunction Analysis
Applying fMRI to Clinical Planning
Significance of Signal Localization
Reliability of Signal Adjacent to Pathology
Value of Resting-State fMRI (rs-fMRI)
Relationship to Outcomes
Conclusion
References
Part VI: BOLD Functional MRI: Clinical & Cognitive Applications
26: fMRI of Language Systems: Methods and Applications
Introduction
Some Proposed Clinical Applications of fMRI Language Mapping
Presurgical Applications
Prediction of Outcome in Aphasia
Diagnosis
Monitoring Treatment Effects
Language and Language Processes
Some Common Pitfalls in Language Mapping
Inadequate Analysis of Language Tasks
Inadequate Control Tasks
Lack of Behavioral Data
Survey of Some Common Language Activation Protocols
Language Task: Passively Listening to Words or Sentences
Control Task: Resting
Language Task: Passively Listening to Words or Sentences
Control Task: Passively Listening to Nonspeech
Language Task: Word Generation
Control Task: Resting or Fixation
Language Task: Word Generation
Control Task: Reading or Repeating
Language Task: Visual Object Naming
Control Task: Resting or Fixation
Language Task: Semantic Decision
Control Task: Sensory Discrimination
Language Task: Semantic Decision
Control Task: Phonological or Phonetic Decision
Language Task: Sentence or Word Reading
Control Task: Viewing Letter Strings or Visual Nonletters
Language Lateralization: Normative, Reliability, and Validation Studies
Normative Studies
Test–Retest Reliability
Wada Comparisons
Comparisons with Electrical Stimulation Mapping
Outcome Prediction Studies
Prediction of Language Outcome
Prediction of Verbal Memory Outcome
Illustrative Cases Using Language fMRI For Quantitative Outcome Prediction
Conclusion
Future Applications: Use of fMRI Language Maps in Surgical Planning
References
27: Functional MRI of Language and Memory in Surgical Epilepsy: fMRI Wada Test
Introduction
The IAT: History and Background
Description of a Standardized IAT Protocol
Language Mapping with fMRI as Compared to IAT
Concordance Between fMRI and IAT Language Lateralization
Discordance Between fMRI and IAT Language Lateralization
fMRI Language Task Selection Considerations
Considerations for Determination of fMRI Language Laterality
Pediatric Language fMRI
Prediction of Postoperative Language Outcome Using fMRI
Case Examples of fMRI Language Activation
fMRI Memory Paradigms as Compared to the IAT
fMRI of Visuospatial/Nonverbal Memory
fMRI of Verbal Memory
Multimodality Memory fMRI Paradigms
Using fMRI to Predict Postsurgical Memory Outcome
Case Examples of fMRI Memory Activation
rsfMRI for Presurgical Mapping in Epilepsy Patients
Use of rsfMRI for Language Lateralization
Association of rsfMRI with Postoperative Language Outcome
Association of rsfMRI with Memory Functioning and Postoperative Memory Outcome
rsfMRI and Multidomain Cognitive Outcomes in TLE
rsfMRI in Pediatric Epilepsy Patients
Summary of Findings for rsfMRI for Surgical Epilepsy and as Related to the IAT
Reliability, Validity, and Clinical Factors Pertinent to fMRI Presurgical Mapping
Summary and Conclusions
References
28: Resting State Functional Magnetic Resonance Imaging
Introduction
Neuronal Organization, Activity, and Recorded Network Observations
Visualizing Functional Networks: Blood Oxygenation Level–Dependent Imaging
Data Acquisition
Data Processing and Analysis
Overview of Resting State Networks
Resting-State fMRI in Intelligence and Individuality and Normal Cognition
Resting-State fMRI in Development and Aging
Resting-State fMRI in Disease
Resting-State fMRI in Surgical Planning
Current Status and Outlook: Can Clinical Resting State Move Beyond a “Resting” State?
Conclusion
References
29: fMRI of Human Visual Pathways
Introduction
Functional Organization of the Human Visual System
Retinotopic Organization
Multiple Visual Areas and Pathways
Functional Specialization
Visual System White Matter Tracts and DTI
Clinical Implications of Visual System Organization
Clinical Applications
Methodology
Visual Field Mapping
Functionally Specific Tests
fMRI Workflow (with Details for Visual System Mapping)
Initial Patient Assessment
Diagnostic Imaging
PrefMRI Assessment and Training. Peripherals Setup and Quality Assurance
FMRI Scan Acquisition
Acquisition Q/A
Postprocessing
Display/Report/Archiving
Interpretation/Treatment Guidance
Follow-Up
Specific Pathologies and Issues
Tumor Resection/Radiation
Epilepsy Treatment
Arterio-Venous Malformations
Diagnosis/Interpretation of Vision Pathologies
Sensory Loss vs. Attentional Neglect
Agnosias and Extrastriate Visual Areas
Cerebral Achromatopsia
Stroke
Complicating Factors
Neurovascular Uncoupling (NVU)
Tissue Distortions
Conclusion
References
30: Functional MRI Studies of Memory in Aging, Mild Cognitive Impairment, and Alzheimer’s Disease
Introduction
Episodic Memory
fMRI Studies of Episodic Memory
fMRI of Episodic Encoding: The Role of the PFC
fMRI of Episodic Encoding: The Role of the MTL
fMRI Studies of Episodic Retrieval
fMRI Studies of Episodic Retrieval: The Role of the PFC
fMRI Studies of Episodic Retrieval: The Role of the MTL
fMRI Studies of Episodic Retrieval: The Role of the Parietal Cortex
fMRI Studies to Identify the Neural Correlates of the Component Processes of Episodic Retrieval
Age-Related Changes in Episodic Memory
fMRI Studies of Episodic Encoding and Retrieval in Normal Aging
Episodic Memory in Alzheimer’s Disease and Related Conditions
fMRI Studies of Episodic Memory in Patients with AD
fMRI Studies of Episodic Memory in Patients with MCI
Functional and Resting State Connectivity Associated with Episodic Memory
fMRI Studies of Episodic Associated Connectivity in Healthy Young Adults
fMRI Studies of Episodic Associated Connectivity in Healthy Older Adults
fMRI Studies of Episodic Associated Connectivity in Patients with MCI and AD
fMRI of Episodic Memory Function as a Biomarker: Patients At-Risk for AD and after Therapeutic Intervention
Summary
Semantic Memory
fMRI of the Neuroanatomical Substrates of Semantic Memory
fMRI of Semantic Memory in Normal Aging
fMRI Studies of Semantic Memory in MCI and AD
fMRI Studies of Semantic Memory Associated Connectivity in Healthy Young Adults
fMRI of Semantic Memory in Patients At-Risk for AD and after Therapeutic Intervention
Summary
Working Memory
fMRI Studies of the Neural Basis for Working Memory
fMRI Studies of Working Memory in Healthy Aging
fMRI Studies of Working Memory in MCI and AD
fMRI Studies of Working Memory Associated Connectivity in Healthy Young Adults
fMRI Studies of Working Memory Associated Connectivity in Healthy Aging, MCI, and AD
fMRI Studies of Working Memory in Patients At-Risk for AD and After Pharmacological Intervention
Summary
Methodological Issues in the Use of fMRI in Aging and Dementia Research
Future Directions
Multi-Modal Imaging Studies and Technological Advancements in MRI
Novel fMRI Memory Paradigms
Conclusion
References
31: Brain Mapping for Cognitive Neuroscience and Neurosurgery
Introduction
The Unity Principle: Cortical Processes Are Linked to Mental Events—Lesion Studies to Neuroimaging
Development of Magnetic Resonance Imaging to Visualize Living Brain Structure
The Development of Functional Magnetic Resonance Imaging to Visualize Living Brain Function
The BOLD Response
The “Real Estate” Principle: Functional Specialization from “Bottom-Up” and “Top-Down”
Identification and Preservation of Cortical Areas Specialized for Essential Tasks
Standard of Care and CPT Codes
Tasks for fMRI and Mapping of Essential Functions
An Example of a Multifunction Task Battery
Healthy Volunteers and Patients
Sensitivity of Task Battery: Healthy Volunteers
Sensitivity of Task Battery: Surgical Population
Comparison of Task Sensitivity for Patients and Healthy Volunteers
Accuracy of Task Battery: Comparison with Intraoperative Electrophysiology
Comparison of fMRI, Wada, and Intraoperative Language Mapping
Comparison of fMRI and Visual Fields
Integrative Mapping of Sensory and Motor Functions
Interoperative Mapping of Language Functions
Postsurgical Status
Functional MRI During Loss of Consciousness: A “Voice” for Unresponsive Patients
“Zero-Tolerance” for Error
Functional Mapping During Sedation
Functional Mapping of Minimally Conscious Patients
An Illustrative Case
Objective Assessment of Patient States During a Disorder of Consciousness Based on the Extent of the Network Engaged by a Command to Name Objects
Determination of the Anatomy and Topography of Cortical Areas Specialized for Cognitive Tasks
Conservation of Effects Versus Individual Differences: Generalizing the Results
Method of Cognitive Subtraction
Method of Cognitive Conjunction
Integration of Functionally Specialized Areas Associated with Cognitive Tasks: The Network Approach
Functional Neuroanatomy of Language Processes: A Large-Scale Network
Functional Neuroanatomy of Attention Processes: A Large-Scale Network
Tests of Cognitive Theory Based on Mapping of Neural Correlates
Functional Neuroanatomy of Working Memory: A Fixed- or Variable-Area Network
The N-Back Task and a Test of a Cognitive Theory
Functional Neuroanatomy of Selective Attention: A Neurological Model of Cognitive Interference
The Stroop Task
Functional Neuroanatomy of Executive Processes: Separate or Combined Systems
The Go/No-Go Task
Integration of Temporal and Spatial Information to Map Executive Processes
Integration of ERP and fMRI
Event-Related fMRI
The Oddball Task
The Functional Neuroanatomy of Very High Level Cognitive Processes
Conclusion
References
32: fMRI of the Central Auditory System
Introduction
Anatomical Organization of the Central Auditory System
Subcortical Organization
Cortical Organization
Primary Core Region
Nonprimary Belt and Parabelt Regions
Technical Challenges Involved in Auditory fMRI
Sound Presentation in the MR Scanner
Safe Scanning of Patients Who Are Wearing Listening Devices in the MR Scanner
Intense Scanner Acoustic Sound
Auditory-Friendly fMRI Acquisition Paradigms
Clinical Applications of Auditory fMRI
Plasticity Related to Hearing Loss as Measured by fMRI
Plasticity Related to Amplification as Measured by fMRI
Cochlear Implantation: Assessment of Candidature and Evaluation of Hearing Recovery
Conclusion
References
33: fMRI of Epilepsy
Introduction
Special Issues in Patients with Epilepsy
FMRI Applications in the Definition of Eloquent Cortical Areas in Epilepsy
Lateralization and Localization of Language
Memory Function
Sensorimotor Systems
Malformations of Cortical Development
Localization of the Epileptic Focus
Ictal fMRI
Interictal fMRI
Methodological Aspects of EEG-Correlated fMRI
EEG-Correlated fMRI of Interictal Activity
Conclusion
References
34: Functional MRI of Multiple Sclerosis
fMRI Studies of the Visual System
fMRI Studies of the Motor System
fMRI Studies of Cognition
fMRI of Fatigue, Depression, Pain, and Other MS Symptoms
Connectivity
Cervical Cord Activation
fMRI of Pharmacologic and Behavioral Interventions
Pediatric MS
Conclusion
References
35: Applications of fMRI to Psychiatry
Introduction
Rs-fMRI
Task-Based fMRI
Mood Disorders
Major Depressive Disorder (MDD)
Rs-fc Studies
Task-Based fMRI
Executive Control Tasks
Emotion Processing Tasks
Bipolar Disorder (BD)
Rs-fc Studies
Task-Based fMRI
Executive Function and Cognitive Tasks
Emotion Processing Tasks
Motor Tasks
Post-Traumatic Stress Disorder (PTSD)
Rs-fc Studies
Task-Based fMRI
Symptom Provocation Paradigms
Trauma-Unrelated Stimuli Paradigms
Substance Use Disorders
Rs-fc Studies
Task-Based fMRI
Cue-Related Reactivity
Cognitive Tasks
Emotion Processing Tasks
Applications of Machine Learning in fMRI
Conclusion
References
36: Applications of fMRI to Neurodegenerative Disease
Introduction
Aging Brain
Neurodegenerative Diseases
Functional MRI in Neurodegenerative Diseases
Dementias
Preclinical AD
MCI
AD
Functional MRI Studies of Memory
Functional MRI in Patients with MCI
Functional MRI in Patients with AD
BOLD Resting-State Connectivity in MCI and AD
Preclinical AD
MCI and AD
Functional MRI Studies in Individuals at Genetic Risk for AD
Functional MRI to Assess Pharmacologic Therapy for AD
Overall Picture
Frontotemporal Dementia
Functional MRI in Patients with FTD
Vascular Dementia
Functional MRI in Patients with Vascular Dementia
Normal Pressure Hydrocephalus
Functional MRI in Patients with Normal Pressure Hydrocephalus
Movement Disorders
Parkinson’s Disease
Motor fMRI in Patients with PD
Motor fMRI Studies of Bradykinesia and Tremor.
Motor fMRI Studies of Therapy for PD.
Motor fMRI Studies in Patients with Familial PD.
Motor fMRI Studies in Patients with Hemiparkinsonian Disease.
Cognitive fMRI in Patients with PD
Visual fMRI in Patients with PD
Olfaction fMRI in Patients with PD
Overall Picture
Huntington’s Disease
Functional MRI in Patients with Pre-HD
Functional MRI in Patients with HD
Amyotrophic Lateral Sclerosis
Resting-State fMRI in Patients with ALS
Functional MRI of Motor Paradigms in Patients with ALS
Functional MRI of Extramotor Paradigms in Patients with ALS
Multiple System Atrophy
Wilson’s Disease (Hepatolenticular Degeneration)
Conclusion
References
37: Applications of MRI to Psychopharmacology
Introduction
Background
Animal Research
Clinical Research
Conclusion
References
38: Functional MRI: Cognitive Neuroscience Applications
Introduction
Inference in Functional Neuroimaging Studies of Cognitive Processes
Functional MRI as a Cognitive Neuroscience Tool
Temporal Resolution
Spatial Resolution
Issues in Functional MRI Experimental Design
Issues in Interpretation of fMRI Data
Statistics
Altered Hemodynamic Response
Types of Hypotheses Tested Using fMRI
Functional Specialization
Functional Integration
Cognitive Theory
Integration of Multiple Methods
Combined fMRI/Lesion Studies
Combined fMRI/Transcranial Magnetic Stimulation Studies
Combined fMRI/Event-Related Potential Studies
Combined fMRI/Pharmacological Studies
Application of a Cognitive Neuroscience Approach to Clinical Studies
Use of Biomarkers Derived from Cognitive Neuroscience Studies
Conclusion
References
Part VII: Diffusion Tensor Imaging: Physical Principles
39: Diffusion Tensor Magnetic Resonance Imaging – Physical Principles
Introduction
Anisotropic Diffusion
Diffusion-Weighted Imaging
Implications of Anisotropic Diffusion for DWI
Diffusion Tensor Imaging
Common Artifacts
Number of Diffusion-Encoding Directions
Ratio of b = 0 Images to Diffusion-Weighted Images
Signal-to-Noise Ratio
Rendering DTI Data
Spherical, Oblate, Prolate Tensors, and DTI “Data Mining”
Trace
Fractional Anisotropy
Longitudinal and Transverse Diffusivity
Basics of Tractography
Crossroads
Diffusion Kurtosis
Time-Dependent Diffusion with Oscillating Gradients
Geometric Multicompartment Models
“Model-Free” Techniques
Parallel Imaging
In-Plane Parallel Imaging
Simultaneous Multislice EPI
Conclusion
References
40: Advanced Diffusion Imaging in Neuroradiology
Introduction
Diffusion Imaging and Diffusion Tensor Imaging
Model-Free Q-Space and Diffusion Spectrum Imaging
High Angular Resolution Diffusion Imaging
Neurite Orientation Dispersion and Density Imaging
Diffusion Kurtosis Imaging
BI-Tensor/Free Water Imaging
Hybrid Approaches
Conclusion
References
Part VIII: Diffusion Tensor Imaging: Clinical Applications
41: Diffusion Tractography in Neurosurgical Planning: Overview of Advanced Clinical Applications
Part I: Principles and Backgrounds of Diffusion Tensor Imaging and Diffusion Tensor Tractography
Part II: Clinical Applications of Tractography
Tractography in Deep Brain Stimulation
The Role of Tractography in Epilepsy Surgery
The Role of Tractography in Oncology
The Role of Tractography in Multiple Sclerosis
The Role of Tractography in Stroke
The Role of Tractography in Spinal Cord Surgery
Conclusion
References
42: Issues in Translating Imaging Technology and Presurgical Diffusion Tensor Imaging
Introduction
Disconnect Between Translational Research and Clinical Translation
Determinants of Imaging Technology Translation
Clinical Impact
Relative Invasiveness
Speed of Technology Development and Translation
Physician Comfort
Resource Impact
Conceptualizing Relationships Among Translational Determinants
Goals of Presurgical DTI
Limitations of Color-Coded Fractional Anisotropy
Importance of Integrating Presurgical DTI with Other Localization Parameters
Clinical Presentation
Functional Anatomy at Standard MRI
Presurgical Mapping Anatomy and Techniques
Intraoperative Brain Mapping and Intraoperative Functional Assessments
Summary
Protocols for Presurgical CC-DTI
The Impact of CC-FA DTI on Operative Decision-Making
Intraoperative Electrostimulation of White Matter (IES)
Intraoperative Functional Dissection Testing (FDT)
Summary
Quality Assurance and the Translation of DTI
Cataloguing Postoperative Neurological Outcomes
Impact of DTI
Guide to Clinical Fiber Tractography
Acquisition
Postprocessing
Integration with Surgical Navigation System
Limitations and Challenges of Fiber Tracking
Color-Coded Fractional Anisotropy Versus Fiber Tracking
Future Developments of DTI and Presurgical Fiber Tracking
Conclusion
References
43: Clinical Applications of Diffusion MRI in Epilepsy
Introduction
Fundamental Concepts in Diffusion MRI
Peri-ictal Diffusion MRI of Seizures
Temporal Lobe Epilepsy
Hippocampal Pathology
White Matter Pathology
Surgical Planning
Malformations of Cortical Development
Conclusion: Future Directions
References
Part IX: Clinical Presurgical Brain Tumor Mapping
44: fMRI and DTI: Review of Complementary Techniques
Introduction
BOLD fMRI
Clinical fMRI
Paradigms in Clinical fMRI
Sensorimotor Paradigms
Language Paradigms
Expressive Language Paradigms
Receptive Language Paradigms
Semantic Paradigms
Visual Paradigms
Memory Paradigms
Diffusion Tensor Imaging
Diffusion-Weighted Imaging
Diffusion Tensor
Fiber Tractography
Clinical Impact of BOLD fMRI and DTI
Case Reports Highlighting the Value of Preoperative Combined BOLD fMRI and DTI for Presurgical Planning
Case 44.1
History
Imaging Findings
Impact on Surgical Management/Postoperative Outcome
Histopathology
Case 44.2
History
Imaging Findings
Impact on Surgical Management/Postoperative Outcome
Histopathology
Case 44.3
History
Imaging Findings
Impact on Surgical Management/Postoperative Outcome
Histopathology
Conclusion
References
45: DTI: Functional Anatomy of Key Tracts
Introduction
Association Tracts (Figs. 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 45.10, 45.11, 45.12, 45.13, 45.14, 45.15, 45.16, 45.17, and 45.18)
Commissural Tracts
Projection Tracts
Association Tracts
Superior Longitudinal Fasciculus
SLF I
SLF II
SLF III
SLF IV (Arcuate Fasciculus Complex)
SLF V (SLF TP-SPL)
Inferior Fronto-Occipital Fasciculus
Inferior Longitudinal Fasciculus
Superior Fronto-Occipital Fasciculus
Middle Longitudinal Fasciculus (MdLF)
Extreme Capsule
Frontal Aslant Tract
Cingulate Fasciculus or Cingulum Bundle
Fornix and Stria Terminalis
Uncinate Fasciculus
Commissural Tracts
Corpus Callosum
Anterior Commissure
Miscellaneous Commissural Tracts
Visual Pathway
DTI: Clinical Cases (Figs. 45.39, 45.40, 45.41, 45.42, 45.43, and 45.44)
References
46: Pediatric Applications of fMRI
Introduction
Uniqueness of FMRI in Children
FMRI Technical Challenges in Children
Study Design
The Three Paradigm Types
Passive Paradigms Under Sedation
Sedation of Choice
Motion
Motion Correction (Image Postprocessing)
Spatial Normalization
Statistical Analysis
Thresholding
Applications
Cortical Functional Mapping for Surgical Planning
Language Mapping
Noncooperative Patients
Language Mapping of Partially Cooperative Children
Language Mapping of Fully Cooperative Children
Motor Mapping
Motor Mapping in Noncooperative Patients
Motor Mapping in Patients with Limited Cooperation
Motor Mapping of Cooperative Patients
Sensory Mapping
Visual Mapping
Noncooperative Patients
Visual Mapping in Partial Cooperative and Full Cooperative Patients
Auditory Mapping
Auditory Mapping in Noncooperative Patients
Auditory Mapping in Cooperative Patients
Memory Mapping
Complex Cognitive Functions
FMRI in Specific Pediatric Conditions
Development
Epilepsy
Ictal fMRI
Interictal fMRI
The Mean-BOLD
Autism
Attention-Deficit Hyperactivity Disorder
Dyslexia
Brain Plasticity
Other fMRI Studies in Specific Pediatric Conditions
Pharmacology-fMRI (ph-fMRI) in Children
Other Significant Findings in Task-Related fMRI on Children
rs-fMRI in Children
The Default Mode
Clinical Application
Conclusion
References
Part X: Magnetoenphalopathy and PET Imaging
47: Magnetoencephalography: Epilepsy and Brain Mapping
Introduction
Clinical Utility
The MEG System
The Clinical MEG Examination
Recording of Spontaneous and Elicited Activity
Evoked Recordings and Mapping
Somatosensory
Motor Mapping
Visual Stimulation
Language Function
Conclusion
References
48: PET-CT/MR Imaging in Head and Neck Cancer: Physiologic Variations, Pitfalls, and Directed Applications
Introduction
Pertinent Head and Neck Anatomy
Evaluating Head and Neck Cancer
Limitations of Computed Tomography and Magnetic Resonance Imaging
Limitations of FDG-PET in Evaluating Head and Neck Cancer
Summary: PET-CT in the Evaluation of Head and Neck Cancer
Conclusion
References
49: Simultaneous PET and MR Imaging of the Human Brain
Introduction
Integrating PET AND MRI: Hardware Perspective
Challenges in Combining PET and MRI
Approaches to Combining PET and MRI
MR-Compatible Dedicated Brain PET Inserts
Whole-Body PET/MRI Scanners
Technologies Enabling New Science
How Can Simultaneously Acquired MRI Improve PET?
Attenuation Correction
Motion Correction
Partial Volume Correction
Image-Based Arterial Input Function Estimation
Deep Learning-Enabled PET Data Enhancement
How Can Simultaneously Acquired PET Improve MRI?
PET-Guided MR Imaging
Artifact-Free MR Image Generation from Undersampled Data
In-Vivo Quantification of “Smart” MR Probes
Techniques for Cross-Calibration and Validation
Promising Applications
Basic Neuroscience
Neuroreceptor Pharmacology and Modulation
Brain Function, Behavior, and Cognition
Functional Networks and Connectivity
Psychiatry
Schizophrenia
Autism Spectrum Disorder
Depression
Alzheimer’s Disease and Dementia
Other Neurological Applications
Chronic Pain
Brain Tumors
Ischemic Stroke
Conclusion
References
Part XI: Emerging Neuroimaging Techniques
50: Functional Imaging in Autism Spectrum Disorder
Introduction
Autism Spectrum Disorder
Magnetoencephalography
MEG Investigations of Auditory Processing in ASD
M50 and M100 Components
Mismatch Field Component
Auditory Gamma-Band Activity
Theta, Alpha, and Beta-Band Activity
MEG Investigations of Resting State in ASD
Clinical Implications
Conclusion
References
51: The Role of Molecular Imaging in Personalized Medicine
Introduction
Linking Molecular Imaging, Cancer Biology, and Personalized Medicine
The Application of a Systems Biology Approach to Cancer
Cancer Biomarkers for Clinical Use
Nanoparticles for Cancer MR Diagnostics and Therapeutics
Magnetic Nanoparticles
Quantum Dots
Nanoshells
The Role of Radiology in Personalized Medicine
Clinical and Near-Clinical Molecular Imaging Applications Using Targeted Probes
Oncologic Molecular Imaging for Detection of Nodal Metastases and Cancer Staging
Molecular Imaging of Cell-Based Therapies
Molecular Imaging of Dendritic Cell Migration to Regional Nodes for Tumor Immunotherapy
Molecular Imaging of Gene Expression and Therapy
Molecular Imaging of Thrombosis in Atherosclerotic Disease
Conclusion
References
52: Quantitative Metabolic Magnetic Resonance Imaging: A Case for Bioscales in Medicine
Introduction
Approaches to Alzheimer’s Disease and Other Neurodegenerative Disease
Bioscales for Monitoring Disease Progression
Quantitative Sodium Imaging and Cell Volume Fraction Bioscale
Clinical Applications of Sodium Bioscales
Subacute Stroke Management
Tumor Response to Radiation and Chemotherapy
Case 52.1
Case 52.2
Other Uses of Metabolic Imaging Bioscales
Conclusion
References
53: Magnetic Resonance Fingerprinting
Introduction
Overview of Technique
Signal Acquisition
Dictionary Generation
Pattern Matching
Reconstruction and Output
Technical Advances
Extended Coverage
Partial Volume Effect
Other Technical Advances
Validation, Repeatability, and Reproducibility
Clinical Applications
Age-Related Physiological Changes in the Brain and Neurodegenerative Diseases
Brain Tumor Imaging
Epilepsy Imaging
Radiation Therapy Planning
Other Applications
Bridging the Translational Gap
Conclusion
References
54: Neuroimaging of Brain Tumors in the Era of Radiogenomics
Introduction
Artificial Intelligence: Machine Learning and Deep Learning in a Nutshell
Standardization of Brain Tumor Imaging
Radiogenomics of Brain Tumors
Adult Diffuse Gliomas
Isocitrate Dehydrogenase (IDH) Mutation
1p/19q Chromosomal Codeletion
ATRX and TP53 Mutation
MGMT Methylation Status
Pediatric Diffuse Gliomas
Diffuse Midline and Hemispheric Pediatric High-Grade Gliomas
Ependymomas
Embryonal Tumors–Medulloblastomas (MB) and Others
Medulloblastoma
Atypical Teratoid/Rhabdoid Tumor (AT/RT)
Embryonal Tumor with Multilayered Rosettes (ETMR)
Conclusion
References
55: Radiomics and Radiogenomics in Glioma
Introduction
Relationship Between Genotypes and Radiomics in Glioma
Isocitrate Dehydrogenase 1 and 2
1p/19q Deletion
ATRX
O6-Methylguanine-DNA-Methyltransferase (MGMT)
EGFR
TP53
Ki-67 Expression
Conclusion and Future Perspective
References
56: Quantitative T1ρ MR Imaging in Neuroradiology
Background on T1ρ Magnetic Resonance Imaging
T1ρ Magnetic Resonance Imaging and Applications to Brain Magnetic Resonance Imaging
Normative Data
Neurodegeneration
Multiple Sclerosis
Acute Ischemic Infarct
Intracranial Neoplasm
T1ρ Magnetic Resonance Imaging and Applications to Spine Magnetic Resonance Imaging
Normative Data
Conclusion
References
57: Neurological Applications of Magnetic Resonance-Guided Focused Ultrasound Therapy
A Brief History
Sequence of Breakthroughs
Current Ablative Applications in Movement Disorders
Essential Tremor
Parkinson’s Disease
Current Applications in Blood–Brain Barrier Disruption
Brain Tumors
Alzheimer’s Disease
Future Applications
Conclusion
References
58: CNS Machine Learning
Introduction
Basic Definitions
Machine Learning: Some Basic Terms
Common Machine Learning Algorithms
Some Common Machine Learning Algorithms
Regression Algorithms
Classification Algorithms
Clustering Algorithms
Dimensionality Reduction Algorithms
Ensemble Algorithms
Deep Learning
Training and Optimizing
Overfitting and Data Augmentation
Common Deep Learning Algorithms
Autoencoder
Convolutional Neural Network (CNN)
UNET
U-Nets
Generative Adversarial Network (GAN)
Model Design and Assessment
Data Preparation and Augmentation
Applications in Neuroradiology
Detection
Critical Findings on Emergent CT
Screening for Aneurysm
Classification
Classify Different Tumors and Subtypes
Classify Different Types of Dementia
Segmentation
Prediction
Prediction in Acute Ischemic Stroke
Predict Aneurysm Rupture Risk and Outcome
Predict Conversion of MCI to AD
Improving Image Quality
Image Improvement and Synthesis
Dose Reduction and Virtual Contrast Enhancement
Reconstruction from Subsampled Diffusion-Weighted Imaging
Improve Image Quality in Quantitative Susceptibility Mapping (QSM)
Reduce Acquisition Time: Magnetic Resonance Fingerprinting
Challenges Ahead
Summary
References
59: Anatomical and Functional Features of the Central Nervous System Lymphatic System
Anatomy and Function of the Peripheral Lymphatic System
Unmet Needs: Chronic Neurodegenerative Disorders
Overview of the Human Glymphatic System
Traditional Model of CSF Flow and Clearance
Expansion of CSF Flow and Clearance in the Glymphatic Pathway
Potential Relevance of Glymphatic Dysfunction in Disease
Sleep
Normal Pressure Hydrocephalus
Cerebrovascular Disease
Multiple Sclerosis
Alzheimer’s Disease
Conclusion
References
60: TBI Sports Related Injury
Introduction
Blood-Oxygen-Level-Dependent (BOLD) Functional MRI
Review of Resting-State fMRI Literature
Review of Task-Based fMRI Studies
Limitations and Considerations
Arterial Spin Labeling (ASL)
ASL Utilized in Patients During the Acute and Subacute Timeframes (

Citation preview

Scott H. Faro Feroze B. Mohamed Editors

Functional Neuroradiology Principles and Clinical Applications Second Edition

123

Functional Neuroradiology

Scott H. Faro  •  Feroze B. Mohamed Editors

Functional Neuroradiology Principles and Clinical Applications Second Edition

Editors Scott H. Faro Division of Neuroradiology Thomas Jefferson University Philadelphia, PA, USA

Feroze B. Mohamed Department of Radiology Thomas Jefferson University Philadelphia, PA, USA

ISBN 978-3-031-10908-9    ISBN 978-3-031-10909-6 (eBook) https://doi.org/10.1007/978-3-031-10909-6 © Springer Nature Switzerland AG 2012, 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

A decade has passed since the publication of the first edition of this textbook. During the last several years, there have been several exciting advances in the field of functional neuroradiology and its translation into clinical applications. The relatively new leading neuroradiological medical society, the American Society of Functional Neuroradiology, has also seen tremendous growth during this period, including a new generation of enthusiastic physicians and MR physicists working together with the goal of further advancing the role of neuroimaging in clinical decision making. The field of functional neuroradiology is bright. This second edition expands upon the core topics covered in the first edition etc. The new topics include: CNS Tumor Surveillance and Functional MR Perfusion Imaging Quantitative and Physiological Magnetic Resonance Imaging in Glioma Chemical Exchange Saturation Transfer (CEST) Imaging Functional Neuroradiology of Multiple Sclerosis: Non-BOLD Techniques Functional Connectivity MR Imaging Resting State Functional Magnetic Resonance Imaging Advanced Diffusion Imaging in Neuroradiology Diffusion Tractography in Neurosurgical Planning: Overview of Advanced Clinical Applications Magnetic Resonance Fingerprinting Radiomics and Radiogenomics in Glioma Neuroimaging of Brain Tumors in the Era of Radiogenomics Functional Imaging in Autism Spectrum Disorder TBI: Sports-Related Injury Neurological Applications of Magnetic Resonance-Guided Focused Ultrasound Therapy Anatomical and Functional Features of the Central Nervous System Lymphatic System Hydrocephalus Imaging Advanced Neuroimaging for Spine Metastasis CNS Machine Learning It is again our hope that this second edition will be a major reference for physicians from various disciplines, MR physicists, and cognitive neuroscientists in this important growing field of functional neuroradiology. Philadelphia, PA, USA Philadelphia, PA, USA 

Scott H. Faro Feroze B. Mohamed

v

Acknowledgments

Our second edition, two volume text, includes updates to the chapters from the first edition as well as 18 new chapters on advanced functional neuroradiology. I would like to wholeheartedly thank all of our 100+ authors who have dedicated all of their precious time to contribute to our book. These are challenging times for many reasons; however all of these dedicated colleagues continue to pursue their passion for research and education that will guide and motivate the next generation of physicians and scientists. Again, I want to acknowledge my family and all of my colleagues at Jefferson who give me the time and encouragement to pursue my academic interests. This importantly includes my friend and colleague Dr. Feroze Mohamed as we near our 30 years of collaboration with no end in sight. Last, I would like to thank Springer Publishing and specifically Maureen Pierce, Margaret Moore, and Arulronika Pathinathan at Springer for their tremendous help in the preparation of this textbook. Scott H. Faro

vii

Contents

Part I Diffusion and Perfusion Imaging: Physical Principles 1 Physical  Principles of Diffusion Imaging���������������������������������������������������������������    3 Thinesh Sivapatham and Elias R. Melhem 2 Physical  Principles of Dynamic Contrast-­Enhanced and Dynamic Susceptibility Contrast MRI �����������������������������������������������������������   15 Mark S. Shiroishi, Jerrold L. Boxerman, C. Chad Quarles, Daniel S. R. Stahl, Saulo Lacerda, Naira Muradyan, Timothy P. L. Roberts, and Meng Law 3 Physical  Principles of Non-gadolinium Perfusion Technique (Arterial Spin Labeling)�������������������������������������������������������������������������������������������   35 Youngkyoo Jung, Huan Tan, and Jonathan H. Burdette Part II Diffusion and Perfusion Imaging: Clinical Applications 4 Clinical Applications of Diffusion���������������������������������������������������������������������������   49 Juan Márquez, Thiparom Sananmuang, Ashok Srinivasan, Pamela W. Schaefer, and Reza Forghani 5 Clinical  Applications of MR Perfusion Imaging ���������������������������������������������������  119 Seyed Ali Nabavizadeh and Ronald L. Wolf 6 Clinical  Application of Perfusion and Diffusion in Stroke �����������������������������������  161 Tanvir Rizvi and Max Wintermark 7 Clinical  Applications of Dynamic Contrast-Enhanced (DCE) Permeability Imaging�����������������������������������������������������������������������������������������������  175 Saulo Lacerda, Giuseppe Barisano, Mark S. Shiroishi, and Meng Law 8 CNS  Tumor Surveillance and Functional MR Perfusion Imaging�����������������������  201 Amit Desai and Rajan Jain Part III Magnetic Resonance Spectroscopy and Chemical Exchange Saturation Transfer Imaging 9 Magnetic  Resonance Spectroscopy: Physical Principles���������������������������������������  223 Stefan Blüml 10 Magnetic  Resonance Spectroscopy: Clinical Applications�����������������������������������  241 Alena Horská, Adam Berrington, Peter B. Barker, and Ivan Tkáč 11 Chemical  Exchange Saturation Transfer (CEST) Imaging ���������������������������������  293 Daniel Paech and Lisa Loi

ix

x

Part IV Multimodal Functional Neuroradiology 12 Functional  Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses�������������������������������������������������������������������������������������������������������������  311 Arastoo Vossough and Seyed Ali Nabavizadeh 13 Functional  Neuroimaging of Epilepsy���������������������������������������������������������������������  345 Noriko Salamon 14 Functional  Neuroradiology of Traumatic Brain Injury ���������������������������������������  355 Giacomo Boffa, Eytan Raz, and Matilde Inglese 15 Functional  Neuroradiology of Multiple Sclerosis: Non-BOLD Techniques���������������������������������������������������������������������������������������������  373 Francesca Benedetta Pizzini and Giacomo Talenti 16 Functional  Neuroradiology of Psychiatric Diseases�����������������������������������������������  393 Paolo Nucifora 17 Neuroimaging of Pain�����������������������������������������������������������������������������������������������  407 Richard H. Gracely and Pia C. Sundgren 18 Quantitative  and Physiological Magnetic Resonance Imaging in Glioma ���������������������������������������������������������������������������������������������������  433 Shah Islam, Melanie A. Morrison, and Adam D. Waldman Part V BOLD Functional MRI: Physical Principles 19 Principles  of BOLD Functional MRI ���������������������������������������������������������������������  461 Seong-Gi Kim and Peter A. Bandettini 20 fMRI Scanning Methods �����������������������������������������������������������������������������������������  473 Chris J. Conklin and Devon M. Middleton 21 Experimental  Design and Data Analysis for fMRI �����������������������������������������������  485 Geoffrey K. Aguirre 22 Challenges  in fMRI and Its Limitations�����������������������������������������������������������������  497 R. Todd Constable 23 Neurovascular  Uncoupling in Functional MRI�����������������������������������������������������  511 Jorn Fierstra and David J. Mikulis 24 Functional  Connectivity MR Imaging �������������������������������������������������������������������  521 Corey Horien, Xilin Shen, Dustin Scheinost, R. Todd Constable, and Michelle Hampson 25 Clinical  Challenges of Functional MRI������������������������������������������������������������������  543 Nader Pouratian, Bayard Wilson, and Susan Y. Bookheimer Part VI BOLD Functional MRI: Clinical & Cognitive Applications 26 fMRI  of Language Systems: Methods and Applications���������������������������������������  565 Jeffrey R. Binder 27 Functional  MRI of Language and Memory in Surgical Epilepsy: fMRI Wada Test �������������������������������������������������������������������������������������������������������  593 Brenna C. McDonald, Rupa Radhakrishnan, and Kathleen M. Kingery

Contents

Contents

xi

28 Resting  State Functional Magnetic Resonance Imaging���������������������������������������  623 Daniel Ryan, Sachin K. Gujar, and Haris I. Sair 29 fMRI  of Human Visual Pathways���������������������������������������������������������������������������  641 Edgar A. DeYoe, John L. Ulmer, Wade M. Mueller, Lotfi Hacein-Bey, Viktor Szeder, Mary Jo Maciejewski, Karen Medler, Danielle Reitsma, and Jedediah Mathis 30 Functional  MRI Studies of Memory in Aging, Mild Cognitive Impairment, and Alzheimer’s Disease �������������������������������������������������������������������  671 Jian Zhu, Shannon L. Risacher, Heather A. Wishart, and Andrew J. Saykin 31 Brain  Mapping for Cognitive Neuroscience and Neurosurgery���������������������������  713 Joy Hirsch 32 fMRI  of the Central Auditory System���������������������������������������������������������������������  745 Deborah Ann Hall and Thomas M. Talavage 33 fMRI of Epilepsy�������������������������������������������������������������������������������������������������������  765 Karsten Krakow 34 Functional  MRI of Multiple Sclerosis���������������������������������������������������������������������  781 Heather A. Wishart 35 Applications  of fMRI to Psychiatry�������������������������������������������������������������������������  799 Chandni Sheth, Erin C. McGlade, and Deborah Yurgelun-Todd 36 Applications  of fMRI to Neurodegenerative Disease���������������������������������������������  819 Shamseldeen Y. Mahmoud, Moon Doksu, Jonathan K. Lee, and Stephen E. Jones 37 Applications  of MRI to Psychopharmacology�������������������������������������������������������  861 Dan J. Stein, Yihong Yang, and Betty Jo Salmeron 38 Functional  MRI: Cognitive Neuroscience Applications ���������������������������������������  877 Andrew S. Kayser, Anthony J. W. Chen, and Mark D’Esposito Part VII Diffusion Tensor Imaging: Physical Principles 39 Diffusion  Tensor Magnetic Resonance Imaging – Physical Principles����������������  903 Jose Guerrero, Thomas A. Gallagher, Andrew L. Alexander, and Aaron S. Field 40 Advanced  Diffusion Imaging in Neuroradiology���������������������������������������������������  933 Devon M. Middleton and Chris J. Conklin Part VIII Diffusion Tensor Imaging: Clinical Applications 41 Diffusion  Tractography in Neurosurgical Planning: Overview of Advanced Clinical Applications���������������������������������������������������������������������������������������������������������������  951 Jingya Miao, Solomon Feuerwerker, Karim Hafazalla, Lauren Janczewski, Michael P. Baldassari, Steven Lange, Arichena Manmatharayan, Jennifer Muller, Michael Kogan, Caio M. Matias, Nikolaos Mouchtouris, Daniel Franco, Joshua E. Heller, James S. Harrop, Ashwini Sharan, and Mahdi Alizadeh

xii

42 Issues  in Translating Imaging Technology and Presurgical Diffusion Tensor Imaging�����������������������������������������������������������������������������������������  969 John L. Ulmer, Jeffrey I. Berman, Wade M. Mueller, Wolfgang Gaggl, Edgar A. DeYoe, and Andrew P. Klein 43 Clinical  Applications of Diffusion MRI in Epilepsy����������������������������������������������� 1003 Joanne M. Rispoli, Christopher P. Hess, and Timothy M. Shepherd Part IX Clinical Presurgical Brain Tumor Mapping 44 fMRI  and DTI: Review of Complementary Techniques��������������������������������������� 1025 Shruti Agarwal, Hussain Al Khalifah, Domenico Zaca, and Jay J. Pillai 45 DTI:  Functional Anatomy of Key Tracts ��������������������������������������������������������������� 1061 Arash Kamali, Vinodh A. Kumar, Khader M. Hasan, Mohit Maheshwari, Andrew P. Klein, Kiran Shankar Talekar, John L. Ulmer, and Scott H. Faro 46 Pediatric Applications of fMRI ������������������������������������������������������������������������������� 1085 Byron Bernal and Nolan R. Altman Part X Magnetoenphalopathy and PET Imaging 47 Magnetoencephalography:  Epilepsy and Brain Mapping ����������������������������������� 1123 Erin Simon Schwartz and Timothy P. L. Roberts 48 PET-CT/MR  Imaging in Head and Neck Cancer: Physiologic Variations, Pitfalls, and Directed Applications������������������������������������������������������� 1137 Laurie A. Loevner 49 Simultaneous  PET and MR Imaging of the Human Brain����������������������������������� 1165 Ciprian Catana, Christin Sander, A. Gregory Sorensen, and Bruce R. Rosen Part XI Emerging Neuroimaging Techniques 50 Functional  Imaging in Autism Spectrum Disorder����������������������������������������������� 1205 Junko Matsuzaki, Heather Green, and Timothy P. L. Roberts 51 The  Role of Molecular Imaging in Personalized Medicine����������������������������������� 1223 Michelle Bradbury 52 Quantitative  Metabolic Magnetic Resonance Imaging: A Case for Bioscales in Medicine����������������������������������������������������������������������������� 1239 Keith R. Thulborn 53 Magnetic Resonance Fingerprinting����������������������������������������������������������������������� 1259 Chaitra Badve and Dan Ma 54 Neuroimaging  of Brain Tumors in the Era of Radiogenomics����������������������������� 1275 Prem P. Batchala, Thomas J. Eluvathingal Muttikkal, Joseph H. Donahue, M. Beatriz Lopes, Eli S. Williams, Nicholas J. Tustison, and Sohil H. Patel 55 Radiomics  and Radiogenomics in Glioma ������������������������������������������������������������� 1313 Murat Ak and Rivka R. Colen 56 Quantitative T1ρ MR Imaging in Neuroradiology ����������������������������������������������� 1323 Christopher G. Filippi, Alexander Klebba, Scott Hipko, and Richard Watts

Contents

Contents

xiii

57 Neurological  Applications of Magnetic Resonance-Guided Focused Ultrasound Therapy����������������������������������������������������������������������������������� 1337 Ahmed Abdul-Kareem, Dheeraj Gandhi, Timothy R. Miller, Rao Gullapalli, and Elias R. Melhem 58 CNS Machine Learning������������������������������������������������������������������������������������������� 1347 Elizabeth Tong, Endre Grøvik, Kyrre Eeg Emblem, Kevin Chen, Audrey Fan, Yannan Yu, Guangming Zhu, Moss Zhao, Sanaz Niri, and Greg Zaharchuk 59 Anatomical  and Functional Features of the Central Nervous System Lymphatic System��������������������������������������������������������������������������������������� 1377 Manus Joseph Donahue, Paula M. C. Donahue, Rachelle Crescenzi, and Colin D. McKnight 60 TBI  Sports Related Injury��������������������������������������������������������������������������������������� 1389 Mohammad I. Kawas, Christopher A. Sheridan, William C. Flood, Adam P. Sweeney, and Christopher T. Whitlow Part XII Functional Spine and Hydrocephalus Imaging 61 Functional  MRI of the Spinal Cord: Diffusion Weighted, Diffusion Tensor Imaging, and Fiber Tractography��������������������������������������������������������������� 1403 Kiran Shankar Talekar, Meng Law, Majda M. Thurnher, Eric D. Schwartz, and Adam E. Flanders 62 Advanced  Neuroimaging for Spine Metastasis������������������������������������������������������ 1425 Varun Sethi, Kristin J. Redmond, and Majid Khan 63 Hydrocephalus Imaging������������������������������������������������������������������������������������������� 1439 Ari M. Blitz, Ameya P. Nayate, Anthony Higginbotham, Daniele Rigamonti, and Harold L. Rekate Part XIII Neuroanatomical Brain Atlas 64 Neuroanatomical  Atlas of Key Presurgical and Cognitive Eloquent Cortex Regions ��������������������������������������������������������������������������������������������������������� 1457 Feroze B. Mohamed, Michael P. Yannes, Muhammed Malik, and Scott H. Faro 65 Normal  Anatomic Atlas of Common White Matter Tracts Using DTI������������������������������������������������������������������������������������������������������� 1477 Andrew P. Klein 66 fMRI Paradigms������������������������������������������������������������������������������������������������������� 1491 Kiran Shankar Talekar, Feroze B. Mohamed, and Scott H. Faro Index����������������������������������������������������������������������������������������������������������������������������������� 1501

Contributors

Shruti  Agarwal, PhD Division of Neuroradiology, Russell H.  Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA Geoffrey  K.  Aguirre, MD, PhD  Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA Ahmed  Abdul-Kareem, SM, MD Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA Murat Ak, MD  Department of Radiology, UPMC Hillman Cancer Center, Pittsburgh, PA, USA Andrew L. Alexander, PhD  Departments of Medical Physics and Psychiatry, University of Wisconsin-Madison, Madison, WI, USA Mahdi Alizadeh, PhD  Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Hussain Al Khalifah, MD  Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA Nolan R. Altman, MD  Department of Radiology, Nicklaus Children’s Hospital, Miami, FL, USA Chaitra  Badve, MBBS, MD Department of Radiology, University Hospitals Cleveland Medical Center, Case School of Medicine, Cleveland, OH, USA Michael  P.  Baldassari, BA  Department of Neurological Surgery, Sidney Kimmel Medical School of Thomas Jefferson University, Philadelphia, PA, USA Peter  A.  Bandettini, PhD Section on Functional Imaging Methods, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA Giuseppe  Barisano, MD Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA Peter  B.  Barker, DPhil Russell H.  Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA Prem  P.  Batchala, MD Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA Jeffrey  I.  Berman, PhD Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

xv

xvi

Byron Bernal, MD, PPI  Brain Institute—Radiology Department, Nicklaus Children’s Health System, Miami, FL, USA Adam Berrington, MSCi, DPhil  Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK Jeffrey  R.  Binder, MD Neurology and Biophysics, Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA Ari  M.  Blitz, MD Division of Neuroradiology, Department of Radiology, University Hospitals, Case Western Reserve University School of Medicine, Cleveland, OH, USA Cleveland Medical Center, Cleveland, OH, USA Stefan Blüml, PhD  Department of Radiology and Viterbi School of Engineering, Children’s Hospital Los Angeles/University of Southern California (USC), Los Angeles, CA, USA Giacomo Boffa, MD  Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Genoa, Italy Susan Y. Bookheimer, PhD  Department of Psychiatry and Behavioral Sciences, Center for Autism Research and Treatment, University of California, Los Angeles, Los Angeles, CA, USA Jerrold L. Boxerman, MD, PhD, FACR  Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA Michelle Bradbury, MD, PhD  MSK-Cornell Center for Translation of Cancer Nanomedicines, Intraoperative Imaging Program, Department of Radiology and Molecular Pharmacology Program, Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center & Weill Medical College of Cornell University, New York, NY, USA Jonathan  H.  Burdette, MD  Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA Ciprian  Catana, MD, PhD Department of Radiology, Athinoula A.  Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA Anthony J. W. Chen, MD  Division of Neurology, Department of Veterans Affairs, Northern California Health Care System, Martinez, CA, USA Kevin Chen, PhD  Department of Radiology, Stanford University, Stanford, CA, USA Rivka R. Colen, MD  Department of Radiology, UPMC Hillman Cancer Center, Pittsburgh, PA, USA Chris J. Conklin, PhD  Neuroscience Medical Affairs, Bioclinica, Princeton, NJ, USA R.  Todd  Constable, PhD Department of Diagnostic Radiology and Biomedical Imaging, Biomedical Engineering, and Neurosurgery, Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA MRI Research, Yale MRRC, Yale University School of Medicine, New Haven, CT, USA Interdepartmental Neuroscience Program, Department of Diagnostic Radiology and Biomedical Imaging, Biomedical Engineering, and Neurosurgery, Yale University School of Medicine, New Haven, CT, USA Rachelle  Crescenzi, PhD  Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

Contributors

Contributors

xvii

Mark  D’Esposito, MD Division of Neurology, Department of Veterans Affairs, Northern California Health Care System, Martinez, CA, USA Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA Amit Desai, MD  Department of Radiology, Mayo Clinic, Jacksonville, FL, USA Edgar  A.  DeYoe, PhD  Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA Moon Doksu, MD  Imaging Institute, Cleveland Clinic, Cleveland, OH, USA Joseph  H.  Donahue, MD Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA Manus  Joseph  Donahue, PhD Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA Paula M. C. Donahue, DPT  Department of Physical Medicine and Rehabilitation, Vanderbilt University Medical Center, Nashville, TN, USA Thomas J. Eluvathingal Muttikkal, MD  Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA Kyrre  Eeg  Emblem, PhD Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway Audrey Fan, PhD  Department of Biomedical Engineering, University of California Davis, Davis, CA, USA Department of Neurology, University of California Davis, Davis, CA, USA Scott  H.  Faro, MD, FASFNR Departments of Radiology and Neurology, Division of Neuroradiology and ENT, Thomas Jefferson University Hospital, Philadelphia, PA, USA Solomon Feuerwerker, MD  Department of Surgery, University of Vermont Medical Center, Burlington, VT, USA Aaron  S.  Field, MD, PhD Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA Jorn  Fierstra, MD, PhD Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland Christopher  G.  Filippi, MD Department of Radiology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA Adam  E.  Flanders, MD Department of Radiology/Neuroradiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA William C. Flood  Department of Neuroscience, Wake Forest School of Medicine, Winston-­ Salem, NC, USA Reza  Forghani, MD, PhD Augmented Intelligence and Precision Health Laboratory (AIPHL), McGill University Health Center, Montreal, QC, Canada Combined Research and Clinical Fellowship in Head and Neck Imaging, McGill University Health Center, Montreal, QC, Canada

xviii

Daniel  Franco, MD Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Wolfgang Gaggl, PhD  GE Healthcare, Waukesha, WI, USA Thomas  A.  Gallagher, MD  Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA Dheeraj  Gandhi, MD Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA Department of Diagnostic Radiology & Nuclear Medicine, Neuroradiology, University of Maryland School of Medicine, Baltimore, MD, USA Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA Richard  H.  Gracely, PhD Center for Pain Research and Innovation, Department of Endodontics, School of Dentistry, School of Medicine, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA Heather  Green, PhD  Department of Radiology, Lurie Family Foundations MEG Imaging Center, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Endre  Grøvik, PhD Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway Faculty of Health Sciences, University of South-Eastern Norway, Drammen, Norway Jose  Guerrero, PhD Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA Sachin  K.  Gujar, MBBS, MD  Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD, USA Rao  Gullapalli, PhD, MBA Department of Diagnostic Radiology & Nuclear Medicine, Neuroradiology, University of Maryland School of Medicine, Baltimore, MD, USA Lotfi  Hacein-Bey, MD Interventional Radiology, Sutter Health Research Enterprise, Sacramento, CA, USA Karim  Hafazalla, MD  Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Deborah  Ann  Hall, BSc, PhD Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK Michelle Hampson, PhD  Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA James S. Harrop, MD  Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Khader M. Hasan  Department of Radiology, University of Texas Houston Medical School, Houston, TX, USA Joshua  E.  Heller, MD, MBA Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Christopher  P.  Hess, MD, PhD Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA Anthony  Higginbotham Department of Radiology, University Hospitals, Case Western Reserve University School of Medicine, Cleveland, OH, USA

Contributors

Contributors

xix

Scott Hipko, BSc  Department of Radiology, Robert Larner School of Medicine University of Vermont, Burlington, VT, USA Joy  Hirsch, PhD Psychiatry, Comparative Medicine, and Neuroscience, Brain Function Laboratory, Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA Haskins Laboratories, New Haven, CT, USA Neuroscience, Department of Medical Physics and Biomedical Engineering, Faculty of Engineering Sciences ex officio, University College London, London, UK Corey  Horien, BA, M.Phil Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA Alena Horská, PhD  Division of Construction and Instruments, National Institutes of Health, Office of Research Infrastructure Programs, Bethesda, MD, USA Matilde  Inglese, MD, PhD Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, San Martino Polyclinic Hospital, Genoa, Italy Shah  Islam, MBBS, BSc, FRCR Department of Surgery and Cancer, Imperial College London, London, UK Rajan  Jain, MD Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA Department of Neurosurgery, New York University Grossman School of Medicine, New York, NY, USA Lauren Janczewski, MD  Department of General Surgery, Northwestern Memorial Hospital, Chicago, IL, USA Stephen  E.  Jones, MD, PhD Division of Neuroradiology, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA Youngkyoo  Jung, PhD Department of Radiology, University of California, Davis, Sacramento, CA, USA Arash  Kamali, MD Department of Radiology, University of Texas, Houston, Memorial Hermann Hospital at Houston Medical Center, Houston, TX, USA Mohammad I. Kawas, MD  Department of Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA Andrew  S.  Kayser, MD, PhD  Department of Neurology, University of California at San Francisco, San Francisco, CA, USA Division of Neurology, Department of Veterans Affairs, Northern California Health Care System, Martinez, CA, USA Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, CA, USA Majid Khan, MD  Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA Seong-Gi Kim, PhD  Center for Neuroscience Imaging Research/Department of Biomedical Engineering, Institute for Basic Science/Sungkyunkwan University, Suwon, Korea Kathleen  M.  Kingery, PhD Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA Alexander  Klebba, MD  Department of Radiology, Donald and Barbara Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA

xx

Department of Radiology, Lenox Hill Hospital, New York, NY, USA Andrew  P.  Klein, MD Department of Radiology, Neuroradiology, Medical College of Wisconsin, Froedtert Hospital, Milwaukee, WI, USA Michael  Kogan, MD, PhD Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Department of Neurosurgery, University of New Mexico, New Mexico, NM, USA Karsten  Krakow, MD PhD Asklepios Neurologische Klinik Falkenstein, Königstein, Germany Vinodh  A.  Kumar, MD Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Saulo Lacerda, MD  Radiologia - Neurorradiologia na Hospital da Bahia, Salvador, Bahia, Brazil Steven  Lange, MD Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA Meng  Law, MD Nuclear Medicine, Neuroscience, Electrical and Computer Systems Engineering, Monash University, Alfred Health Organization, Melbourne, VIC, Australia Department of Radiology and Nuclear Medicine, Monash University, Alfred Hospital, Melbourne, VIC, Australia Department of Radiology, Alfred Health, Department of Neuroscience, Monash University, Melbourne, VIC, Australia Jonathan K. Lee, MD  Imaging Institute, Cleveland Clinic, Cleveland, OH, USA Laurie  A.  Loevner, MD  Department of Radiology, Division of Neuroradiology, Perelman Center for Advanced Medicine, Philadelphia, PA, USA Department of Neurosurgery, Perelman Center for Advanced Medicine, Philadelphia, PA, USA Department of Ophthalmology, Perelman Center for Advanced Medicine, Philadelphia, PA, USA Penn Radiology Perelman, Perelman Center for Advanced Medicine, Philadelphia, PA, USA Lisa Loi  Department of Radiology, German Cancer Research Center, Heidelberg, Germany M.  Beatriz  Lopes, MD, PhD  Department of Pathology, Divisions of Neuropathology and Molecular Diagnostics, University of Virginia Health System, Charlottesville, VA, USA Mary  Jo  Maciejewski, PhD  Educational Therapy and Enrichment Support, Houston, TX, USA Dan  Ma, PhD  Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA Mohit  Maheshwari, MBBS, MD Department of Radiology, Children’s Wisconsin and Medical College of Wisconsin, Milwaukee, WI, USA Shamseldeen Y. Mahmoud, MD  Department of Radiology, Saint Louis University, St. Louis, MO, USA Muhammed Malik, BS  Department of Radiology, Temple University School of Medicine, Philadelphia, PA, USA Arichena  Manmatharayan, MBBS Department of Neuroradiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA

Contributors

Contributors

xxi

Juan Márquez, MD  Diagnostic Imaging, Fundacion Valle Del Lili, Cali, Colombia Jedediah Mathis, BS  Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA Caio M. Matias, MD  Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Junko Matsuzaki, PhD  Department of Radiology, Lurie Family Foundations MEG Imaging Center, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Brenna C. McDonald, PsyD, MBA  Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA Erin C. McGlade, PhD  Department of Psychiatry, University of Utah, Salt Lake City, UT, USA Colin  D.  McKnight, MD  Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA Karen Medler  Clinical Research Ethics Board, Vancouver Island Health Authority, Victoria, BC, Canada Elias  R.  Melhem, MD, PhD Department of Diagnostic Radiology & Nuclear Medicine, Neuroradiology, University of Maryland School of Medicine, Baltimore, MD, USA Jingya Miao, MS  Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA Devon  M.  Middleton, PhD Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA David  J.  Mikulis, MD Joint Department of Medical Imaging, Toronto Western Hospital, Toronto, ON, Canada Timothy  R.  Miller, MD Department of Diagnostic Radiology & Nuclear Medicine, Neuroradiology, University of Maryland School of Medicine, Baltimore, MD, USA Feroze B. Mohamed, PhD, FASFNR  Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA Melanie A. Morrison, PhD  Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA Department of Medicine, Imperial College London, London, UK Nikolaos  Mouchtouris, MD Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Wade  M.  Mueller, MD Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA Jennifer Muller, MSc  Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Naira  Muradyan, PhD U.S.  Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA Seyed  Ali  Nabavizadeh, MD Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Ameya P. Nayate, MD  Cleveland Medical Center, Cleveland, OH, USA

xxii

Department of Radiology, University Hospitals, Case Western Reserve University School of Medicine, Cleveland, OH, USA Sanaz Niri, MD  Department of Radiology, Stanford University, Stanford, CA, USA Paolo Nucifora, MD, PhD  Department of Radiology, Loyola University Chicago, Maywood, IL, USA Daniel  Paech, MD, MS Department of Radiology, German Cancer Research Center, Heidelberg, Germany Sohil H. Patel, MD  Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA Jay J. Pillai, MD  Division of Neuroradiology, Mayo Clinic College of Medicine and Science, Rochester, MN, USA Francesca Benedetta Pizzini, MD, PhD  Radiology, Department of Diagnostic and Public Health, Verona University, Verona, Italy Nader Pouratian, MD, PhD  Department of Neurological Surgery, University of California, Los Angeles, Los Angeles, CA, USA C. Chad Quarles, PhD  Division of Neuroimaging Research, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, USA Rupa  Radhakrishnan, MBBS, MS Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA Eytan Raz, MD, PhD  Department of Radiology, New York University Medical Center, New York, NY, USA Kristin J. Redmond, MD, MPH  Department of Radiation Oncology and Molecular Radiation Sciences, The Johns Hopkins University, Baltimore, MD, USA Danielle Reitsma, PhD  Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA Harold  L.  Rekate Department of Neurosurgery, Hofstra Northwell School of Medicine, Great Neck, NY, USA Department of Neurosurgery, Northshore University Hospital, Manhasset, NY, USA Daniele  Rigamonti Department of Neurosurgery, Johns Hopkins Medical Institutions, Baltimore, MD, USA Shannon  L.  Risacher, PhD Department of Radiology and Imaging Sciences, Indiana Alzheimer’s Disease Research Center and Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA Joanne  M.  Rispoli, MD  Department of Radiology, New York University, New York, NY, USA Department of Radiology, Boston Children’s Hospital, Boston, MA, USA Tanvir  Rizvi, MBBS, MD  Department of Radiology and Medical Imaging, University of Virginia Health, Charlottesville, VA, USA Timothy  P.  L.  Roberts, PhD Department of Radiology, Lurie Family Foundations MEG Imaging Center, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA Bruce  R.  Rosen, MD, PhD Department of Radiology, Athinoula A.  Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA

Contributors

Contributors

xxiii

Daniel Ryan, MD  Department of Neuroradiology, Johns Hopkins Hospital, Baltimore, MD, USA Haris  I.  Sair, MD Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD, USA The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA Noriko Salamon, MD, PhD  Department of Radiology, David Geffen School of Medicine at UCLA, Ronald Reagan Medical Center, Los Angeles, CA, USA Betty  Jo  Salmeron, MD Cognitive and Affective Neuroscience of Addiction Section, Biomedical Research Center, The National Institute on Drug Abuse (NIDA), Baltimore, MD, USA Thiparom  Sananmuang, MD Department of Diagnostic and Therapeutic Radiology and Research, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand Christin  Sander, PhD Department of Radiology, Athinoula A.  Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA Andrew  J.  Saykin, PsyD Department of Radiology and Imaging Sciences, Indiana Alzheimer’s Disease Research Center and Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA Pamela W. Schaefer, MD  Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA Dustin Scheinost, PhD  Interdepartmental Neuroscience Program, Department of Radiology and Biomedical Imaging, Department of Biomedical Engineering, Department of Statistics & Data Science, Child Study Center, Yale University School of Medicine, New Haven, CT, USA Eric D. Schwartz, MD  Department of Radiology, St. Elizabeth’s Medical Center, Brighton, MA, USA Erin Simon Schwartz, MD, FACR  Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA Varun Sethi, MD  Department of Radiology, Temple University, Philadelphia, PA, USA Ashwini Sharan, MD  Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA Xilin Shen, PhD  Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA Timothy  M.  Shepherd, MD, PhD Department of Radiology, New York University, New York, NY, USA Christopher  A.  Sheridan  Department of Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA Chandni Sheth, PhD  Department of Psychiatry, University of Utah, Salt Lake City, UT, USA Mark S. Shiroishi, MD, MS  Division of Neuroradiology, Department of Radiology, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA Thinesh Sivapatham, MD  Comprehensive Stroke Program, Interventional Neuroradiology, Christiana Care Health System, Newark, DE, USA

xxiv

A.  Gregory  Sorensen, MD Department of Radiology, Athinoula A.  Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA Ashok Srinivasan, MD, FACR  University of Michigan, Ann Arbor, MI, USA Daniel S. R. Stahl, DO  Rutgers New Jersey Medical School, Newark, NJ, USA Dan J. Stein, PhD  Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa Pia C. Sundgren, MD, PhD  Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden Department for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden Adam  P.  Sweeney Department of Radiology, Wake Forest School of Medicine, Winston-­ Salem, NC, USA Viktor  Szeder, MD, PhD, MSc Radiology and Neurosurgery, Division of Interventional Neuroradiology, Department of Radiology, UCLA Ronald Reagan Medical Center, Los Angeles, CA, USA Thomas M. Talavage, PhD  Department of Biomedical Engineering, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, USA Kiran  Shankar  Talekar, MBBS, MD, DABR Department of Radiology, Division of Neuroradiology and ENT, Thomas Jefferson University Hospital, Philadelphia, PA, USA Giacomo Talenti, MD  Neuroradiology Unit, University Hospital of Padua, Padua, Italy Huan Tan, PhD  Beghou Consulting, Durham, NC, USA Keith  R.  Thulborn, MD, PhD Center for Magnetic Resonance Research, University of Illinois Health, Chicago, IL, USA Majda M. Thurnher, MD  Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria Ivan  Tkáč, PhD Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA Elizabeth Tong, MD  Department of Radiology, Stanford University, Stanford, CA, USA Nicholas  J.  Tustison, DSc  Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, USA John L. Ulmer, MD  Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA Neuroradiology, Medical College of Wisconsin, Milwaukee, WI, USA Arastoo Vossough, PhD, MD  Department of Radiology, Children’s Hospital of Philadelphia— University of Pennsylvania, Philadelphia, PA, USA Adam D. Waldman, BSc (Hons), MA, MB, BChir, PhD  Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK Richard Watts, DPhil  Department of Psychology, Yale University School of Medicine, New Haven, CT, USA Christopher T. Whitlow, MD, PhD, MHA  Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA

Contributors

Contributors

xxv

Eli S. Williams, PhD  Department of Pathology, Division of Laboratory Medicine, University of Virginia Health System, Charlottesville, VA, USA Bayard  Wilson, MD Department of Neurological Surgery, University of California, Los Angeles, Los Angeles, CA, USA Max Wintermark, MD, MS, MBA  Department of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Heather  A.  Wishart, PhD Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA Department of Psychiatry, Dartmouth-Hitchcock Medical Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA Ronald L. Wolf, MD, PhD  Department of Radiology, Division of Neuroradiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA Yihong  Yang, PhD Neuroimaging Research Branch, Magnetic Resonance Imaging and Spectroscopy Section, Biomedical Research Center, The National Institute on Drug Abuse (NIDA), Baltimore, MD, USA Michael P. Yannes, MD  Diagnostic Radiology, St. Luke’s University Health Network, The Vascular Center, Easton, PA, USA Deborah  Yurgelun-Todd, PhD Department of Psychiatry, University of Utah, Salt Lake City, UT, USA Yannan Yu, MD  Department of Radiology, Stanford University, Stanford, CA, USA Domenico Zaca, PhD  Siemens Healthcare, Milan, Italy Greg Zaharchuk, MD, PhD  Department of Radiology, Stanford University, Stanford, CA, USA Moss Zhao, DPhil  Department of Radiology, Stanford University, Stanford, CA, USA Guangming Zhu, MD, PhD  Department of Radiology, Stanford University, Stanford, CA, USA Jian Zhu, PhD  Department of Radiology and Imaging Sciences, Indiana Alzheimer’s Disease Research Center and Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, IN, USA Department of Psychology, Eastern Illinois University, Charleston, IL, USA

Part I Diffusion and Perfusion Imaging: Physical Principles

1

Physical Principles of Diffusion Imaging Thinesh Sivapatham and Elias R. Melhem

Introduction While a still body of water may appear static to the eye, water molecules are in constant random motion at a microscopic level. This is termed Brownian motion, and is a result of the thermal agitation of the water molecules. While Brownian motion is a microscopic phenomenon, it results in a macroscopically observable phenomenon known as diffusion. The diffusion of water molecules in the brain provides us with a sensitive window to its underlying physiology and structure. Diffusion-weighted imaging (DWI) of the brain was introduced into clinical use in the early 1990s, primarily in the detection of acute ischemic stroke [1–4]. Since that time, advances in technology have resulted in significant improvements in image quality, allowing the application of DWI to the evaluation of a variety of intracranial disease processes, such as infections, neoplasms, demyelinating processes, and trauma. The development of diffusion tensor imaging (DTI) has allowed mapping of white matter tracts in the brain, and is discussed elsewhere in this book. In this chapter, we review the physical principles of DWI, while the clinical applications of DWI are discussed in following chapters.

Brownian Motion and Molecular Diffusion In 1827, Robert Brown, a Scottish botanist, discovered the random and constant motion of pollen grains suspended in water while studying them under a light microscope [5]. Brown initially believed this motion to be related to the male T. Sivapatham Comprehensive Stroke Program, Interventional Neuroradiology, Christiana Care Health System, Newark, DE, USA E. R. Melhem (*) Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA e-mail: [email protected]

sexual cells of plants, but after further investigation of both organic and inorganic materials, he found this motion to be a general property of small particles suspended in solution. We currently know that this motion is attributed to the constant motion of the water molecules that the particles are suspended in. This motion is known as Brownian motion, or diffusion, and is a result of constant random microscopic molecular motion due to thermal agitation. The molecular motion is related to the thermal kinetic energy (Ekin), of the molecules, which is proportional to the temperature, T: Ekin = (3/2)kBT; where kB = 1.38 × 10−23 J/K is the Boltzmann constant. This Brownian motion also depends upon the size of the particles, density, and viscosity of the medium. Diffusion is a naturally occurring transport process at the molecular level that describes the spread of particles through random motion. This mixing or mass transport occurs as a result of collisions between molecules in liquids and gases rather than by bulk motion as is necessary for other transport mechanisms such as convection or dispersion. When there is a concentration gradient, particles spread from areas of higher concentration to areas of lower concentration until their distribution becomes equilibrate. This results in a net flux of particles with a magnitude that is proportional to the concentration gradient and to the diffusion coefficient, as described by Fick’s First Law and represented by the following equation: (1.1) J = D∗∇ C where J is the flux density, D is diffusion coefficient, and ∇C is change in concentration. The diffusion coefficient is a constant for a given substance in a given medium of known viscosity at a given temperature. Diffusion can also occur in the absence of a concentration gradient, but in this scenario the diffusive fluxes cancel each other out, resulting in no net flux. The concept of molecular diffusion was first formally described by Einstein in 1905 [6]. In a contained volume of water, each water molecule undergoes random motion as part of the diffusion process. This phenomenon of thermal

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Faro, F. B. Mohamed (eds.), Functional Neuroradiology, https://doi.org/10.1007/978-3-031-10909-6_1

3

T. Sivapatham and E. R. Melhem

4

motion of water molecules in a medium that itself consists primarily of water is termed self-diffusion. The path of any single water molecule is completely random and would be impossible to predict, limited only by the boundaries of the container. Einstein was able to prove that the squared displacement of molecules from their starting point could be described by the equation: r 2 = 6 Dt (1.2) where refers to the mean squared displacement of the molecules from their original location, t is the diffusion time, and D is the diffusion coefficient for the particular substance being studied. The diffusion coefficient is typically expressed in units of mm2/s and relates the average displacement of a molecule over an area to the observation time; higher diffusion constants infer increased mobility of water molecules. The diffusion coefficient of water at body temperature (37 °C) is 3 × 10−3 mm2/s. The distribution of squared displacements of free water molecules is typically a Gaussian (bell-­ shaped) function with peak at zero displacement, indicating that most molecules have the same position at the starting point and at time t [7, 8]. The probability of molecular displacement by a given distance from the starting position is the same regardless of the direction of measurement, with standard deviation proportional to the diffusion coefficient and time measured. Diffusion-weighted (DW) magnetic resonance imaging (MRI) utilizes Brownian motion to study the movement of water in  vivo, thereby garnering information about the underlying tissue structure of the brain. DW MRI does not measure the diffusion coefficient directly, but rather the effect of the mean displacement of water molecules within each three-dimensional (3-D) volume element, or voxel, on the nuclear magnetic resonance (NMR) signal. One might ask: If the diffusion coefficient of water at body temperature is a constant, then how can we use this information to evaluate tissue structure? We must remember that in vivo, water molecules do not freely diffuse as they would in a medium of water alone. In tissues, the movement of water occurs largely in the extracellular space, and their movement is modified by interactions with cell membranes and macromolecules, i.e., the underlying tissue structure of the brain. Additionally, the movement of water molecules in vivo due to diffusion cannot be distinguished from the movement of water molecules from other sources such as active transport, pressure gradients, ionic interactions, and changes in membrane permeability. As a result, the overall movement of water molecules in the brain is reduced as compared to their movement in free

water, and only the “apparent diffusion coefficient” (ADC) can be calculated [7–9]. The average ADC in the brain is approximately 0.7 × 10-3 mm2/s [10], about four times smaller than the diffusion coefficient in free water.

 iffusion Encoding and the Stejskal-Tanner D Equation Shortly after Bloch and Purcell discovered the NMR phenomenon [11–13], Hahn reported his findings that the NMR spin-echo was sensitive to the effects of diffusion [14]. He noted that the random thermal motion of the spins would reduce the amplitude of the observed spin-echo signal as a result of the dephasing that occurs in the presence of magnetic field inhomogeneity, which results in  local magnetic field gradients. Building on these observations, Carr and Purcell proposed NMR sequences using constant gradients to sensitize the NMR spin-echo to the effects of diffusion, and developed a mathematical framework to measure the diffusion coefficient from these sequences [15]. In 1956, Torrey modified Bloch’s magnetization equations to include the effects of molecular diffusion [16]; the Bloch-Torrey equations describe how net magnetization depends on several factors, including longitudinal and transverse magnetization, as well as diffusion. In their seminal paper on the spin-diffusion experiment in 1965, Stejskal and Tanner described the theory of the pulsed gradient spin-echo (PGSE), which replaced the steady-state gradients used by Carr and Purcell with short-duration gradient pulses [17]. This resulted in much improved sensitivity to diffusion by distinguishing the encoding time (pulse duration) and the diffusion time. This also allowed the direct measurement of the diffusion function and paved the way for quantitative measurements of the diffusion coefficient. In the presence of a magnetic field, static spins accumulate phase shifts according to the equation:

Φ ( t ) = γ B0t + ∫ G ( t ) . X ( t ) dt

(1.3)

The first term (γB0t) in the equation represents the phase accumulation owing to the static magnetic field, while the second term (∫G(t).X(t)dt) reflects the effect of a magnetic field gradient. The phase accumulation owing to the gradient is proportional to the strength of the field gradient, spatial location of the spin, and the duration of the gradient pulse. In their experiment, Stejskal and Tanner sensitized a standard spin-echo T2-weighted pulse sequence to diffusion by adding a pair of diffusion gradients along the same

1  Physical Principles of Diffusion Imaging

axis both before and after the 180° refocusing pulse [17]. The basic idea is to excite the spins with a 90° radiofrequency (RF) pulse, encode the spin position with a timeconstant magnetic field gradient of duration δ, invert the spin phase with a 180° refocusing pulse, apply a second magnetic field gradient equal in intensity and duration to the first gradient at a time Δ after the first gradient, and then acquire the echo at time TE—echo time. The application of a linear gradient to a homogeneous magnetic field results in phase shifts of the spins along that axis, which is position dependent; this phase dispersion leads to signal loss. However, the application of a second gradient equal to the first in magnitude and duration but opposite in direction can refocus the phase changes. The first gradient is called the dephasing gradient, while the second is called the rephasing gradient [18]. If a spin is stationary between the applications of the two gradient pulses, the net effect of the gradients is zero and the spin maintains its signal intensity. This explains the relatively high signal intensity on DWI seen in the setting of cytotoxic edema, where there is a relative increase in intracellular water content and water molecules are “trapped” inside the cells (i.e., unable to diffuse freely) and relative decrease in the size of the extracellular compartment [19– 25]. For a spin that moves along the axis of the diffusion gradient, phase accumulation due to the two gradients is no longer equal so the rephasing is incomplete. As a result, intravoxel dephasing occurs and there is a loss of signal intensity; the greater the distance the spin moves along the axis of the gradient, the greater the signal loss [8, 14, 16, 17]. This explains the relatively low signal intensity seen on DWI in the normal brain parenchyma, and even lower signal intensity in the cerebrospinal fluid (CSF) spaces, where water can diffuse the most freely.

5

The effects of signal loss due to diffusion can be explained by the Stejskal-Tanner equation:

S = S0 exp ( −bD )

(1.4)

where S is the signal intensity observed in a given voxel with a diffusion gradient applied, S0 is the signal intensity of the same voxel in the absence of a diffusion gradient, b is the diffusion sensitivity factor, and D is the diffusion coefficient. The diffusion sensitivity factor (b) is a function of the strength, duration, and temporal spacing of the diffusion-­ sensitizing gradients, and can be expressed by the following equation:

b = γ 2 G 2δ 2 ( ∆ − δ / 3)

(1.5)

In this equation, γ is the gyromagnetic ratio (the ratio of magnetic moment to angular moment of a nuclear spin, a constant), G is the amplitude of the diffusion gradient (usually measured in milliteslas per meter), δ is the duration of the diffusion gradient pulse (measured in milliseconds), and Δ the time interval between the dephasing and rephasing gradient pulses (also measured in milliseconds). The diffusion sensitivity factor (b) is measured in units of seconds/mm2. Raising the b-value increases the degree of diffusion weighting (i.e., increases the signal loss caused by the diffusion of water molecules along the direction that the gradient was applied; Fig. 1.1). The most effective ways to increase diffusion sensitivity, as seen in Eq. (1.3), are to increase gradient amplitude (G) and gradient duration (δ), as these parameters have a quadratic effect on b. Typical b-values in clinical DWI range from 0 to 1500 s/mm2. However, it should be emphasized that higher diffusion weightings increase exponentially the contrast between tissues with different diffusion coefficients, but at the same time they decrease the overall signal intensity and signal-to-noise ratio (SNR) [26, 27].

6

T. Sivapatham and E. R. Melhem

Fig. 1.1 Diffusion-weighted images acquired using diffusion sensitivity factors (b) of increasing strength. The b = 0 images (top row) demonstrate the T2-weighting inherent in DWI. As the diffusion weighting is increased (b = 500 and b = 1000, second and third rows, respectively), the diffusion of water molecules results in signal loss, most apparent in the CSF where water can diffuse freely. Corresponding ADC map is shown in the bottom row. ADC apparent diffusion coefficient, CSF cerebrospinal fluid, DWI diffusion-weighted imaging

Diffusion Anisotropy Isotropic diffusion refers to a condition where molecular motion is the same in all directions. Since the diffusivity is independent of direction in this scenario, the displacement distribution is Gaussian and can be conceptualized as a sphere (i.e., if the center of the sphere is the starting point of a water molecule, the probability of diffusing any given distance from the center is equal in all directions) [7, 8, 28].

Biological tissues are heterogeneous in their structure and consist of various compartments and barriers of various permeability. Neuronal tissue, in particular, is highly fibrillar, with tightly packed and coherently aligned axons surrounded by a network of glial cells that are often themselves organized in bundles. The movement of water molecules in these tissues is not isotropic, but rather has a propensity to be greater in a direction parallel to the fiber tracts than perpendicular to it. Diffusion in this scenario varies with direction,

1  Physical Principles of Diffusion Imaging

7

and is described as anisotropic [29–33]. The associated displacement distribution of anisotropic diffusion is not Gaussian or spherical, but rather ellipsoid (or even more complex if the underlying tissues contain fibers with various orientations). This anisotropy can be explained by the diffusion tensor model, and is discussed further in the chapter on diffusion tensor imaging (DTI). While isotropic diffusion can be seen in the cerebrospinal fluid (CSF) spaces and in the gray matter of the adult cerebral cortex, diffusion in the white matter is primarily anisotropic. Multiple explanations have been suggested for the mechanisms underlying diffusion anisotropy of white matter, including the myelin sheath, axonal direction, axonal transport, and local susceptibility gradients [34–36]. Myelin itself does not appear to be necessary for diffusion anisotropy, as experiments have shown anisotropic diffusion in the absence of myelin [37, 38]. A series of experiments in the 1990s excluded the effects of susceptibility-induced gradients, axonal cytoskeleton, and fast-axonal transport as the etiology of anisotropy in white matter [39–41]. Current evidence suggests that the presence of intact cell membranes is the tissue component predominantly responsible for the anisotropy of molecular diffusion in white matter, while the degree of myelination and cellular density serve to modulate anisotropy [42].

Calculating the ADC Since diffusion in the brain is approximated by the ADC rather than by direct measurement of the diffusion coefficient, Eq. (1.2) might be better represented as:

Si = S0 exp ( −bADCi )

(1.6)

where Si is the signal intensity in a given voxel with the diffusion-­sensitization gradient applied along direction i, and ADCi is the ADC in the i direction. In an isotropic environment, the direction of the gradient pulse is irrelevant, since the ADCi would be identical for all directions i, and a single (scalar) diffusion gradient application is sufficient to calculate the ADC.  Higher ADC values result in lower signal intensity Si in the DWI, while reduced ADC values result in higher signal intensity. In addition to contrast due to differences in ADC, the long TE of a DWI pulse sequence makes it inherently sensitive to T2-contrast as well. T2-prolongation in pathologic tissues can elevate the DWI signal intensity in the absence of reduced ADC values [43]. This “T2-shine-­ through” effect results in the DWI hyperintensity being less specific than reduced ADC values on ADC maps in the measurement of true restricted diffusion in tissues (Fig. 1.2). In order to remove the T2-effects, the diffusion experiment is repeated for each gradient direction with a low

b-value and high b-value (commonly 0 s/mm2 and 1000 s/ mm2 in clinical practice). The low b-value sequence essentially yields a T2-weighted image. The DW image Si (with b = 1000 s/mm2) can then be divided by the T2-weighted image S0 (with b = 0 s/mm2) to remove the T2-weighting effects and produce an exponential image, also known as an attenuation coefficient map, exponential diffusion-weighted image, or the attenuation factor map (shown in Fig.  1.3). Areas of restricted diffusion on these maps will demonstrate increased signal intensity, similar to DWI. ADC values can also be calculated by solving for ADC in Eq. (1.7): (1.7) ADCi = − ln ( Si / S0 ) / b where ln is the natural logarithm. Instead of deriving the ADC value mathematically, however, the ADC is typically determined graphically by obtaining two image sets (again, low b-value and high b-value image sets) and plotting the natural logarithm of Si versus b for the two b-values; the ADC is then determined from the slope of that line. Biological tissue can be considered a combination of intra- and extracellular compartments in which the water molecule is in a state of continuous exchange between these two compartments. The observed signal attenuation in the diffusion experiment therefore depends on the rate of exchange and the diffusion time. In the limit of slow exchange, water spins remain within their respective compartments during the diffusion time and signal attenuation follows a bi-exponential behavior. This bi-exponential behavior is given by the equation: S = S0  f1 exp ( −b1ADC1 ) + f 2 exp ( −b2 ADC2 )  (1.8) where f1 and f2 are the volume fractions of water spins within each of the two compartments and f1 + f2 = 1; ADC1 and ADC2 are the apparent diffusion coefficients in the two compartments. On the other hand, in the limit of fast exchange with complete redistribution of water between the two compartments during the diffusion time, the signal attenuation follows a single exponential behavior given by the equation:

S / S0 = exp ( −b∗ ADC )

(1.9)

The observed apparent diffusion coefficient for a two-­ compartment system includes contributions from both the intracellular and extracellular environments and is approximated by the equation:

ADC = f1ADC1 + f 2 ADC2 (1.10)

In most practical situations, the bi-exponential behavior of signal attenuation is not observed. Therefore, it is a common practice to fit the DWI data to the single exponential decay model.

8

T. Sivapatham and E. R. Melhem

a

b

c

d

Fig. 1.2  Images from a patient who presented with an acute stroke demonstrate the T2-shine-through effects of DWI. The FLAIR image (d) shows multifocal areas of T2-prolongation in the periventricular and deep white matter (arrow and arrowhead). The lesion marked by the arrow in (d) also demonstrates increased signal on the b = 0 (a), b = 1000 (b), and ADC (c) images, indicating that this is not an area of

restricted diffusion, but rather T2-shine-through on DWI.  The lesion marked by the arrowhead in (d), on the contrary, also demonstrates marked signal intensity on the b = 0 and b = 1000 images, but is dark on the ADC map, indicating that this is an area of restricted diffusion. ADC apparent diffusion coefficient, FLAIR fluid-attenuated inversion recovery, DWI diffusion-weighted imaging

1  Physical Principles of Diffusion Imaging Fig. 1.3 Diffusion anisotropy. The top three rows show apparent diffusion coefficient (ADC) maps derived from diffusion gradients applied in three orthogonal directions. Variations in brightness between the three sets of images demonstrate the anisotropic nature of diffusion in the brain; diffusion of water molecules is greater along nerve bundles than perpendicular to them. When the gradient is applied in the x direction (Dxx; diffusion gradient in the left-to-right direction), diffusion is higher (brighter signal) in fibers that course in the left-to-right direction (i.e., splenium) than perpendicular to that direction (i.e., posterior limb internal capsule, and anterior-to-­ posterior-oriented white matter tracts of the cerebral hemispheres). With gradient applied in the y direction (Dyy; anterior to posterior), diffusion is greatest in the anterior-to-posterior-oriented cerebral white matter tracts, and lowest in the perpendicularly oriented splenium and posterior limb internal capsule. With gradient applied in the z direction (Dzz; cranial to caudal), diffusion is greatest in the posterior limb internal capsule oriented in the same direction, and lowest in the perpendicularly oriented splenium and anterior-to-­ posterior cerebral white matter tracts. The trace ADC images shown in the fourth row are the average of the ADCs from each of the three directions, and show diffusion magnitude information alone with directional information removed. The fifth row shows the corresponding isotropic diffusion-weighted (DW) images. The exponential DW images (Exp, bottom row) were derived by dividing the b = 1000 images by the b = 0 images (with b measured in seconds/mm2) in order to remove the T2-weighting effects

9

10

T. Sivapatham and E. R. Melhem

Isotropic DWI and Trace ADC

tant to concomitantly view the ADC map or exponential image to evaluate for true restricted diffusion. In neonates and young children, the ADC is initially Diffusion gradient pulses are applied in one direction at a much higher in rapidly developing gray matter structures time, with the resulting DW image giving both directional like the thalamus and basal ganglia, and can be more than and magnitude information about the ADC. This would be twice as high in the slowly developing white matter regions sufficient if imaging an isotropic tissue, since the ADC [44, 45]. This can be attributed to the relatively high water would be the same regardless of the direction of the diffusion gradient. When imaging an anisotropic tissue like the white content in the immature brain, with relative paucity of matter of the brain, however, interpretation of the DWI myelinated neurons. ADC values decrease rapidly over the would be challenging if only a single gradient direction were first 2 years of brain development, and continue to decrease probed, due to the variable signal intensity of white matter gradually through childhood, adolescence, and into young tracts depending on their orientation relative to the direction adulthood [46–52]. Due to the higher ADC values in neoof the diffusion gradient. While the diffusion anisotropy dis- nates and infants compared to adults [45–48], it is common cussed in the previous section can be exploited to image fiber to reduce the diffusion sensitivity factor (b) in this age 2 2 tracts in DTI, the directional effects of anisotropy are unde- group to 600 or 700 s/mm , compared to the 1000 s/mm typically used in adults. In the aging but otherwise healthy sirable in routine clinical DWI. In order to overcome the directional influences of the dif- brain, mild increases can be seen in the ADC, particularly fusion gradient in anisotropic tissues, four separate scalar in white matter [53–58]. Engelter et al. found a significant image acquisitions are required: one without a diffusion-­ correlation between the average ADC of white matter and sensitizing gradient (S0, where b = 0 s/mm2), and three with age, with patients 60 years of age or older having increased the diffusion gradients applied in three orthogonal directions ADC compared to those under the age of 60; a similar trend x, y, and z (which will be called Sx, Sy, and Sz). To create an was seen in the average ADC of the thalamus [55]. Chen image related only to the magnitude of the ADC, the DW et  al. found that the average ADC increases by 3% per images acquired with the gradient pulses applied in three decade after the age of 40 [56]. This has been attributed to orthogonal planes can be multiplied, and the cube root of that loss of myelinated neurons and structural changes seen product yields a DW image weighted with diffusion magni- with aging. tude information alone and directional information removed, called the “isotropic DWI” (SDWI): S DWI = ( S x S y S z ) 1/ 3



(1.11)

The ADC derived from application of the gradients in three orthogonal planes, called the “trace ADC” (simply called ADC below), is the average of the ADCs from each of the planes (Fig. 1.3):

ADC = ( ADC x + ADC y + ADCz ) / 3

(1.12)

The images submitted to the radiologist for interpretation in the clinical setting are typically the isotropic DWI and trace ADC images.

DWI of the Normal Brain As mentioned previously, the average ADC in the brain is approximately 0.7 × 10−3 mm2/s; more specifically, mean diffusivities in the adult brain range from 0.67 to 0.83 × 10−3 mm2/s in gray matter, and 0.64 to 0.71 × 10−3 mm2/s in white matter [10]. Because the ADC values for gray and white matter are so similar, there is essentially no contrast between gray and white matter on the ADC map or exponential image. The subtle gray–white matter contrast that can be seen on the DWI can be attributed to T2-effects, which makes it impor-

Current DWI Techniques

The movement of water molecules detected by DWI occurs on a length scale of micrometers that is significantly larger than intracellular distances, but much smaller than the millimeter scale of voxel size in typical magnetic resonance (MR) images. The original PGSE T2-weighted sequence described by Stejskal and Tanner was sensitive to even minimal bulk patient motion, which was enough to obscure the much more miniscule molecular motion of diffusion. As MRI technology has improved and high-performance gradients have been developed, DWI is now typically performed using spin-echo single-shot echo-planar imaging (SS-EPI) techniques. With this type of pulse sequence, an entire two-dimensional (2-D) image can be formed from a single RF excitation pulse (hence the term “single-shot”). Images can be acquired in a fraction of a second, minimizing artifacts related to patient motion. Additionally, the SS-EPI technique has a relatively high signal-to-noise ratio (SNR); this is an important advantage in DWI, as high b-value diffusion gradients cause a significant loss of signal intensity (see Eq. (1.4)). Using the SS-EPI technique, DW imaging of the brain can typically be completed in 1–2 min, beneficial in clinical settings where acutely ill and often uncooperative patients (i.e., hyperacute stroke patients) are being imaged.

11

1  Physical Principles of Diffusion Imaging

Limitations of the SS-EPI technique include low spatial resolution, blurring effects of T2*- and T2-decay, and sensitivity to artifacts. Matrix size using the single-shot technique is limited to 128 × 128 for a typical DWI using current MRI hardware and software, compared to matrix sizes of 256 × 192 or larger for standard T1- and T2-weighted sequences. Blurring of T2*- and T2-weighted contrast occurs during image readout due to the extended echo-train length. SS-EPI is also sensitive to artifacts due to Nyquist ghosting, chemical shift, and particularly magnetic field inhomogeneities caused by local susceptibility differences between adjacent structures. As stronger and faster gradients are developed to improve DWI, problems such as eddy currents and mechanical vibration may be exacerbated, resulting in additional artifacts. These artifacts are explained in detail in reviews by Le Bihan et al. [59], and by Mukherjee et al. [60]. The development of multichannel coils and parallel imaging has also led to improvements in DWI. Compared to standard head RF coils, which have a uniform sensitivity throughout their imaging volume, the newer multichannel phased-array RF coils have increased sensitivity around their periphery than at their center, resulting in improved SNR in the cerebral cortex and, therefore, improved DW images. These coils also allow parallel imaging on modern MR scanners due to their multiple independent receiver channels. Parallel imaging techniques can be used to shorten the echo-­ train length of EPI, thereby reducing the susceptibility-­ induced and blurring artifacts that typically occur with extended echo trains. The shorter readout may also increase SNR by decreasing the TE. Alternatives to SS-EPI DWI include variations of fast spin-echo (FSE) or turbo spin-echo (TSE) imaging, multishot EPI, spiral imaging, and line-scan imaging techniques [60, 61]. SS-FSE and half-Fourier acquired single-shot turbo spin-echo (HASTE) are ultrafast sequences that limit artifacts related to bulk patient motion similar to SS-EPI, but with reduced susceptibility and chemical shift artifacts compared to SS-EPI.  However, they require longer scan times due to their low SNR compared with SS-EPI, and therefore have not become popular techniques for brain DWI imaging. Multishot techniques also confer a reduction in susceptibility artifacts compared with SS-EPI, but scan times are longer, which makes these methods more sensitive to bulk patient motion. One multishot FSE technique reduces motion artifacts by continually oversampling the center of k-space, and is called periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER). This technique improves detection of small acute infarcts, particularly at the skull base and in the posterior fossa, where SS-EPI techniques have significant susceptibility-induced distortion. Due to much longer scanning times, however, the technique has not surpassed SS-EPI for routine clinical DWI.

Conclusion From the observation of Brownian motion in 1827 to the description of molecular diffusion by Einstein in 1905, and the application of these principles to DW MRI, a great deal has been learned about the movement of water molecules in the brain (Table 1.1). DWI is a powerful tool that provides a great deal of information about the underlying structure and physiology of the brain that is not provided with standard T1- and T2-weighted imaging techniques. DWI was first Table 1.1  Summary of articles reviewing the basic principles of DWI discussed in this chapter Authors Schaefer et al. (2000) [9]

Topic Diffusion-weighted MR imaging of the brain

Summary Reviews the basic principles behind DW MR imaging of the brain, as well as clinical applications of the technique Bammer Basic principles of Describes the physics behind (2003) [61] diffusion-weighted image contrast in DWI and imaging various imaging sequences that can be utilized for DWI, with respect to speed, spatial resolution, and sensitivity to motion; more advanced diffusion measurement techniques are also discussed Hagmann Understanding Explains the physics of water et al. diffusion MR imaging diffusion and basic principles (2006) [8] techniques: from of diffusion contrast encoding scalar diffusion-­ with MRI; discusses and weighted imaging to compares the spectrum of diffusion tensor diffusion-based MR imaging imaging and beyond techniques, from the simplest to most complex Le Bihan Artifacts and pitfalls Artifacts and pitfalls of et al. in diffusion MRI diffusion-based MR imaging (2006) [59] techniques are discussed, as well as potential strategies to overcome them Jones Studying connections Review of the basic principles (2008) [7] in the living human underlying DWI, and how brain with diffusion water diffusion can be utilized MRI to study underlying brain structure Mukherjee Diffusion tensor MR Reviews technical et al. imaging and fiber considerations in DWI, (2008) [60] tractography: including current techniques technical and their limitations, considerations optimization of current techniques, and newer techniques that have been developed for both DWI and DTI Mukherjee Diffusion tensor MR Review of the basic physics et al. imaging and fiber and theory underlying DWI (2008) [62] tractography: theoretic and ADC mapping, as well as underpinnings DTI

ADC apparent diffusion coefficient, DW, diffusion-weighted, DWI diffusion-­weighted imaging, DTI diffusion tensor imaging, MR magnetic resonance, MRI magnetic resonance imaging

12

routinely used in brain imaging for the detection of acute ischemia, and is now an essential part of the imaging evaluation of patients with acute stroke. The applications of DWI in neuroimaging are extensive, and include the evaluation of intracranial infections, neoplasms, demyelinating processes, and trauma. These will be discussed in the following chapter. DWI techniques can also be used to evaluate the fiber tracts of the brain, and these techniques and applications are discussed elsewhere in this book. As MRI technology advances and improvements are made in imaging hardware and software, newer techniques and sequences may provide more sensitive DW images with reduction in the artifacts inherent in current DWI techniques.

References 1. Moseley ME, Cohen Y, Mintorovitch J, Chileuitt L, Shimizu H, Kucharczyk J, et al. Early detection of regional cerebral ischemia in cats: comparison of diffusion- and T2-weighted MRI and spectroscopy. Magn Reson Med. 1990;14:330–46. 2. Chien D, Kwong KK, Gress DR, Buonanno FS, Buxton RB, Rosen BR. MR diffusion imaging of cerebral infarction in humans. AJNR Am J Neuroradiol. 1992;13:1097–102. 3. Warach S, Chien D, Li W, Ronthal M, Edelman RR. Fast magnetic resonance diffusion-weighted imaging of acute human stroke. Neurology. 1992;42:1717–23. 4. van Everdingen KJ, van der Grong J, Kappelle LJ, Ramos LM, Mali WP. Diffusion-weighted magnetic resonance imaging in acute stroke. Stroke. 1998;29(9):1783–90. 5. Brown R. A brief account of microscopical observations made in the months of June, July and August 1827 on the particles contained in the pollen of plants, and on the general existence of active molecules in organic and inorganic bodies. Philos Mag. 1828;4:161. 6. Einstein A.  Investigations on the theory of Brownian motion. New York: Dover; 1956. 7. Jones DK.  Studying connections in the living human brain with diffusion MRI. Cortex. 2008;44(8):936–52. 8. Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R.  Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics. 2006;26(Suppl 1):205–23. 9. Schaefer PW, Grant PE, Gonzalez RG.  Diffusion-weighted MR imaging of the brain. Radiology. 2000;217(2):331–45. 10. Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G.  Diffusion tensor MR imaging of the human brain. Radiology. 1996;201:637–48. 11. Bloch F. Nuclear induction. Phys Rev. 1946;70:460–74. 12. Bloch F, Hansen WW, Packard M.  Nuclear induction. Phys Rev. 1946;69:127. 13. Purcell EM, Torrey HC, Pound RV.  Resonance absorption by nuclear magnetic moments in a solid. Phys Rev. 1946;69:37. 14. Hahn EL. Spin echoes. Phys Rev. 1950;80:580–94. 15. Carr HY, Purcell EM.  Effects of diffusion on free precession in nuclear magnetic resonance experiments. Phys Rev. 1954;94:630–8. 16. Torrey HC.  Bloch equations with diffusion terms. Phys Rev. 1956;104:563–35. 17. Stejskal EO, Tanner JE. Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J Chem Phys. 1965;42:288–92. 18. Mori S, Barker PB. Diffusion magnetic resonance imaging: its principle and applications. Anat Rec. 1999;257(3):102–9.

T. Sivapatham and E. R. Melhem 19. Sevick RJ, Kanda F, Mintorovitch J, Arieff AI, Kucharczyk J, Tsuruda JS, et  al. Cytotoxic brain edema: assessment with diffusion-­ weighted MR imaging. Radiology. 1992;185(3):687–90. 20. Ebisu T, Naruse S, Horikawa Y, Ueda S, Tanaka C, Uto M, Umeda M, Higuchi T.  Discrimination between different types of white matter edema with diffusion-weighted MR imaging. J Magn Reson Imaging. 1993;3(6):863–8. 21. Schaefer PW, Buonanno FS, Gonzalez RG, Schwamm LH.  Diffusion-weighted imaging discriminates between cytotoxic and vasogenic edema in a patient with eclampsia. Stroke. 1997;28:1082–5. 22. Schwartz R, Mulkern R, Gudbjartsson H, Jolesz F.  Diffusion-­ weighted MR imaging in hypertensive encephalopathy: clues to pathogenesis. AJNR Am J Neuroradiol. 1998;19:859–62. 23. Ay H, Buonanno FS, Schaefer PW, Le DA, Wang B, Gonzalez RG, Koroshetz WJ. Posterior leukoencephalopathy without severe hypertension: utility of diffusion-weighted MRI.  Neurology. 1998;51:1369–76. 24. Mukherjee P, McKinstry RC.  Reversible posterior leukoencephalopathy syndrome: evaluation with diffusion-tensor MR imaging. Radiology. 2001;219:756–65. 25. Provenzale JM, Petrella JR, Cruz LC Jr, Wong JC, Engelter S, Barboriak DP.  Quantitative assessment of diffusion abnormalities in posterior reversible encephalopathy syndrome. AJNR Am J Neuroradiol. 2001;22:1455–61. 26. Chen PE, Simon JE, Hill MD, Sohn CH, Dickhoff P, Morrish WF, et  al. Acute ischemic stroke: accuracy of diffusion-weighted MR imaging–effects of b value and cerebrospinal fluid suppression. Radiology. 2006;238(1):232–9. 27. Kim HJ, Choi CG, Lee DH, Lee JH, Kim SJ, Suh DC. High-b-value diffusion-weighted MR imaging of hyperacute ischemic stroke at 1.5T. AJNR Am J Neuroradiol. 2005;26(2):208–15. 28. Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci. 2003;4(6):469–80. 29. Cleveland GG, Chang DC, Hazlewood CF, Rorschach HE. Nuclear magnetic resonance measurement of skeletal muscle: anisotropy of the diffusion coefficient of the intracellular water. Biophys J. 1976;16(9):1043–53. 30. Moseley ME, Cohen Y, Kucharczyk J, Mintorovitch J, Asgari HS, Wendland MF, Tsuruda J, Norman D.  Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology. 1990;176(2):439–45. 31. Moseley ME, Kucharczyk J, Asgari HS, Norman D. Anisotropy in diffusion-weighted MRI. Magn Reson Med. 1991;19(2):321–6. 32. Doran M, Hajnal JV, Van Bruggen N, King MD, Young IR, Bydder GM. Normal and abnormal white matter tracts shown by MR imaging using directional diffusion weighted sequences. J Comput Assist Tomogr. 1990;14(6):865–73. 33. Chenevert TL, Brunberg JA, Pipe JG.  Anisotropic diffusion in human white matter: demonstration with MR techniques in  vivo. Radiology. 1990;177(2):401–5. 34. Thomsen C, Henriksen O, Ring P. In vivo measurement of water self-diffusion in the human brain by magnetic resonance imaging. Acta Radiol. 1987;28(3):353–61. 35. Hong X, Dixon WT. Measuring diffusion in inhomogeneous systems in imaging mode using antisymmetric sensitizing gradients. J Magn Reson. 1992;99:561–70. 36. Lian J, Williams DS, Lowe IJ. Magnetic resonance imaging in the presence of background gradients and imaging of background gradients. J Magn Reson Ser. 1994;106:65–74. 37. Wimberger DM, Roberts TP, Barkovich AJ, Prayer LM, Moseley ME, Kucharczyk J. Identification of “premyelination” by diffusion-­ weighted MRI. J Comput Assist Tomogr. 1995;19(1):28–33. 38. Prayer D, Roberts T, Barkovich AJ, Prayer L, Kucharczyk J, Moseley M, Arieff A.  Diffusion-weighted MRI of myelina-

1  Physical Principles of Diffusion Imaging tion in the rat brain following treatment with gonadal hormones. Neuroradiology. 1997;39(5):320–5. 39. Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn Reson Med. 1994;31(4):394–400. 40. Beaulieu C, Allen PS.  Water diffusion in the giant axon of the squid: implications for diffusion-weighted MRI of the nervous system. Magn Reson Med. 1994;32(5):579–83. 41. Beaulieu C, Allen PS. An in vitro evaluation of the effects of local magnetic-susceptibility-induced gradients on anisotropic water diffusion in nerve. Magn Reson Med. 1996;36(1):39–44. 42. Beaulieu C. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed. 2002;15(7):435–55. 43. Burdette JH, Elster AD, Ricci PE. Acute cerebral infarction: quantification of spin-density and T2 shine-through phenomena on diffusion-­weighted MR images. Radiology. 1999;212(2):333–9. 44. Hüppi PS, Maier SE, Peled S, Zientara GP, Barnes PD, Jolesz FA, Volpe JJ. Microstructural development of human newborn cerebral white matter assessed in  vivo by diffusion tensor magnetic resonance imaging. Pediatr Res. 1998;44(4):584–90. 45. Neil JJ, Shiran SI, McKinstry RC, Schefft GL, Snyder AZ, Almli CR, Akbudak E, Aronovitz JA, Miller JP, Lee BC, Conturo TE.  Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. Radiology. 1998;209(1):57–66. 46. Mukherjee P, Miller JH, Shimony JS, Conturo TE, Lee BC, Almli CR, McKinstry RC.  Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR imaging. Radiology. 2001;221(2):349–58. 47. Mukherjee P, McKinstry RC.  Diffusion tensor imaging and tractography of human brain development. Neuroimaging Clin N Am. 2006;16(1):19–43. 48. Mukherjee P, Miller JH, Shimony JS, Philip JV, Nehra D, Snyder AZ, Conturo TE, Neil JJ, McKinstry RC. Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR Am J Neuroradiol. 2002;23(9):1445–56. 49. McGraw P, Liang L, Provenzale JM.  Evaluation of normal age-­ related changes in anisotropy during infancy and childhood as shown by diffusion tensor imaging. AJR Am J Roentgenol. 2002;179(6):1515–22.

13 50. Schneider JF, Il’yasov KA, Hennig J, Martin E.  Fast quantitative diffusion-tensor imaging of cerebral white matter from the neonatal period to adolescence. Neuroradiology. 2004;46(4):258–66. 51. Snook L, Paulson LA, Roy D, Phillips L, Beaulieu C.  Diffusion tensor imaging of neurodevelopment in children and young adults. NeuroImage. 2005;26(4):1164–73. 52. Hermoye L, Saint-Martin C, Cosnard G, Lee SK, Kim J, Nassogne MC, Menten R, Clapuyt P, Donohue PK, Hua K, Wakana S, Jiang H, van Zijl PC, Mori S. Pediatric diffusion tensor imaging: normal database and observation of the white matter maturation in early childhood. NeuroImage. 2006;29(2):493–504. 53. Gideon P, Thomsen C, Henriksen O. Increased self-diffusion of brain water in normal aging. J Magn Reson Imaging. 1994;4(2):185–8. 54. Chun T, Filippi CG, Zimmerman RD, Uluğ AM.  Diffusion changes in the aging human brain. AJNR Am J Neuroradiol. 2000;21(6):1078–83. 55. Engelter ST, Provenzale JM, Petrella JR, DeLong DM, MacFall JR.  The effect of aging on the apparent diffusion coefficient of normal appearing white matter. AJR Am J Roentgenol. 2000;175:425–30. 56. Chen ZG, Li TQ, Hindmarsh T.  Diffusion tensor trace mapping in normal adult brain using single-shot EPI technique. A methodological study of the aging brain. Acta Radiol. 2001;42(5):447–58. 57. Nusbaum AO, Tang CY, Buchsbaum MS, Wei TC, Atlas SW. Regional and global changes in cerebral diffusion with normal aging. AJNR Am J Neuroradiol. 2001;22(1):136–42. 58. Moseley M. Diffusion tensor imaging and aging - a review. NMR Biomed. 2002;15(7-8):553–60. 59. Le Bihan D, Poupon C, Amadon A, Lethimonnier F. Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging. 2006;24(3):478–88. 60. Mukherjee P, Chung SW, Berman JI, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol. 2008;29(5):843–52. 61. Bammer R. Basic principles of diffusion-weighted imaging. Eur J Radiol. 2003;45(3):169–84. 62. Mukherjee P, Berman JI, Chung SW, Hess CP, Henry RG. Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR Am J Neuroradiol. 2008;29(4):632–41.

2

Physical Principles of Dynamic Contrast-­Enhanced and Dynamic Susceptibility Contrast MRI Mark S. Shiroishi, Jerrold L. Boxerman, C. Chad Quarles, Daniel S. R. Stahl, Saulo Lacerda, Naira Muradyan, Timothy P. L. Roberts, and Meng Law

Introduction The use of dynamic contrast-agent-enhanced magnetic resonance imaging (MRI) can provide insight into hemodynamic processes not detectable using conventional contrast-­enhanced magnetic resonance (MR) techniques. This additional data may allow refinement of differential diagnoses based on microvascular physiology. The dominant dynamic gadolinium-based contrast agent (GBCA) injection MRI techniques currently utilized in brain imaging are: (1) T1-weighted dynamic contrastenhanced (DCE) MRI, and (2) T2/T2*-weighted dynamic susceptibility contrast (DSC) MRI.  Of these, DSC-MRI is much more commonly used for clinical perfusion imaging of the brain, especially for the evaluation of stroke and tumor. On the other hand, DCE-­MRI is the dominant method of dynamic contrast-enhanced MRI outside of the brain [1]. In both DCEMRI and DSC-­MRI, dynamic images are acquired before, during, and after the administration of an exogenous GBCA. As opposed to other techniques, such as contrast-enhanced computed tomography (CT), contrast-enhanced MRI is distinctive because it detects the changes induced in the local relaxation times of water rather than detecting the GBCA itself, where the M. S. Shiroishi (*) Division of Neuroradiology, Department of Radiology, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA e-mail: [email protected] J. L. Boxerman Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA e-mail: [email protected]; [email protected] C. C. Quarles Division of Neuroimaging Research, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, USA e-mail: [email protected]

passage of a GBCA through tissue decreases the intrinsic T1, T2, and T2* relaxation times [2]. This chapter will provide an overview of the general physical principles of these techniques. An overview of these two methods is provided in Table 2.1 [3]. Table 2.1  Overview of DCE-MRI and DSC-MRI Bolus handling Acquisition point Contrast media Tracer Relaxation mechanism Effect Signal behaviors

DCE-MRI Bolus passage Accumulation of contrast agent Intravenous bolus injection of a GBCA Flow or permeability-­ limited diffusible tracer T1 relaxation

DSC-MRI Bolus tracking First-pass of contrast agent Intravenous bolus injection of a GBCA Non-diffusible blood pool tracer T2/T2* relaxation

T1 shortening effect

Increased susceptibility effect Decreased signal

Increased signal

DCE-MRI dynamic contrast-enhanced magnetic resonance imaging, DSC-MRI dynamic susceptibility contrast magnetic resonance imaging, GBCA gadolinium-based contrast agent Source: Adapted under terms of Creative Commons license from [3] D. S. R. Stahl Rutgers New Jersey Medical School, Newark, NJ, USA S. Lacerda Radiologia - Neurorradiologia na Hospital da Bahia, Salvador, Bahia, Brazil N. Muradyan U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD, USA e-mail: [email protected] T. P. L. Roberts Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA e-mail: [email protected] M. Law Nuclear Medicine, Neuroscience, Electrical and Computer Systems Engineering, Monash University, Alfred Health Organization, Melbourne, VIC, Australia e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Faro, F. B. Mohamed (eds.), Functional Neuroradiology, https://doi.org/10.1007/978-3-031-10909-6_2

15

M. S. Shiroishi et al.

16

T1-Weighted Dynamic Contrast-Enhanced MRI When applied to the brain, DCE-MRI is primarily employed to characterize the functional integrity of the blood–brain barrier (BBB) via estimation of microvascular permeability to GBCAs. The evaluation of cancer is a major application of DCE-MRI where it has potential to provide prognostic, predictive, and physiological response imaging biomarkers. Conventional GBCAs used in clinical MRI are diffusible, low-molecular-weight extracellular agents (~500 to 1000 Da) that remain intravascular when the BBB is intact. Disruption of the BBB secondary to a variety of pathological processes results in the transfer of GBCA moieties across the capillary endothelium from the intravascular space into the extravascular–extracellular space (EES). Leakage of GBCAs into the EES results in T1-shortening and contrast enhancement on T1-weighted imaging.

DCE-MRI Acquisition There has historically been quite a variation of DCE-MRI acquisition protocols in the literature. DCE-MRI acquisition parameters are generally intended to emphasize R1 contrast and minimize competing T2* effects by employing short echo times (TEs) and repetition times (TRs) [2]. DCE-MRI most often utilizes a fast T1-weighted spoiled gradient-­ recalled echo sequence with the temporal resolution contingent on the volume coverage, contrast-to-noise ratio (CNR), and spatial resolution for a particular organ system [4, 5]. The temporal resolution demands for DCE-MRI are generally less than that for DSC-MRI unless an arterial input function (AIF) is needed [2]. DCE-MRI scan durations are generally much longer than for DSC-MRI and to estimate microvascular permeability with DCE-MRI, the temporal resolution generally ranges between 5 and 20 s [6–8]. Like with DSC-MRI, consistent technique including the use of a power injector for bolus injection (2–4 cc/s) of GBCA followed by a 20–30 cc saline flush at the same rate into the right arm to decrease possible venous reflux should be performed if possible in all cases. Recent initiatives such as those by the Radiological Society of North America’s (RSNA’s) Quantitative Imaging Biomarkers Alliance (QIBA) have focused on standardizing acquisition and analysis of various imaging methods including DCEMRI.  QIBA recommendations for DCE-MRI acquisition are included in Table 2.2 [5]. (Please see section “Standardization Efforts and Variability of DCE-MRI” below). In addition to conventional DCE-MRI acquisition methods, there have been several recent advances in pulse sequence acceleration methods to obtain high temporal and/or spatial resolution in DCEMRI.  These include dynamic compressed sensing combined

Table 2.2  QIBA DCE-MRI acquisition parameters for brain imaging Parameter Field strength Acquisition sequence Receive coil type Lipid suppression Slice thickness Gap thickness FOV Acquisition matrix Plane orientation Phase/frequency encode direction Receiver bandwidth # Phases # Averages Flip angles TR (ms) TE Temporal resolution Total acquisition time

DCE-MRI 1.5/3T 3D SPGR ≥8 channel head array coil On ≤5 mm 0–1 mm 220–240 mm 256 × 128–160 Axial AP/RL 250 Hz/pixel Pre-contrast Post-contrast 40–80 ≥5 1 ≥1 2–30a 25–30 3–8 msb 3–8 ≤3 msb ≤3 ms PS), Ktrans mainly reflects permeability (Ktrans = PS), and in these situations DCE-MRI could be referred to as “permeability imaging” [6, 34, 35]. In the original Tofts model, neglecting the contribution of intravascular tracer to the MRI signal may be appropriate for a diffusible tracer where its distribution volume is large relative to blood volume. However, with an extracellular tracer, this may be problematic as its distribution volume is smaller [6, 36]. This assumption may produce erroneous Ktrans estimates because intravascular tracer could contribute a significant proportion of the observed tissue signal. Therefore, in the presence of an intravascular–extracellular tracer, the model has been modified and expressed as: t

Ct ( t ) = v p C p ( t ) + K trans ∫C p (τ ) e



− K trans ( t −τ ) / ve

0

dτ



where vp represents the blood plasma volume per unit volume of tissue. This model is often referred to as the “extended Tofts model” [6] (Fig. 2.1). vp may be ignored in situations when the plasma volume or tracer concentration is negligible; e.g., hypovascular low-enhancing tissues or a few minutes after the bolus. However, in diseases that are highly perfused, such as neoplasms, there should be consideration of vp [4] (Fig. 2.2). There are less commonly utilized PK models besides the Tofts and extended Tofts models. While the Tofts models assume a bi-directional exchange of CA between the vascular space and EES, a simpler assumption of a unidirectional transport of CA from the vascular to the EES compartment can be formulated. The “Patlak model” [37] utilizes this form and can be expressed as: t



Ct ( t ) = v p C p ( t ) + K trans ∫C p (τ ) dτ 0



The two-compartment exchange model (2XCM) is a more generalized kinetic model than the Tofts and Patlak models. It can be used in mixed perfusion and permeability conditions that can allow estimation of PS and F to be calculated [1, 38, 39]. This takes the form of:

dC p ( t ) dCe ( t ) vp = F ( Ca − C p ) − K PS ( C p − Ce ) and ve = K PS ( C p − Ce ) dt dt where Ca represents the AIF and KPS now can be thought of as Ktrans without the F versus PS uncertainty. It is important to note that more complex modeling such as this necessitates a

b

Fig. 2.2  A 52-year-old female with pathology-proven high-grade glioma. (a) Axial contrast-enhanced T1-weighted image and (b) axial T2-weighted image demonstrate an enhancing tumor in the medial

increased concern regarding sources of error during data acquisition and analysis [40, 41].

c

aspect of the right temporal lobe. (c) Ktrans color map demonstrating a lesion with high values in the enhancing wall of the tumor

2  Physical Principles of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MRI

 tandardization Efforts and Variability S of DCE-MRI

19

ticularly important in longitudinal DCE-MRI studies. In order to analyze image data from the QIBA DCE-MRI phantom, QIBA also provides automated T1 quantification Lately, there has been increasing awareness regarding the software [45]. A recent multicenter phantom study of vendor-provided need to decrease bias and variability of quantitative imaging B1 mapping sequences demonstrated the potential for these biomarkers. Efforts such as the RSNA’s QIBA have focused techniques to provide unbiased and reproducible quantificaon various imaging methods including DCE-MRI [42]. The QIBA Perfusion Biomarker Committee Task Force has been tion of B1 field inhomogeneity that could be used to account continuously working on their DCE-MRI profile [43]. The for spatial variation in the transmitted radio frequency (RF) goal of QIBA profiles such as the one on DCE-MRI is to field [46]. Version 2.0 of the RSNA QIBA DCE-MRI Profile assist in achieving adequate performance for an imaging bio- is currently being written and it will address spatially depenmarker and to provide details about the capabilities and limi- dent B1 field inhomogeneity effects that may affect VFA T1 tations of an imaging marker. It does so by offering guidance measurements. This is particularly problematic at higher regarding imaging acquisition, devices, technologists, radi- fields like 3T and when data are acquired over large anaologists, subject handling, image quality assurance, recon- tomic regions and may necessitate B1 mapping and correcstruction software, imaging analysis tools, and image quality tions to be incorporated in the measurement of T1 values [5]. While the QIBA T1 phantom is a static phantom, there assurance. Standardization of image acquisition parameters is a has been recent work by Kim et al. [47] on a dynamic perfumajor point of emphasis for QIBA. Inter-scanner and inter-­ sion phantom focused on DCE-MRI of the abdomen. They site variability of T1 values in the brain is well known where used two different 3T MRI scanners and three healthy volunthe Look-Locker IR method can underestimate while the teers. When compared to a static phantom, they found that VFA technique can overestimate white matter T1 measure- the perfusion phantom significantly decreased the variability ments [19]. Factors such as the particular MR sequence of contrast concentration and Ktrans measurements measured employed, B1 field inhomogeneity, temperature of the mag- in four abdominal organs (liver, spleen, pancreas, and paranet bore, and incomplete spoiling of transverse magnetiza- vertebral muscles). One should note that while estimates of tion can influence the derived T1 values [44]. Before the DCE-MRI performance can be conducted with phantoms, acquisition of clinical DCE-MRI data, it is important to these experiments likely underestimate the variability prodetermine the true scanner variance and bias for T1 values duced in clinical populations due to the absence of motion through the use of a T1 phantom. The QIBA DCE-MRI T1 artifacts [5]. Few clinical DCE-MRI studies of variability have been phantom is composed of spheres containing solutions of done in the brain and more data are desperately needed. varying concentrations of nickel chloride [44]. The phantom contains two sets of spheres: one set to simulate the vascular However, practical difficulties centering on the need to do input function and the other set to represent tissue (Fig. 2.3). multiple GBCA injections in patients make such studies difThe T1 values for the vascular input spheres range between ficult to conduct. One such study was performed in 2003 by 0.75 and 41.6 s−1 while the tissue spheres range between Jackson et  al. [48] in 9 glioma patients and found that the 0.67 and 7.5 s−1. The phantom is filled with 30-mM sodium within-region of interest (ROI) coefficient of variation for chloride solution to simulate patient coil loading. To obtain mean Ktrans was 7.7% with a repeatability coefficient of T1 values, an acquisition protocol that encompasses the 21.3%. A more recent publication by Barboriak et al. in 2019 typical VFAs is used for T1 mapping. This employs a coro- [49] found that in a multicenter imaging study of recurrent nal fast spoiled gradient echo sequence with VFAs of 2, 5, glioblastoma, less variation in inter-reader tumor segmenta10, 15, 20, 25, and 30°. The use of the QIBA DCE phantom tion volumes, possibly through the use of automated tools, to determine test–retest reliability and T1 accuracy is par- may decrease variability in DCE-MRI metrics like Ktrans.

20

M. S. Shiroishi et al.

a

b

Loc: -6.963 IM: 12 Series: 2 FOV: 360 mm TR/TE/FA/NEX: 4.7/1.3/30/4

c

d

VIF

45

Tissue

12

40 10

30

Measured R1 (S1)

Measured R1 (S1)

35

25 20 15

8

6

4

10 2 5 0

0

5

10

25 30 15 20 NIST Theoretical R 1 (S-1)

35

40

45

0

0

2

4 6 8 NIST Theoretical R 1 (S-1)

10

12

Fig. 2.3 (a) The QIBA dynamic contrast-enhanced phantom layout with 32 spheres, with different concentrations of NiCl2 solutions for varying T1 relaxation rates (R1). (b) T1-weighted MR image of the phantom showing the 32 spheres and (c) R1 values of the eight-vascular input function-mimicking inserts compared with National Institute of

Standards and Technology (NIST) theoretical R1 values. (d) R1 values for the 24 tissue-mimicking inserts. (Images contributed by Edward Jackson, University of Wisconsin-Madison. Reprinted with permission from [5]

 2/T2*-Weighted Dynamic Susceptibility T Contrast MRI

Also sometimes referred to as bolus tracking MRI, a non-­ diffusible tracer, typically a GBCA, is administered and rapid images are obtained during the first-pass of the contrast agent. Several parameters are derived from DSC-MRI including relative cerebral blood flow (CBF), mean transit time (MTT), and relative cerebral blood volume (rCBV). rCBV is generally considered the most widely utilized and robust DSC-MRI perfusion metric in brain imaging.

Dynamic susceptibility contrast (DSC)-MRI has been applied to many neurological diseases, most prominently brain tumors and stroke. Compared to DCE-MRI, DSC-MRI is much more commonly used in the clinical setting for brain imaging, though the opposite is true outside of the brain.

2  Physical Principles of Dynamic Contrast-Enhanced and Dynamic Susceptibility Contrast MRI

DSC-MRI Acquisition Like DCE-MRI, DSC-MRI acquisition consists of images acquired before, during, and after administration of an intravascular contrast administration (CA).  While DCE-MRI emphasizes T1 contrast and uses short TE and TR to minimize competing T2* effects, DSC-MRI emphasizes T2* and T2 contrast. Accordingly, long TE and TR are used to minimize competing T1 effects [2]. In gradient echo-echo planar imaging (GRE-EPI DSC-­MRI), TE is usually in the range of 25–35 ms in order to optimize T2* weighting, signal-to-noise ratio (SNR), and sensitivity to T1 effects [2, 50, 51]. With regard to TR, 1.5 s or less is recommended to optimize temporal resolution given the constraints of desired slices, TE and T1 weighting [2, 50, 51]. Regardless of whether GRE or spin echo (SE) sequences are used, DSC-MRI requires very robust temporal resolution (70 cm3 had poor outcome, in spite of a 50% recanalization rate [105]. In another study analyzing 98 patients from the EPITHET trial, Parsons et  al. [103] found that while patients with an initial DWI lesion volume of 18 mL had substantially improved chances of a good outcome if treated with IV tPA, the odds of a benefit dropped rapidly at larger volumes and there was little treatment benefit with a DWI lesion volume >25 mL. Similar to the observations by Yoo et al., the authors from this study found that a large initial DWI lesion volume of >65 mL was strongly associated with a poor outcome.

Thrombolysis/Thrombectomy and DWI/FLAIR and DWI/T2W Mismatch It is globally accepted that administration of intravenous thrombolysis within 4.5 h after the onset of symptoms in patients who don’t have intracranial hemorrhage can improve outcomes in ischemic stroke [85, 106, 107]. Although there have been multiple attempts to extend this window to 6 h or greater, definitive evidence supporting extension of this time window is lacking [108]. At most centers, in the 0–6 h time period, the decision to perform mechanical thrombectomy on patients with proximal ICA or MCA occlusion is usually based on CT parameters such as a NCCT ASPECTS >5–6, CTP core 200/μ[mu]L, in patients with normal MRI scans, and at all stages of HIV-­ related neurocognitive impairment (including patients with no or minimal impairment) [191]. Cho appears elevated in HIV-positive patients, regardless of the degree of cognitive impairment [190, 197] or presence of clinical symptoms [190]. In several studies, decreased NAA levels were detected only in patients with advanced stages of dementia [190, 191, 195] and CD4 counts 1.3) and abnormal Cho/NAA ratio (>1); presumed vasogenic edema (“edema”) ROIs, with metabolite peaks lower than or similar to normal, and a normal Cho/NAA ratio (0.9 and Cho/NAA ratios >1.9 [354]. In a and brain metastases) may present with MRI features sug- retrospective study of 32 children with primary brain lesions gestive of a non-neoplastic lesion [319]. Since the spectra of (19 tumors and 13 non-neoplastic lesions), a 78% diagnostic brain tumors typically show elevated Cho and decreased accuracy was achieved based on the Cho/Cr ratio [355]. NAA, adding 1H MRS to a clinical examination may be most helpful in diagnoses of lesions with different spectroscopic patterns; e.g., strokes, focal cortical dysplasias, or brain Classification of Brain Tumors abscesses. Spectra from brain abscesses have usually decreased Cho, Cr, and NAA signals and often exhibit ele- Tumor Grading vated signals of amino acids (e.g., alanine), acetate, or suc- Metabolic information obtained with 1H MRS can help to cinate [344, 345]. Conversely, differentiation between differentiate among tumor types and tumor grades. Several tumors and entities with a similar spectroscopic pattern—for studies reported higher Cho concentrations, consistent with example, acute demyelinating lesion (typically presenting higher membrane turnover and higher cellular density, in with highly increased Cho and decreased NAA and, often, tumors of higher grades [330, 356–358]. Other studies also lactate)—may be difficult. In these cases, adding a perfusion reported lower Cho concentrations (however, with highly scan to the MRI examination may be helpful [346]. Typical elevated Cho/Cr ratios) in grade IV glioblastoma multiforme perfusion MRI, diffusion MRI, and 1H MRS features of com- (GBM) than in grade II or grade III gliomas [335]. Lower mon adult brain lesions were summarized recently [347]. Cho concentrations in GBM may be explained by the presSeveral studies applied 1H MRS to differentiate between ence of necrotic regions, which typically have low levels of tumors and non-neoplastic lesions [178, 181, 322, 348, 349] Cho, Cr, and NAA. NAA levels are higher in grade II tumors

274

compared with grade III tumors [359, 360]. Concentrations of mI are elevated in grade II astrocytomas, are within normal limits in anaplastic astrocytomas, and are reduced in GBM [335, 340]. Grade III astrocytomas have higher Cr concentrations than grade III oligoastrocytomas or oligodendrogliomas [360].

Meningiomas Meningiomas are routinely diagnosed based on conventional imaging features; the diagnosis may be confirmed by the presence of elevated alanine signal in 1H MR spectra (Fig.  10.24) [336]. However, alanine is not identified in spectra of all meningiomas [335]. Ex-vivo experiments indicated that alanine concentrations are higher in grade I meningiomas compared with grades II and III meningiomas [361]. Meningiomas were also reported to have markedly elevated Cho, high Glx levels [342], low Cr and mI concentrations, and very low NAA and lipid concentrations [335, 362]. Since meningiomas arise from arachnoid structures, NAA signal should not be present in the spectrum; the reason for the presence of NAA in the spectra of meningiomas may be contamination of the voxel by adjacent normal brain parenchyma, use of large voxels, or inadequate voxel placement [363]. Lactate signal in meningiomas may be elevated [335]. a

A. Horská et al.

Solitary Metastases Diagnosis of solitary metastases is challenging because it may be difficult to distinguish them from primary brain neoplasms on conventional MRI [363]. Metabolite concentrations in metastases are variable, and the spectra, which may contain large lipid signals [342], are difficult to differentiate from GBM [335]. However, solitary metastases may be differentiated from primary brain tumors by evaluating spectra from peri-enhancing regions. While gliomas are often invasive lesions showing elevated Cho in surrounding regions, metastatic lesions do not typically show high Cho signals or other abnormalities outside the region of enhancement [364– 366]. Peritumoral regions of gliomas also exhibit higher relative cerebral blood volume than metastases [364]. I DH Mutation and 2-HG The 2016 WHO guidelines on the classification of CNS tumors incorporate molecular markers, alongside histological features, for the definition of brain tumors [367]. In particular, mutations in genes encoding for isocitrate dehydrogenase enzymes (IDH) 1 and 2 have emerged as defining features for the subtyping of diffuse gliomas and have been associated with improved prognoses [368]. IDH mutations have been reported to occur in the majority (~80%) of low-grade gliomas and secondary glioblastomas [369–372]. At a cellular b

Cho↑↑

Cr↓

Glx

NAc↓↓↓

Ala 3.5

Fig. 10.24  Intraventricular meningioma in a 20-year-old woman. (a) Axial contrast-enhanced T1-weighted image shows a large tumor in the right trigone. The tumor is slightly heterogeneous, with moderate contrast enhancement and a nonenhancing central area (arrow). At this age, the most common intraventricular tumors are low-grade gliomas. Meningioma and anaplastic astrocytoma were included in the differential diagnosis. The voxel for 1H MRS is shown. (b) The spectrum

3

2.5

2 ppm

1.5

1

0.5

shows a typical alanine (Ala) doublet centered at 1.47 ppm, increased choline (Cho) and Glx (glutamine and glutamate), and decreased creatine (Cr) and N-acetyl-containing compounds (NAc). (Reprinted with permission from Majós C, Alonso J, Aguilera C, Serrallonga M, Coll S, Acebes JJ, et al. Utility of proton MR spectroscopy in the diagnosis of radiologically atypical intracranial meningiomas. Neuroradiology 2003;45(3):129-136; Springer)

10  Magnetic Resonance Spectroscopy: Clinical Applications

275

level, IDH1 and IDH2 are located in cytosol and mitochondria, respectively, and perform oxidative catalysis of isocitrate to α(alpha)-ketoglutarate (α-KG) within the tricarboxylic acid (TCA) cycle. Critically, mutations in genes encoding for IDH confer a gain-in-function, enabling the conversion of α-KG to the metabolite 2-­hydroxyglutarate (2HG), which subsequently accumulates in millimolar concentrations, far higher than present in healthy tissue [373]. The accumulation of 2-HG in tumor cells may suggest a role as an oncometabolite in gliomagenesis [374]. Critically for 1H-MRS, 2-HG is an MR-visible metabolite [375], which has opened the possibility to noninvasively determine IDH-status in glioma. Several 1H MRS techniques have been presented for the in vivo detection of 2-HG in IDH-mutated gliomas. In a prospective study, using a short-TE PRESS sequence (TE of 30 ms), 2-HG was detected in 9 out of 9 IDH-mutated gliomas and in 4 out of 15 wild-type gliomas [376]. Further short-TE studies showed a similar ability to detect 2-HG in IDH-­mutant gliomas [377, 378], however, reported high false-­positive detections in IDH wild-type tumors owing to the large spectral overlap between 2-HG, Glu, Gln, and GABA, which is exacerbated by an unrelaxed macromolecular baseline. A study using a shortTE PRESS sequence (35 ms) combined measures of 2-HG and Glu (2-HG+Glu) and reported an improvement in specificity of detection from 72 to 96% [379]. To overcome detection difficulties arising from spectral overlap, J-difference editing [380, 381] and 2-dimensional correlation spectroscopy (2D-COSY) [380, 382] have been proposed, resulting in the unambiguous a

assignment of 2-HG.  However, such techniques require the nonstandard acquisition and can suffer from long scan times. Long echo time approaches have been used to avoid macromolecular contributions and have been tailored to specifically detect 2-HG. Using a long-TE PRESS sequence, with an echo time optimized for 2-HG detection (TE of 97 ms), 2-HG was shown to be detectable in 15/15 IDH mutant gliomas and undetectable in 15/15 wild-type tumors [383]. The 97 ms PRESS method was reported to lead to higher detection accuracy of 2-HG compared to a short TE approach, owing to the improved separation of 2-HG among overlapping Glu and Gln peaks (in particular at 2.25 ppm) [384]. When carried out as part of a standard glioma clinical imaging protocol, one study reported a sensitivity of 89.5% and a specificity of 81.3% across 35 patients [385] and higher specificities were reported using optimal cutoff values for 2-HG [386]. In this study by Zhou et al., it was also showed that single-voxel MRS (SVS) has a better diagnostic performance for untreated IDH-mutant gliomas, whereas chemical shift imaging MRS (CSI) demonstrates greater performance in identifying recurrent tumors. Additionally, using this method, a decrease in sensitivity for 2-HG detection was reported for small tumor volumes, in particular (2.5, was strongly associated with shorter survival (P < 0.0001) (Fig. 10.26).

Aside from 2-HG and glycine detection in IDH-mutated tumors, elevated cystathionine has been reported using 3 T 1HMRS and associated with the presence of 1p/19q co-­deletion, which is a further prognostic molecular marker in glioma [395].

Post-Treatment Evaluation Radiation necrosis is usually identified in 3–24% of glioma patients receiving adjuvant radiotherapy. Areas of radiation necrosis typically have a T2-hyperintense signal and T1-­enhancement after contrast administration that is difficult to distinguish from tumor progression [396]. 1H MRS has been applied to differentiate between radiation-

10  Magnetic Resonance Spectroscopy: Clinical Applications

a

277

b

Fig. 10.27  Evolution of radiation necrosis in 49-year-old man after radical resection for grade II oligodendroglioma. 1H MR spectra were obtained at 12 months (a), and 20 months (b) after radiation therapy. Initially, an increase in choline (Cho)/creatine (Cr) and Cho/N-acetylaspartate (NAA) ratios on MRS may reflect inflammatory cells infiltrating in early necrosis (a). At the 20-month follow-up (b), 1H

MRS shows a decrease in signal intensities of all metabolites and presence of high lipid/lactate signal, typical of radiation necrosis. (Reprinted with permission from Pružincová Ľ, Šteňo J, Srbecký M, Kalina P, Rychlý B, Boljesíková E, et al. MR imaging of late radiation therapyand chemotherapy-induced injury: a pictorial essay. European Radiology 2009; 19(11): 2716-2727; Springer)

induced tissue injury and tumor recurrence in both adult and pediatric brain tumor patients postradiation, after gamma knife radiosurgery, and brachytherapy. Reduced levels of Cho, Cr, and NAA are suggestive of radiation necrosis, while increased levels of Cho signal (measured relative to Cho signal in normal-­appearing tissue, Cho/Cr or Cho/NAA ratios) are suggestive of tumor recurrence [359, 397–399]. Necrotic regions may also show elevated lipid and lactate signals [400, 401]. Comparisons with biopsy specimens revealed that it may be difficult to reliably differentiate between tissue containing mixed tumor/ radiation necrosis and either tumor or radiation necrosis [400]. 1H MR spectra in a patient with oligodendroglioma treated with surgery and radiation therapy are shown in Fig. 10.27 [402].

tral nervous system. 1H MRS can assist with the diagnosis of a variety of disorders, help to monitor effects of therapy, and evaluate disease progression, and assess prognosis (see Table  10.1 for summary of key studies [44, 65, 85, 103, 214, 249, 301, 308, 317, 335, 383, 403–405]). All manufacturers of clinical MR scanners offer integrated MRS packages for the acquisition and display of spectra, and software for semiquantitative analysis. The quality of spectra and the diagnostic utility of the spectroscopic examination depend both on the MRI system and the skills and experience of the operator and clinical spectroscopist interpreting the data. Standardization of in-vivo MRS techniques, development of automated methods for data analysis, and availability of databases (including data on healthy brain and different pathologies) may help to advance the integration of MRS in routine clinical diagnostic practice in the future [406, 407].

Conclusion By providing unique information on the chemical composition of the brain tissue, 1H MRS may help to improve our understanding of the mechanism of the diseases of the cen-

A. Horská et al.

278 Table 10.1  1H MRS clinical applications: summary of key studies Application Epilepsy

Authors Ende et al., 1997 [249] Temporal lobe epilepsy: bilateral hippocampal metabolite changes revealed at proton MR spectroscopic imaging. Radiology 1997;202(3):809–817

Subjects 16 adults (mean age 35.9 years) with unilateral temporal lobe seizure focus on EEG, 16 age-matched controls

Technique 1.5T, MRSI with PRESS volume pre-selection (TE/TR = 1.8 s/135 ms)

Wardlaw et al., 1998 [85] Studies of acute ischemic stroke with proton magnetic resonance spectroscopy: relation between time from onset, neurological deficit, metabolite abnormalities in the infarct, blood flow, and clinical outcome. Stroke 1998;29(8):1618–1624 Brain development Pouwels et al., 1999 [44] Regional age dependence of human brain metabolites from infancy to adulthood as detected by quantitative localized proton MRS. Pediatr Res 1999;46(4):474–485 Neurodegenerative Kantarci et al., 2000 [103] diseases Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: A 1H MRS study. Neurology 2000;55(2):210–217

50 patients admitted within 3 days of an acute stroke, with symptoms of a medium-to-large cortical hemispheric infarct

1.5 T, serial single voxel 1H MRS and MRSI (TR/TE = 1.6 s/135 ms)

97 children (neuropediatric patients), 72 healthy young adults

2.0 T, single-voxel 1H MRS (STEAM: TR/TE = 6 s/20 ms; TM = 30 or 10 ms)

21 patients with mild cognitive impairment (MCI), 21 patients with Alzheimer’s disease (AD), 65 healthy elderly

1.5T, single-voxel MRS (TR/ TE = 2 s, 135 and 30 ms); posterior cingulate, medial occipital lobe, and superior temporal lobe were examined

Stroke

Hepatic encephalopathy

Multiple sclerosis

Mitochondrial disorders

Results Value of 1H MRS for presurgical lateralization of seizure focus in temporal lobe epilepsy was demonstrated. Compared to controls, NAA/(Cr+Cho) ratio in patients (with and without hippocampal atrophy) was decreased by 27%. In eight patients, low NAA levels were also detected in the contralateral hippocampus Reduced NAA and elevated lactate within the first 4 days were detected. Presence of lactate was associated with extensive infarction. In some patients, a trend to NAA “recovery” was detected at the follow-up examinations

Regional and age-related differences in 1H MR spectra in the gray matter, white matter, basal ganglia, thalamus, and the cerebellum are shown

The mI/Cr ratio in the posterior cingulate was elevated in MCI and AD patients compared with controls. NAA/Cr (in the temporal lobe and posterior cingulate) was lower in AD patients compared with both MCI and controls. The data suggest that elevation in mI/Cr ratio may represent pathologic progression to AD Naegele et al., 2000 [317] 22 patients (age range: 1.5 T, single-voxel 1H MRS of Decreased mI/Cr and Cho/Cr ratios and MR imaging and 1H 22–57 years) scheduled for the medial occipital lobe elevated Glx/Cr ratios were found in 21 spectroscopy of brain liver transplantation; 8 (STEAM, TR/TE/TM = 1.5 s/5 patients. Of the eight patients with the metabolites in hepatic patients also had a follow-up ms/5 ms) follow-up MRS scan, 3–7 months after encephalopathy: time-course scan after the transplantation liver transplantation, the mI/Cr and Glx/Cr of renormalization after liver ratios were within the normal range in five transplantation. Radiology and eight patients, respectively, without 2000;216(3):683–691 any residual signs of subclinical or overt hepatic encephalopathy Filippi et al., 2003 [214] 31 patients (mean age 28.7 1.5T, whole-brain NAA WBNAA concentration in patients at the Evidence for widespread years) with clinically (WBNAA) technique described earliest clinical stage of multiple sclerosis axonal damage at the earliest isolated syndromes in Gonen O, Catalaa I, Babb was lower by 22% compared with clinical stage of multiple suggestive of multiple JS, et al. Total brain controls. No difference between patients sclerosis. Brain 2003;126(Pt sclerosis N-acetylaspartate. A new with and without enhancing lesions on the 2):433–437 measure of disease load in baseline MRI examination was detected MS. Neurology 2000;54:15–19 Lin et al., 2003 [301] 29 patients with suspected 1.5 T, single voxel 1H MRS Of eight patients with a diagnosis of Proton MR spectroscopy in the mitochondrial disease (age (TR = 2 s, TR = 144, 280 or 30 mitochondrial disorder on the basis of diagnostic evaluation of range 0.5–39 years) ms); 1H MRSI (TR/TE = 2.3 genetic, biochemical, or pathologic suspected mitochondrial s/280 ms) features, five patients had elevated brain or disease. AJNR Am J CSF lactate signal. Of 16 patients who Neuroradiol 2003;24(1):33–41 were highly suspected of having mitochondrial disorders on the basis of clinical data only, four patients had abnormal lactate levels. Mitochondrial disorder was excluded for five patients; no lactate was detected in this group

10  Magnetic Resonance Spectroscopy: Clinical Applications

279

Table 10.1 (continued) Application Brain tumors

Hypoxic-ischemic encephalopathy

Authors Howe et al., 2003 [335] Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 2003;49(2):223–232

Subjects 42 patients diagnosed with brain tumors; no previous surgery or treatment (other than dexamethasone) were applied; eight healthy controls

Technique 1.5T, single-voxel 1H MRS (STEAM: TR/TE = 2 s/30 ms; PRESS: TR/TE = 2 s/136 ms). Spectra were acquired from normal-appearing white matter (NAWM), meningiomas, grade II astrocytomas, anaplastic astrocytomas, glioblastomas, and metastases

Results Meningiomas had a very low mI and Cr concentrations. Grade II astrocytomas had high mI concentrations. Tumor Cho concentration was higher than in NAWM and increased with grade for grade II and anaplastic astrocytomas. Cho concentration was highly variable for glioblastomas. Higher Cho and Cr were correlated with low lipid and lactate, indicating low metabolite concentrations in necrotic areas of high-grade tumors Choi et al., 2012. [383] 30 patients diagnosed with 3.0 T, single-voxel MRS Tumors with IDH1 or IDH2 mutations by 2-hydroxyglutarate detection brain tumors, 15 (PRESS, TE 97 ms) was shown DNA sequencing were all found to have by magnetic resonance glioblastoma, 15 grade 2 or to be optimal for detecting detectable 2HG levels using MRS spectroscopy in subjects with 3 astrocytoma or 2HG (concentration range 1.7–8.9 mM); IDH-mutated gliomas. Nature oligodendroglioma whereas 2HG was undetectable in Medicine 2012;18(4):624–629 wild-type tumors Zhou et al., 2018 [386] 85 patients:39 postop The optimal 2HG/creatine 2HG MRS provides diagnostic utility for Neuro-Oncology. 20(9), residual or recurrent thresholds of SVS and 75th IDH-mutant gliomas both preoperatively 1262–1271, 2018 IDH-mutant glioma c/w 6 percentile CSI for IDH and at the time of suspected tumor normals; 24 preop validation mutations were 0.11 and 0.23, recurrence. SVS has a better diagnostic cohort;16 recurrent lesion respectively. Validation sets, performance for untreated IDH-mutant validation cohort the sen., spec., and accuracy in gliomas, whereas CSI demonstrates distinguishing IDH mutant greater performance in identifying gliomas in the preoperative recurrent tumors cohort were 85.71%, 100.00%, and 94.12% for SVS, and 100.00%, 69.23%, and 81.82% for CSI, respectively. In recurrent-lesion cohort, the sen., spec., and accuracy for discriminating IDH-positive recurrent gliomas were 40.00%, 62.50%, and 53.85% for SVS, and 66.67%, 100.00%, and 86.67% for CSI, respectively The data suggest that aggressive gliomas Tiwari et al. 2020 [394] 35 glioma patients using an Elevated glycine was strongly reprogram glycine-mediated one-carbon Neuro-Oncology MRS sequence tailored for associated with presence of metabolism to meet the biosynthetic 22(7), 1018–1029, 2020 co-detection of glycine and gadolinium enhancement, demands for rapid cell proliferation. MRS 2HG in gadoliniumindicating more rapidly evaluation of glycine provides a enhancing and proliferative disease. Glycine noninvasive metabolic imaging biomarker nonenhancing tumor regions concentration was positively that is predictive of tumor progression and on 3T MRI correlated with MIB-1, and clinical outcome levels higher than 2.5 mM showed a significant association with shorter patient survival, irrespective of IDH status. Concentration of 2HG did not correlate with MIB-1 index. A high glycine/2HG concentration ratio, >2.5, was strongly associated with shorter survival (P < 0.0001) Hangel et al., 2020. [403] 23 patients with high-grade High resolution (up to 3.4 mm This paper showed that multi-metabolites High-resolution metabolic glioma isotropic) MRSI data was can be mapped in human brain tumors at 7 imaging of high-grade gliomas recorded with extended T. In addition to Cho, Cr and NAA, using 7T-CRT-FIDcoverage at 7 T in 15 min scan compounds such as glutamine, 2HG and MRSI. Neuroimage Clin time glycine were also reported to be abnormal 2020;28:102433 Metabolite ratios tended to worsen initially Barkovich et al., 2006 [65] 10 neonates with overt 1.5T, serial single voxel 1H MRS (PRESS TR/TE = 2 s/288 until approximately the fifth day of life MR imaging, MR neonatal encephalopathy ms) and MRSI; DTI was also (NAA/Cho decreased, Cr/NAA increased), spectroscopy, and diffusion performed and then tended to normalize. Spectra of tensor imaging of sequential hypoxic-ischemic regions showed elevated studies in neonates with lactate encephalopathy. AJNR Am J Neuroradiol 2006;27(3):533–547

(continued)

A. Horská et al.

280 Table 10.1 (continued) Application Childhood white matter disorders

Authors Bizzi et al., 2008 [308] Classification of childhood white matter disorders using proton MR spectroscopic imaging. AJNR Am J Neuroradiol 2008;29(7):1270–1275

Subjects 70 children (age range 0.7–17 years) with a diagnosis of leukoencephalopathy

Technique 1.5 T, 1H MRSI (TR/TE = 1.5 s/135 ms)

General clinical applications of MRS

Oz et al., 2014. [405] Clinical Proton MR spectroscopy in central nervous system disorders. Radiology 2014;270(3):658–679 Wilson et al., 2019. [404] Methodological consensus on clinical proton MRS of the brain: Review and recommendations. Magn Reson Med 2019;82(2):527–550

A review article that summarizes the main clinical applications of MRS in the brain

Mainly short TE single voxel spectroscopy at 1.5 or 3.0 T. MR spectroscopic imaging for multiple and/or heterogeneous lesions

A review article that summarizes the technical requirements for MRS in the human brain

Recommends use of semi-LASER (sLASER) localization methods for single-voxel MRS with minimal spatial distortions at 3T. Several choices are available for MRSI.

Technical developments

Results In hypomyelinating disorders, Cho/NAA, Cho/Cr, and NAA/Cr ratios were within the normal range for healthy children. In leukoencephalopathies with white matter rarefaction, the Cho/NAA, Cho/Cr, and NAA/Cr ratios were approximately 1.0. In demyelinating diseases, Cho/NAA and Cho/Cr ratios were elevated while NAA/ Cr was reduced Describes diagnostic applications of MRS to brain lesions, neonatal, and pediatric disorders

In addition to localization methods, this paper discusses other requirements for MRS including quality control measures, and post-processing software packages. Recommended acquisition parameters are also suggested.

H hydrogen-1 isotope, 2HG 2-hydroxyglutarate, DTI diffusion tensor imaging, EEG electroencephalogram, MRS magnetic resonance spectroscopy, MRSI magnetic resonance spectroscopic imaging, NAA N-acetyl aspartate, PRESS Point RESolved Spectroscopy, sLASER semi-Localization by Adiabatic SElective Refocusing, STEAM STimulated Echo Acquisition Mode, TM mixing time, TR/TE repetition time/echo time 1

References 1. Tran T, Ross B, Lin A. Magnetic resonance spectroscopy in neurological diagnosis. Neurol Clin. 2009;27(1):21–60. 2. van der Graaf M.  In vivo magnetic resonance spectroscopy: basic methodology and clinical applications. Eur Biophys J. 2010;39(4):527–40. 3. Keevil SF.  Spatial localization in nuclear magnetic resonance spectroscopy. Phys Med Biol. 2006;51(16):579–636. 4. Frahm J, Merboldt KD, Hanicke W.  Localized proton spectroscopy using stimulated echoes. J Magn Reson. 1987;72(3): 502–8. 5. Bottomley PA. Spatial localization in NMR spectroscopy in vivo. Ann N Y Acad Sci. 1987;508:333–48. 6. Oz G, Tkac I. Short-echo, single-shot, full-intensity proton magnetic resonance spectroscopy for neurochemical profiling at 4 T: validation in the cerebellum and brainstem. Magn Reson Med. 2011;65(4):901–10. 7. Scheenen TW, Klomp DW, Wijnen JP, Heerschap A. Short echo time 1H-MRSI of the human brain at 3T with minimal chemical shift displacement errors using adiabatic refocusing pulses. Magn Reson Med. 2008;59(1):1–6. 8. Kaiser LG, Young K, Matson GB.  Numerical simulations of localized high field 1H MR spectroscopy. J Magn Reson. 2008;195(1):67–75. 9. Klose U.  Measurement sequences for single voxel proton MR spectroscopy. Eur J Radiol. 2008;67(2):194–201. 10. Garwood M, DelaBarre L.  The return of the frequency sweep: designing adiabatic pulses for contemporary NMR.  J Magn Reson. 2001;153(2):155–77. 11. Haase A, Frahm J, Hanicke W, Matthaei D. 1H NMR chemical shift selective (CHESS) imaging. Phys Med Biol. 1985;30(4):341–4. 12. Ogg RJ, Kingsley PB, Taylor JS. WET, a T1- and B1-insensitive water-suppression method for in  vivo localized 1H NMR spectroscopy. J Magn Reson B. 1994;104(1):1–10.

13. Tkac I, Gruetter R. Methodology of H NMR spectroscopy of the human brain at very high magnetic fields. Appl Magn Reson. 2005;29(1):139–57. 14. Tkac I, Starcuk Z, Choi IY, Gruetter R. In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time. Magn Reson Med. 1999;41(4):649–56. 15. Brown TR, Kincaid BM, Ugurbil K.  NMR chemical shift imaging in three dimensions. Proc Natl Acad Sci U S A. 1982;79(11):3523–6. 16. Zierhut ML, Ozturk-Isik E, Chen AP, Park I, Vigneron DB, Nelson SJ. (1)H spectroscopic imaging of human brain at 3 Tesla: comparison of fast three-dimensional magnetic resonance spectroscopic imaging techniques. J Magn Reson Imaging. 2009;30(3):473–80. 17. Maudsley AA, Andronesi OC, Barker PB, Bizzi A, Bogner W, Henning A, Nelson SJ, Posse S, Shungu DC, Soher BJ. Advanced magnetic resonance spectroscopic neuroimaging: experts’ consensus recommendations. NMR Biomed. 2020;2020:e4309. 18. Scheenen TW, Heerschap A, Klomp DW. Towards 1H-MRSI of the human brain at 7T with slice-selective adiabatic refocusing pulses. MAGMA. 2008;21(1-2):95–101. 19. Wijnen JP, van Asten JJ, Klomp DW, Sjobakk TE, Gribbestad IS, Scheenen TW, Heerschap A.  Short echo time 1H MRSI of the human brain at 3T with adiabatic slice-selective refocusing pulses; reproducibility and variance in a dual center setting. J Magn Reson Imaging. 2010;31(1):61–70. 20. Maudsley AA, Domenig C, Govind V, Darkazanli A, Studholme C, Arheart K, Bloomer C. Mapping of brain metabolite distributions by volumetric proton MR spectroscopic imaging (MRSI). Magn Reson Med. 2009;61(3):548–59. 21. Tkac I, Oz G, Adriany G, Ugurbil K, Gruetter R.  In vivo 1H NMR spectroscopy of the human brain at high magnetic fields: metabolite quantification at 4T vs. 7T.  Magn Reson Med. 2009;62(4):868–79. 22. Hetherington HP, Chu WJ, Gonen O, Pan JW. Robust fully automated shimming of the human brain for high-field 1H spectroscopic imaging. Magn Reson Med. 2006;56(1):26–33.

10  Magnetic Resonance Spectroscopy: Clinical Applications 23. Gruetter R.  Automatic, localized in  vivo adjustment of all first- and second-order shim coils. Magn Reson Med. 1993;29(6):804–11. 24. Gruetter R, Tkac I.  Field mapping without reference scan using asymmetric echo-planar techniques. Magn Reson Med. 2000;43(2):319–23. 25. Emir UE, Burns B, Chiew M, Jezzard P, Thomas MA. Non-water-­ suppressed short-echo-time magnetic resonance spectroscopic imaging using a concentric ring k-space trajectory. NMR Biomed. 2017;30(7):3714. 26. van Der Veen JW, Weinberger DR, Tedeschi G, Frank JA, Duyn JH. Proton MR spectroscopic imaging without water suppression. Radiology. 2000;217(1):296–300. 27. Cudalbu C, Cavassila S, Rabeson H, van Ormondt D, Graveron-­ Demilly D.  Influence of measured and simulated basis sets on metabolite concentration estimates. NMR Biomed. 2008;21(6):627–36. 28. Naressi A, Couturier C, Castang I, de Beer R, Graveron-Demilly D. Java-based graphical user interface for MRUI, a software package for quantitation of in vivo/medical magnetic resonance spectroscopy signals. Comput Biol Med. 2001;31(4):269–86. 29. Provencher SW.  Estimation of metabolite concentrations from localized in  vivo proton NMR spectra. Magn Reson Med. 1993;30(6):672–9. 30. Provencher SW.  Automatic quantitation of localized in  vivo 1H spectra with LCModel. NMR Biomed. 2001;14(4):260–4. 31. Naressi A, Couturier C, Devos JM, Janssen M, Mangeat C, de Beer R, Graveron-Demilly D.  Java-based graphical user interface for the MRUI quantitation package. MAGMA. 2001;12(2-3):141–52. 32. Wilson M, Reynolds G, Kauppinen RA, Arvanitis TN, Peet AC. A constrained least-squares approach to the automated quantitation of in vivo (1)H magnetic resonance spectroscopy data. Magn Reson Med. 2011;65(1):1–12. 33. Mekle R, Mlynarik V, Gambarota G, Hergt M, Krueger G, Gruetter R. MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T. Magn Reson Med. 2009;61(6):1279–85. 34. Pfeuffer J, Tkac I, Provencher SW, Gruetter R. Toward an in vivo neurochemical profile: quantification of 18 metabolites in short-­ echo-­time (1)H NMR spectra of the rat brain. J Magn Reson. 1999;141(1):104–20. 35. Gasparovic C, Song T, Devier D, Bockholt HJ, Caprihan A, Mullins PG, Posse S, Jung RE, Morrison LA. Use of tissue water as a concentration reference for proton spectroscopic imaging. Magn Reson Med. 2006;55(6):1219–26. 36. Brandt AS, Unschuld PG, Pradhan S, Lim IA, Churchill G, Harris AD, Hua J, Barker PB, Ross CA, van Zijl PC, Edden RA, Margolis RL.  Age-related changes in anterior cingulate cortex glutamate in schizophrenia: a (1)H MRS Study at 7 Tesla. Schizophr Res. 2016;172(1-3):101–5. 37. Mohamed M, Barker PB, Skolasky RL, Sacktor N. 7T Brain MRS in HIV infection: correlation with cognitive impairment and performance on neuropsychological tests. AJNR Am J Neuroradiol. 2018;39(4):704–12. 38. Oeltzschner G, Wijtenburg SA, Mikkelsen M, Edden RAE, Barker PB, Joo JH, Leoutsakos JS, Rowland LM, Workman CI, Smith GS. Neurometabolites and associations with cognitive deficits in mild cognitive impairment: a magnetic resonance spectroscopy study at 7 Tesla. Neurobiol Aging. 2019;73:211–8. 39. Unschuld PG, Edden RA, Carass A, Liu X, Shanahan M, Wang X, Oishi K, Brandt J, Bassett SS, Redgrave GW, Margolis RL, van Zijl PC, Barker PB, Ross CA.  Brain metabolite alterations and cognitive dysfunction in early Huntington’s disease. Mov Disord. 2012;27(7):895–902.

281 40. Wang AM, Pradhan S, Coughlin JM, Trivedi A, DuBois SL, Crawford JL, Sedlak TW, Nucifora FC Jr, Nestadt G, Nucifora LG, Schretlen DJ, Sawa A, Barker PB. Assessing brain metabolism with 7-T proton magnetic resonance spectroscopy in patients with first-episode psychosis. JAMA Psychiat. 2019;76(3):314–23. 41. van der Knaap MS, van der Grond J, van Rijen PC, Faber JAJ, Valk J, Willemse K.  Age-dependent changes in  localized proton and phosphorus MR spectroscopy of the brain. Radiology. 1990;176:509–15. 42. Huppi PS, Posse S, Lazeyras F, Burri R, Bossi E, Herschkowitz N.  Magnetic resonance in preterm and term newborns: 1H-spectrscopy in developing brain. Pediatr Res. 1991;30:574–8. 43. Kreis R, Ernst T, Ross BD.  Development of the human brain: in  vivo quantification of metabolite and water content with proton magnetic resonance spectroscopy. Magn Reson Med. 1993;30(4):424–37. 44. Pouwels PJ, Brockmann K, Kruse B, Wilken B, Wick M, Hanefeld F, Frahm J. Regional age dependence of human brain metabolites from infancy to adulthood as detected by quantitative localized proton MRS. Pediatr Res. 1999;46(4):474–85. 45. Penrice J, Cady EB, Lorek A, Wylezinska M, Amess PN, Aldridge RF, Stewart A, Wyatt JS, Reynolds EO. Proton magnetic resonance spectroscopy of the brain in normal preterm and term infants, and early changes after perinatal hypoxia-ischemia. Pediatr Res. 1996;40(1):6–14. 46. Robertson NJ, Kuint J, Counsell TJ, Rutherford TA, Coutts A, Cox IJ, Edwards AD.  Characterization of cerebral white matter damage in preterm infants using 1H and 31P magnetic resonance spectroscopy. J Cereb Blood Flow Metab. 2000;20(10):1446–56. 47. Horska A, Kaufmann WE, Brant LJ, Naidu S, Harris JC, Barker PB.  In vivo quantitative proton MRSI study of brain development from childhood to adolescence. J Magn Reson Imaging. 2002;15(2):137–43. 48. Panigrahy A, Nelson MD, Blüml S. Magnetic resonance spectroscopy in pediatric neuroradiology: clinical and research applications. Pediatr Radiol. 2009;40(1):3–30. 49. Haga KK, Khor YP, Farrall A, Wardlaw JM.  A systematic review of brain metabolite changes, measured with 1H magnetic resonance spectroscopy, in healthy aging. Neurobiol Aging. 2009;30(3):353–63. 50. Kadota T, Horinouchi T, Kuroda C. Development and aging of the cerebrum: assessment with proton MR spectroscopy. AJNR Am J Neuroradiol. 2001;22(1):128–35. 51. Ando K, Ishikura R, Morikawa T, Tominaga S, Takayasu Y, Miura K, Nakao N, Minagawa K, Tanizawa T, Takemura Y, Ikeda J.  Regional differences of in  vivo proton MR spectroscopy in developing human brain. Nippon Igaku Hoshasen Gakkai Zasshi. 2000;60(4):199–204. 52. Vigneron DB, Barkovich AJ, Noworolski SM, von dem Bussche M, Henry RG, Lu Y, Partridge JC, Gregory G, Ferriero DM. Three-­ dimensional proton MR spectroscopic imaging of premature and term neonates. AJNR Am J Neuroradiol. 2001;22(7):1424–33. 53. Girard N, Confort-Gouny S, Schneider J, Barberet M, Chapon F, Viola A, Pineau S, Combaz X, Cozzone P. MR imaging of brain maturation. J Neuroradiol. 2007;34(5):290–310. 54. Arslanoglu A, Bonekamp D, Barker PB, Horska A. Quantitative proton MR spectroscopic imaging of the mesial temporal lobe. J Magn Reson Imaging. 2004;20(5):772–8. 55. Degaonkar MN, Pomper MG, Barker PB.  Quantitative proton magnetic resonance spectroscopic imaging: regional variations in the corpus callosum and cortical gray matter. J Magn Reson Imaging. 2005;22(2):175–9. 56. Barker PB, Szopinski K, Horska A.  Metabolic heterogeneity at the level of the anterior and posterior commissures. Magn Reson Med. 2000;43(3):348–54.

282 57. McLean MA, Woermann FG, Simister RJ, Barker GJ, Duncan JS.  In vivo short echo time 1H-magnetic resonance spectroscopic imaging (MRSI) of the temporal lobes. NeuroImage. 2001;14(2):501–9. 58. Jacobs MA, Horska A, van Zijl PC, Barker PB. Quantitative proton MR spectroscopic imaging of normal human cerebellum and brain stem. Magn Reson Med. 2001;46(4):699–705. 59. Pouwels PJ, Frahm J.  Regional metabolite concentrations in human brain as determined by quantitative localized proton MRS. Magn Reson Med. 1998;39(1):53–60. 60. Baker EH, Basso G, Barker PB, Smith MA, Bonekamp D, Horska A.  Regional apparent metabolite concentrations in young adult brain measured by (1)H MR spectroscopy at 3 Tesla. J Magn Reson Imaging. 2008;27(3):489–99. 61. Nagae-Poetscher LM, Bonekamp D, Barker PB, Brant LJ, Kaufmann WE, Horska A. Asymmetry and gender effect in functionally lateralized cortical regions: a proton MRS imaging study. J Magn Reson Imaging. 2004;19(1):27–33. 62. Perlman JM.  Summary proceedings from the neurology group on hypoxic-ischemic encephalopathy. Pediatrics. 2006;117(3 Pt 2):S28–33. 63. Miller SP. Newborn brain injury: looking back to the fetus. Ann Neurol. 2007;61(4):285–7. 64. Chao CP, Zaleski CG, Patton AC.  Neonatal hypoxic-ischemic encephalopathy: multimodality imaging findings. Radiographics. 2006;26(Suppl 1):S159–72. 65. Barkovich AJ, Miller SP, Bartha A, Newton N, Hamrick SE, Mukherjee P, Glenn OA, Xu D, Partridge JC, Ferriero DM, Vigneron DB. MR imaging, MR spectroscopy, and diffusion tensor imaging of sequential studies in neonates with encephalopathy. AJNR Am J Neuroradiol. 2006;27(3):533–47. 66. Barkovich AJ, Baranski K, Vigneron D, Partridge JC, Hallam DK, Hajnal BL, Ferriero DM.  Proton MR spectroscopy for the ­evaluation of brain injury in asphyxiated, term neonates. AJNR Am J Neuroradiol. 1999;20(8):1399–405. 67. Cady EB. Magnetic resonance spectroscopy in neonatal hypoxic-­ ischaemic insults. Childs Nerv Syst. 2001;17(3):145–9. 68. Pu Y, Li QF, Zeng CM, Gao J, Qi J, Luo DX, Mahankali S, Fox PT, Gao JH. Increased detectability of alpha brain glutamate/glutamine in neonatal hypoxic-ischemic encephalopathy. AJNR Am J Neuroradiol. 2000;21(1):203–12. 69. Malik GK, Pandey M, Kumar R, Chawla S, Rathi B, Gupta RK.  MR imaging and in  vivo proton spectroscopy of the brain in neonates with hypoxic ischemic encephalopathy. Eur J Radiol. 2002;43(1):6–13. 70. Mathur AM, McKinstry RC.  Imaging of hypoxic-ischemic encephalopathy in the full-term neonate. In: Medina LS, Applegate KE, Blackmore CC, editors. Evidence-based imaging in pediatrics. New York: Springer; 2010. p. 71–83. 71. Amess PN, Penrice J, Wylezinska M, Lorek A, Townsend J, Wyatt JS, Amiel-Tison C, Cady EB, Stewart A. Early brain proton magnetic resonance spectroscopy and neonatal neurology related to neurodevelopmental outcome at 1 year in term infants after presumed hypoxic-ischaemic brain injury. Dev Med Child Neurol. 1999;41(7):436–45. 72. Cheong JL, Cady EB, Penrice J, Wyatt JS, Cox IJ, Robertson NJ. Proton MR spectroscopy in neonates with perinatal cerebral hypoxic-ischemic injury: metabolite peak-area ratios, relaxation times, and absolute concentrations. AJNR Am J Neuroradiol. 2006;27(7):1546–54. 73. Shanmugalingam S, Thornton JS, Iwata O, Bainbridge A, O’Brien FE, Priest AN, Ordidge RJ, Cady EB, Wyatt JS, Robertson NJ. Comparative prognostic utilities of early quantitative magnetic resonance imaging spin-spin relaxometry and proton magnetic resonance spectroscopy in neonatal encephalopathy. Pediatrics. 2006;118(4):1467–77.

A. Horská et al. 74. Ledezma CJ, Fiebach JB, Wintermark M. Modern imaging of the infarct core and the ischemic penumbra in acute stroke patients: CT versus MRI. Expert Rev Cardiovasc Ther. 2009;7(4):395–403. 75. Butcher K, Emery D. Acute stroke imaging. Part I: fundamentals. Can J Neurol Sci. 2010;37(1):4–16. 76. Wardlaw JM.  Neuroimaging in acute ischaemic stroke: insights into unanswered questions of pathophysiology. J Intern Med. 2010;267(2):172–90. 77. Gideon P, Sperling B, Arlien-Soborg P, Olsen TS, Henriksen O. Long-term follow-up of cerebral infarction patients with proton magnetic resonance spectroscopy. Stroke. 1994;25(5):967–73. 78. Saunders DE, Howe FA, van den Boogaart A, McLean MA, Griffiths JR, Brown MM.  Continuing ischemic damage after acute middle cerebral artery infarction in humans demonstrated by short-echo proton spectroscopy. Stroke. 1995;26(6):1007–13. 79. Barker PB, Gillard JH, van Zijl PC, Soher BJ, Hanley DF, Agildere AM, Oppenheimer SM, Bryan RN. Acute stroke: evaluation with serial proton MR spectroscopic imaging. Radiology. 1994;192(3):723–32. 80. Felber SR, Aichner FT, Sauter R, Gerstenbrand F. Combined magnetic resonance imaging and proton magnetic resonance spectroscopy of patients with acute stroke. Stroke. 1992;23(8):1106–10. 81. Lanfermann H, Kugel H, Heindel W, Herholz K, Heiss WD, Lackner K.  Metabolic changes in acute and subacute cerebral infarctions: findings at proton MR spectroscopic imaging. Radiology. 1995;196(1):203–10. 82. Ricci PE Jr. Proton MR spectroscopy in ischemic stroke and other vascular disorders. Neuroimaging Clin N Am. 1998;8(4):881–900. 83. Gillard JH, Barker PB, van Zijl PC, Bryan RN, Oppenheimer SM.  Proton MR spectroscopy in acute middle cerebral artery stroke. AJNR Am J Neuroradiol. 1996;17(5):873–86. 84. Munoz Maniega S, Cvoro V, Chappell FM, Armitage PA, Marshall I, Bastin ME, Wardlaw JM. Changes in NAA and lactate following ischemic stroke: a serial MR spectroscopic imaging study. Neurology. 2008;71(24):1993–9. 85. Wardlaw JM, Marshall I, Wild J, Dennis MS, Cannon J, Lewis SC. Studies of acute ischemic stroke with proton magnetic resonance spectroscopy: relation between time from onset, neurological deficit, metabolite abnormalities in the infarct, blood flow, and clinical outcome. Stroke. 1998;29(8):1618–24. 86. Cvoro V, Wardlaw JM, Marshall I, Armitage PA, Rivers CS, Bastin ME, Carpenter TK, Wartolowska K, Farrall AJ, Dennis MS.  Associations between diffusion and perfusion parameters, N-acetyl aspartate, and lactate in acute ischemic stroke. Stroke. 2009;40(3):767–72. 87. Cvoro V, Marshall I, Armitage PA, Bastin ME, Carpenter T, Rivers CS, Dennis MS, Wardlaw JM. MR diffusion and perfusion parameters: relationship to metabolites in acute ischaemic stroke. J Neurol Neurosurg Psychiatry. 2010;81(2):185–91. 88. Nicoli F, Lefur Y, Denis B, Ranjeva JP, Confort-Gouny S, Cozzone PJ. Metabolic counterpart of decreased apparent diffusion coefficient during hyperacute ischemic stroke: a brain proton magnetic resonance spectroscopic imaging study. Stroke. 2003;34(7):e82–7. 89. Ford CC, Griffey RH, Matwiyoff NA, Rosenberg GA. Multivoxel 1H-MRS of stroke. Neurology. 1992;42(7):1408–12. 90. Sappey-Marinier D, Calabrese G, Hetherington HP, Fisher SN, Deicken R, Van Dyke C, Fein G, Weiner MW.  Proton magnetic resonance spectroscopy of human brain: applications to normal white matter, chronic infarction, and MRI white matter signal hyperintensities. Magn Reson Med. 1992;26(2):313–27. 91. Federico F, Simone IL, Lucivero V, Giannini P, Laddomada G, Mezzapesa DM, Tortorella C.  Prognostic value of proton magnetic resonance spectroscopy in ischemic stroke. Arch Neurol. 1998;55(4):489–94. 92. Parsons MW, Li T, Barber PA, Yang Q, Darby DG, Desmond PM, Gerraty RP, Tress BM, Davis SM. Combined (1)H MR spectros-

10  Magnetic Resonance Spectroscopy: Clinical Applications copy and diffusion-weighted MRI improves the prediction of stroke outcome. Neurology. 2000;55(4):498–505. 93. Lemesle M, Walker P, Guy F, D’Athis P, Billiar T, Giroud M, Demougeot C, Lalande A, Baudouin N, Martin D, Brunotte F.  Multi-variate analysis predicts clinical outcome 30 days after middle cerebral artery infarction. Acta Neurol Scand. 2000;102(1):11–7. 94. Glodzik-Sobanska L, Li J, Mosconi L, Slowik A, Walecki J, Szczudlik A, Sobiecka B, de Leon MJ.  Prefrontal N-acetylaspartate and poststroke recovery: a longitudinal proton spectroscopy study. AJNR Am J Neuroradiol. 2007;28(3):470–4. 95. Felber SR.  Magnetic resonance in the differential diagnosis of dementia. J Neural Transm. 2002;109(7-8):1045–51. 96. Griffith HR, Stewart CC, den Hollander JA. Proton magnetic resonance spectroscopy in dementias and mild cognitive impairment. Int Rev Neurobiol. 2009;84:105–31. 97. Braak H, Braak E.  Neuropathological staging of Alzheimer-­ related changes. Acta Neuropathol. 1991;82(4):239–59. 98. Chetelat G, Baron JC.  Early diagnosis of Alzheimer’s disease: contribution of structural neuroimaging. NeuroImage. 2003;18(2):525–41. 99. Kantarci K. 1H magnetic resonance spectroscopy in dementia. Br J Radiol. 2007;80:146–52. 100. Kantarci K, Knopman DS, Dickson DW, Parisi JE, Whitwell JL, Weigand SD, Josephs KA, Boeve BF, Petersen RC, Jack CR Jr. Alzheimer disease: postmortem neuropathologic correlates of antemortem 1H MR spectroscopy metabolite measurements. Radiology. 2008;248(1):210–20. 101. Schuff N, Capizzano AA, Du AT, Amend DL, O’Neill J, Norman D, Kramer J, Jagust W, Miller B, Wolkowitz OM, Yaffe K, Weiner MW. Selective reduction of N-acetylaspartate in medial temporal and parietal lobes in AD. Neurology. 2002;58(6):928–35. 102. Chantal S, Braun CM, Bouchard RW, Labelle M, Boulanger Y. Similar 1H magnetic resonance spectroscopic metabolic pattern in the medial temporal lobes of patients with mild cognitive impairment and Alzheimer disease. Brain Res. 2004;1003(1-2):26–35. 103. Kantarci K, Jack CR Jr, Xu YC, Campeau NG, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Kokmen E, Tangalos EG, Petersen RC.  Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease: a 1H MRS study. Neurology. 2000;55(2):210–7. 104. Herminghaus S, Frolich L, Gorriz C, Pilatus U, Dierks T, Wittsack HJ, Lanfermann H, Maurer K, Zanella FE. Brain metabolism in Alzheimer disease and vascular dementia assessed by in  vivo proton magnetic resonance spectroscopy. Psychiatry Res. 2003;123(3):183–90. 105. Kantarci K, Xu Y, Shiung MM, O’Brien PC, Cha RH, Smith GE, Ivnik RJ, Boeve BF, Edland SD, Kokmen E, Tangalos EG, Petersen RC, Jack CR Jr. Comparative diagnostic utility of different MR modalities in mild cognitive impairment and Alzheimer’s disease. Dement Geriatr Cogn Disord. 2002;14(4):198–207. 106. Waldman AD, Rai GS.  The relationship between cognitive impairment and in vivo metabolite ratios in patients with clinical Alzheimer’s disease and vascular dementia: a proton magnetic resonance spectroscopy study. Neuroradiology. 2003;45(8):507–12. 107. Kantarci K, Weigand SD, Petersen RC, Boeve BF, Knopman DS, Gunter J, Reyes D, Shiung M, O’Brien PC, Smith GE, Ivnik RJ, Tangalos EG, Jack CR Jr. Longitudinal 1H MRS changes in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging. 2007;28(9):1330–9. 108. Jellinger KA. Formation and development of Lewy pathology: a critical update. J Neurol. 2009;256(Suppl 3):270–9. 109. Kurz A, Perneczky R. Neurobiology of cognitive disorders. Curr Opin Psychiatry. 2009;22(6):546–51. 110. Watson R, Blamire AM, O’Brien JT. Magnetic resonance imaging in lewy body dementias. Dement Geriatr Cogn Disord. 2009;28(6):493–506.

283 111. Lucetti C, Del Dotto P, Gambaccini G, Ceravolo R, Logi C, Berti C, Rossi G, Bianchi MC, Tosetti M, Murri L, Bonuccelli U. Influences of dopaminergic treatment on motor cortex in Parkinson disease: a MRI/MRS study. Mov Disord. 2007;22(15):2170–5. 112. Langston JW. The Parkinson’s complex: parkinsonism is just the tip of the iceberg. Ann Neurol. 2006;59(4):591–6. 113. Eidelberg D.  Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 2009;32(10):548–57. 114. Kalaitzakis ME, Pearce RK. The morbid anatomy of dementia in Parkinson’s disease. Acta Neuropathol. 2009;118(5):587–98. 115. Summerfield C, Gomez-Anson B, Tolosa E, Mercader JM, Marti MJ, Pastor P, Junque C.  Dementia in Parkinson disease: a proton magnetic resonance spectroscopy study. Arch Neurol. 2002;59(9):1415–20. 116. Griffith HR, den Hollander JA, Okonkwo OC, O’Brien T, Watts RL, Marson DC.  Brain metabolism differs in Alzheimer’s disease and Parkinson’s disease dementia. Alzheimers Dement. 2008;4(6):421–7. 117. Griffith HR, den Hollander JA, Okonkwo OC, O’Brien T, Watts RL, Marson DC.  Brain N-acetylaspartate is reduced in Parkinson disease with dementia. Alzheimer Dis Assoc Disord. 2008;22(1):54–60. 118. Oz G, Terpstra M, Tkac I, Aia P, Lowary J, Tuite PJ, Gruetter R.  Proton MRS of the unilateral substantia nigra in the human brain at 4 tesla: detection of high GABA concentrations. Magn Reson Med. 2006;55(2):296–301. 119. Molina JA, Garcia-Segura JM, Benito-Leon J, Gomez-Escalonilla C, del Ser T, Martinez V, Viano J.  Proton magnetic resonance spectroscopy in dementia with Lewy bodies. Eur Neurol. 2002;48(3):158–63. 120. Xuan X, Ding M, Gong X.  Proton magnetic resonance spectroscopy detects a relative decrease of N-acetylaspartate in the hippocampus of patients with dementia with Lewy bodies. J Neuroimaging. 2008;18(2):137–41. 121. Kantarci K, Avula R, Senjem ML, Samikoglu AR, Zhang B, Weigand SD, Przybelski SA, Edmonson HA, Vemuri P, Knopman DS, Ferman TJ, Boeve BF, Petersen RC, Jack CR Jr. Dementia with Lewy bodies and Alzheimer disease: neurodegenerative patterns characterized by DTI.  Neurology. 2010;74(22):1814–21. 122. Rabinovici GD, Miller BL.  Frontotemporal lobar degeneration: epidemiology, pathophysiology, diagnosis and management. CNS Drugs. 2010;24(5):375–98. 123. Bian H, Grossman M.  Frontotemporal lobar degeneration: recent progress in antemortem diagnosis. Acta Neuropathol. 2007;114(1):23–9. 124. Chawla S, Wang S, Moore P, Woo JH, Elman L, McCluskey LF, Melhem ER, Grossman M, Poptani H. Quantitative proton magnetic resonance spectroscopy detects abnormalities in dorsolateral prefrontal cortex and motor cortex of patients with frontotemporal lobar degeneration. J Neurol. 2010;257(1):114–21. 125. Ernst T, Chang L, Melchor R, Mehringer CM.  Frontotemporal dementia and early Alzheimer disease: differentiation with frontal lobe H-1 MR spectroscopy. Radiology. 1997;203(3):829–36. 126. Coulthard E, Firbank M, English P, Welch J, Birchall D, O’Brien J, Griffiths TD. Proton magnetic resonance spectroscopy in frontotemporal dementia. J Neurol. 2006;253(7):861–8. 127. Mihara M, Hattori N, Abe K, Sakoda S, Sawada T. Magnetic resonance spectroscopic study of Alzheimer’s disease and frontotemporal dementia/pick complex. Neuroreport. 2006;17(4):413–6. 128. Garrard P, Schott JM, MacManus DG, Hodges JR, Fox NC, Waldman AD.  Posterior cingulate neurometabolite profiles and clinical phenotype in frontotemporal dementia. Cogn Behav Neurol. 2006;19(4):185–9. 129. Kizu O, Yamada K, Ito H, Nishimura T.  Posterior cingulate metabolic changes in frontotemporal lobar degeneration

284 detected by magnetic resonance spectroscopy. Neuroradiology. 2004;46(4):277–81. 130. Kirshner HS.  Vascular dementia: a review of recent evidence for prevention and treatment. Curr Neurol Neurosci Rep. 2009;9(6):437–42. 131. MacKay S, Meyerhoff DJ, Constans JM, Norman D, Fein G, Weiner MW.  Regional gray and white matter metabolite differences in subjects with AD, with subcortical ischemic vascular dementia, and elderly controls with 1H magnetic resonance spectroscopic imaging. Arch Neurol. 1996;53(2):167–74. 132. Schuff N, Capizzano AA, Du AT, Amend DL, O’Neill J, Norman D, Jagust WJ, Chui HC, Kramer JH, Reed BR, Miller BL, Yaffe K, Weiner MW.  Different patterns of N-acetylaspartate loss in subcortical ischemic vascular dementia and AD.  Neurology. 2003;61(3):358–64. 133. Kantarci K, Petersen RC, Boeve BF, Knopman DS, Tang-Wai DF, O’Brien PC, Weigand SD, Edland SD, Smith GE, Ivnik RJ, Ferman TJ, Tangalos EG, Jack CR Jr. 1H MR spectroscopy in common dementias. Neurology. 2004;63(8):1393–8. 134. Capizzano AA, Schuff N, Amend DL, Tanabe JL, Norman D, Maudsley AA, Jagust W, Chui HC, Fein G, Segal MR, Weiner MW.  Subcortical ischemic vascular dementia: assessment with quantitative MR imaging and 1H MR spectroscopy. AJNR Am J Neuroradiol. 2000;21(4):621–30. 135. MacKay S, Ezekiel F, Di Sclafani V, Meyerhoff DJ, Gerson J, Norman D, Fein G, Weiner MW.  Alzheimer disease and subcortical ischemic vascular dementia: evaluation by combining MR imaging segmentation and H-1 MR spectroscopic imaging. Radiology. 1996;198(2):537–45. 136. Kattapong VJ, Brooks WM, Wesley MH, Kodituwakku PW, Rosenberg GA.  Proton magnetic resonance spectroscopy of vascular- and Alzheimer-type dementia. Arch Neurol. 1996;53(7):678–80. 137. Gil JM, Rego AC.  Mechanisms of neurodegeneration in Huntington’s disease. Eur J Neurosci. 2008;27(11):2803–20. 138. Rosas HD, Feigin AS, Hersch SM. Using advances in neuroimaging to detect, understand, and monitor disease progression in Huntington’s disease. NeuroRx. 2004;1(2):263–72. 139. Reynolds NC Jr, Prost RW, Mark LP. Heterogeneity in 1H-MRS profiles of presymptomatic and early manifest Huntington’s disease. Brain Res. 2005;1031(1):82–9. 140. Jenkins BG, Koroshetz WJ, Beal MF, Rosen BR.  Evidence for impairment of energy metabolism in  vivo in Huntington’s disease using localized 1H NMR spectroscopy. Neurology. 1993;43(12):2689–95. 141. Koroshetz WJ, Jenkins BG, Rosen BR, Beal MF. Energy metabolism defects in Huntington’s disease and effects of coenzyme Q10. Ann Neurol. 1997;41(2):160–5. 142. Jenkins BG, Rosas HD, Chen YC, Makabe T, Myers R, MacDonald M, Rosen BR, Beal MF, Koroshetz WJ. 1H NMR spectroscopy studies of Huntington’s disease: correlations with CAG repeat numbers. Neurology. 1998;50(5):1357–65. 143. Martin WR, Wieler M, Hanstock CC. Is brain lactate increased in Huntington’s disease? J Neurol Sci. 2007;263(1-2):70–4. 144. Taylor-Robinson SD, Weeks RA, Bryant DJ, Sargentoni J, Marcus CD, Harding AE, Brooks DJ.  Proton magnetic resonance spectroscopy in Huntington’s disease: evidence in favour of the glutamate excitotoxic theory. Mov Disord. 1996;11(2):167–73. 145. Davie CA, Barker GJ, Quinn N, Tofts PS, Miller DH. Proton MRS in Huntington’s disease. Lancet. 1994;343(8912):1580. 146. Ruocco HH, Lopes-Cendes I, Li LM, Cendes F. Evidence of thalamic dysfunction in Huntington disease by proton magnetic resonance spectroscopy. Mov Disord. 2007;22(14):2052–6. 147. Gomez-Anson B, Alegret M, Munoz E, Sainz A, Monte GC, Tolosa E.  Decreased frontal choline and neuropsychological performance in preclinical Huntington disease. Neurology. 2007;68(12):906–10.

A. Horská et al. 148. Kalra S, Arnold DL. Magnetic resonance spectroscopy for monitoring neuronal integrity in amyotrophic lateral sclerosis. Adv Exp Med Biol. 2006;576:275–82. 149. Turner MR, Kiernan MC, Leigh PN, Talbot K.  Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol. 2009;8(1):94–109. 150. Plaitakis A.  Glutamate dysfunction and selective motor neuron degeneration in amyotrophic lateral sclerosis: a hypothesis. Ann Neurol. 1990;28(1):3–8. 151. Sarchielli P, Pelliccioli GP, Tarducci R, Chiarini P, Presciutti O, Gobbi G, Gallai V. Magnetic resonance imaging and 1H-magnetic resonance spectroscopy in amyotrophic lateral sclerosis. Neuroradiology. 2001;43(3):189–97. 152. Bowen BC, Pattany PM, Bradley WG, Murdoch JB, Rotta F, Younis AA, Duncan RC, Quencer RM. MR imaging and localized proton spectroscopy of the precentral gyrus in amyotrophic lateral sclerosis. AJNR Am J Neuroradiol. 2000;21(4):647–58. 153. Pioro EP, Antel JP, Cashman NR, Arnold DL. Detection of cortical neuron loss in motor neuron disease by proton magnetic resonance spectroscopic imaging in vivo. Neurology. 1994;44(10):1933–8. 154. Gredal O, Rosenbaum S, Topp S, Karlsborg M, Strange P, Werdelin L.  Quantification of brain metabolites in amyotrophic lateral sclerosis by localized proton magnetic resonance spectroscopy. Neurology. 1997;48(4):878–81. 155. Pohl C, Block W, Karitzky J, Traber F, Schmidt S, Grothe C, Lamerichs R, Schild H, Klockgether T.  Proton magnetic resonance spectroscopy of the motor cortex in 70 patients with amyotrophic lateral sclerosis. Arch Neurol. 2001;58(5):729–35. 156. Suhy J, Miller RG, Rule R, Schuff N, Licht J, Dronsky V, Gelinas D, Maudsley AA, Weiner MW.  Early detection and longitudinal changes in amyotrophic lateral sclerosis by (1)H MRSI. Neurology. 2002;58(5):773–9. 157. Rule RR, Suhy J, Schuff N, Gelinas DF, Miller RG, Weiner MW.  Reduced NAA in motor and non-motor brain regions in amyotrophic lateral sclerosis: a cross-sectional and longitudinal study. Amyotroph Lateral Scler Other Motor Neuron Disord. 2004;5(3):141–9. 158. Kalra S, Hanstock CC, Martin WR, Allen PS, Johnston WS.  Detection of cerebral degeneration in amyotrophic lateral sclerosis using high-field magnetic resonance spectroscopy. Arch Neurol. 2006;63(8):1144–8. 159. Han J, Ma L. Study of the features of proton MR spectroscopy ((1) H-MRS) on amyotrophic lateral sclerosis. J Magn Reson Imaging. 2010;31(2):305–8. 160. Wang S, Poptani H, Woo JH, Desiderio LM, Elman LB, McCluskey LF, Krejza J, Melhem ER. Amyotrophic lateral sclerosis: diffusion-tensor and chemical shift MR imaging at 3.0 T. Radiology. 2006;239(3):831–8. 161. Cwik VA, Hanstock CC, Allen PS, Martin WR.  Estimation of brainstem neuronal loss in amyotrophic lateral sclerosis with in  vivo proton magnetic resonance spectroscopy. Neurology. 1998;50(1):72–7. 162. Pioro EP, Majors AW, Mitsumoto H, Nelson DR, Ng TC. 1H-MRS evidence of neurodegeneration and excess glutamate + glutamine in ALS medulla. Neurology. 1999;53(1):71–9. 163. Kalra S, Tai P, Genge A, Arnold DL. Rapid improvement in cortical neuronal integrity in amyotrophic lateral sclerosis detected by proton magnetic resonance spectroscopic imaging. J Neurol. 2006;253(8):1060–3. 164. Kalra S, Vitale A, Cashman NR, Genge A, Arnold DL. Cerebral degeneration predicts survival in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2006;77(11):1253–5. 165. Kastrup O, Wanke I, Maschke M. Neuroimaging of infections of the central nervous system. Semin Neurol. 2008;28(4):511–22. 166. Karampekios S, Hesselink J.  Cerebral infections. Eur Radiol. 2005;15(3):485–93. 167. Garg M, Gupta RK, Husain M, Chawla S, Chawla J, Kumar R, Rao SB, Misra MK, Prasad KN.  Brain abscesses: etiologic cat-

10  Magnetic Resonance Spectroscopy: Clinical Applications egorization with in  vivo proton MR spectroscopy. Radiology. 2004;230(2):519–27. 168. Kapsalaki EZ, Gotsis ED, Fountas KN. The role of proton magnetic resonance spectroscopy in the diagnosis and categorization of cerebral abscesses. Neurosurg Focus. 2008;24(6):E7. 169. Shukla-Dave A, Gupta RK, Roy R, Husain N, Paul L, Venkatesh SK, Rashid MR, Chhabra DK, Husain M. Prospective evaluation of in  vivo proton MR spectroscopy in differentiation of similar appearing intracranial cystic lesions. Magn Reson Imaging. 2001;19(1):103–10. 170. Gupta RK, Vatsal DK, Husain N, Chawla S, Prasad KN, Roy R, Kumar R, Jha D, Husain M. Differentiation of tuberculous from pyogenic brain abscesses with in  vivo proton MR spectroscopy and magnetization transfer MR imaging. AJNR Am J Neuroradiol. 2001;22(8):1503–9. 171. Agarwal M, Chawla S, Husain N, Jaggi RS, Husain M, Gupta RK.  Higher succinate than acetate levels differentiate cerebral degenerating cysticerci from anaerobic abscesses on in-vivo proton MR spectroscopy. Neuroradiology. 2004;46(3):211–5. 172. Lai PH, Li KT, Hsu SS, Hsiao CC, Yip CW, Ding S, Yeh LR, Pan HB. Pyogenic brain abscess: findings from in vivo 1.5-T and 11.7-T in vitro proton MR spectroscopy. AJNR Am J Neuroradiol. 2005;26(2):279–88. 173. Lai PH, Ho JT, Chen WL, Hsu SS, Wang JS, Pan HB, Yang CF. Brain abscess and necrotic brain tumor: discrimination with proton MR spectroscopy and diffusion-weighted imaging. AJNR Am J Neuroradiol. 2002;23(8):1369–77. 174. Chiang IC, Hsieh TJ, Chiu ML, Liu GC, Kuo YT, Lin WC.  Distinction between pyogenic brain abscess and necrotic brain tumour using 3-tesla MR spectroscopy, diffusion and perfusion imaging. Br J Radiol. 2009;82(982):813–20. 175. Rock RB, Olin M, Baker CA, Molitor TW, Peterson PK. Central nervous system tuberculosis: pathogenesis and clinical aspects. Clin Microbiol Rev. 2008;21(2):243–61. 176. Be NA, Kim KS, Bishai WR, Jain SK.  Pathogenesis of central nervous system tuberculosis. Curr Mol Med. 2009;9(2):94–9. 177. Morgado C, Ruivo N. Imaging meningo-encephalic tuberculosis. Eur J Radiol. 2005;55(2):188–92. 178. Poptani H, Gupta RK, Roy R, Pandey R, Jain VK, Chhabra DK.  Characterization of intracranial mass lesions with in  vivo proton MR spectroscopy. AJNR Am J Neuroradiol. 1995;16(8):1593–603. 179. Jayasundar R, Singh VP, Raghunathan P, Jain K, Banerji AK.  Inflammatory granulomas: evaluation with proton MRS. NMR Biomed. 1999;12(3):139–44. 180. Gupta RK, Roy R, Dev R, Husain M, Poptani H, Pandey R, Kishore J, Bhaduri AP. Finger printing of Mycobacterium tuberculosis in patients with intracranial tuberculomas by using in vivo, ex  vivo, and in  vitro magnetic resonance spectroscopy. Magn Reson Med. 1996;36(6):829–33. 181. Poptani H, Kaartinen J, Gupta RK, Niemitz M, Hiltunen Y, Kauppinen RA. Diagnostic assessment of brain tumours and non-­ neoplastic brain disorders in vivo using proton nuclear magnetic resonance spectroscopy and artificial neural networks. J Cancer Res Clin Oncol. 1999;125(6):343–9. 182. Baringer JR.  Herpes simplex infections of the nervous system. Neurol Clin. 2008;26(3):657–74. 183. Menon DK, Sargentoni J, Peden CJ, Bell JD, Cox IJ, Coutts GA, Baudouin C, Newman CG.  Proton MR spectroscopy in herpes simplex encephalitis: assessment of neuronal loss. J Comput Assist Tomogr. 1990;14(3):449–52. 184. Takanashi J, Sugita K, Ishii M, Aoyagi M, Niimi H. Longitudinal MR imaging and proton MR spectroscopy in herpes simplex encephalitis. J Neurol Sci. 1997;149(1):99–102. 185. Salvan AM, Confort-Gouny S, Cozzone PJ, Vion-Dury J. Atlas of brain proton magnetic resonance spectra. Part III: Viral infections. J Neuroradiol. 1999;26(3):154–61.

285 186. Anthony IC, Bell JE. The neuropathology of HIV/AIDS. Int Rev Psychiatry. 2008;20(1):15–24. 187. Singer EJ, Valdes-Sueiras M, Commins D, Levine A. Neurologic presentations of AIDS. Neurol Clin. 2010;28(1):253–75. 188. Thurnher MM, Donovan Post MJ.  Neuroimaging in the brain in HIV-1-infected patients. Neuroimaging Clin N Am. 2008;18(1):93–117. 189. Tarasow E, Wiercinska-Drapalo A, Kubas B, Dzienis W, Orzechowska-Bobkiewicz A, Prokopowicz D, Walecki J. Cerebral MR spectroscopy in neurologically asymptomatic HIV-infected patients. Acta Radiol. 2003;44(2):206–12. 190. Meyerhoff DJ, Bloomer C, Cardenas V, Norman D, Weiner MW, Fein G.  Elevated subcortical choline metabolites in cognitively and clinically asymptomatic HIV+ patients. Neurology. 1999;52(5):995–1003. 191. Tracey I, Carr CA, Guimaraes AR, Worth JL, Navia BA, Gonzalez RG.  Brain choline-containing compounds are elevated in HIV-­ positive patients before the onset of AIDS dementia complex: a proton magnetic resonance spectroscopic study. Neurology. 1996;46(3):783–8. 192. Chang L, Ernst T, Leonido-Yee M, Witt M, Speck O, Walot I, Miller EN.  Highly active antiretroviral therapy reverses brain metabolite abnormalities in mild HIV dementia. Neurology. 1999;53(4):782–9. 193. Winston A, Duncombe C, Li PC, Gill JM, Kerr SJ, Puls R, Petoumenos K, Taylor-Robinson SD, Emery S, Cooper DA. Does choice of combination antiretroviral therapy (cART) alter changes in cerebral function testing after 48 weeks in treatment-naive, HIV-1-infected individuals commencing cART? A randomized, controlled study. Clin Infect Dis. 2010;50(6):920–9. 194. Woods SP, Moore DJ, Weber E, Grant I. Cognitive neuropsychology of HIV-associated neurocognitive disorders. Neuropsychol Rev. 2009;19(2):152–68. 195. Chang L, Ernst T, Witt MD, Ames N, Gaiefsky M, Miller E.  Relationships among brain metabolites, cognitive function, and viral loads in antiretroviral-naive HIV patients. NeuroImage. 2002;17(3):1638–48. 196. Paul RH, Ernst T, Brickman AM, Yiannoutsos CT, Tate DF, Cohen RA, Navia BA. Relative sensitivity of magnetic resonance spectroscopy and quantitative magnetic resonance imaging to cognitive function among nondemented individuals infected with HIV. J Int Neuropsychol Soc. 2008;14(5):725–33. 197. Mohamed MA, Lentz MR, Lee V, Halpern EF, Sacktor N, Selnes O, Barker PB, Pomper MG.  Factor analysis of proton MR spectroscopic imaging data in HIV infection: metabolite-­ derived factors help identify infection and dementia. Radiology. 2010;254(2):577–86. 198. Lentz MR, Kim WK, Lee V, Bazner S, Halpern EF, Venna N, Williams K, Rosenberg ES, Gonzalez RG. Changes in MRS neuronal markers and T cell phenotypes observed during early HIV infection. Neurology. 2009;72(17):1465–72. 199. Chang L, Miller BL, McBride D, Cornford M, Oropilla G, Buchthal S, Chiang F, Aronow H, Beck CK, Ernst T.  Brain lesions in patients with AIDS: H-1 MR spectroscopy. Radiology. 1995;197(2):525–31. 200. Simone IL, Federico F, Tortorella C, Andreula CF, Zimatore GB, Giannini P, Angarano G, Lucivero V, Picciola P, Carrara D, Bellacosa A, Livrea P.  Localised 1H-MR spectroscopy for metabolic characterisation of diffuse and focal brain lesions in patients infected with HIV.  J Neurol Neurosurg Psychiatry. 1998;64(4):516–23. 201. Kingsley PB, Shah TC, Woldenberg R.  Identification of diffuse and focal brain lesions by clinical magnetic resonance spectroscopy. NMR Biomed. 2006;19(4):435–62. 202. Lange T, Dydak U, Roberts TP, Rowley HA, Bjeljac M, Boesiger P. Pitfalls in lactate measurements at 3T. AJNR Am J Neuroradiol. 2006;27(4):895–901.

286 203. Zacharia TT, Law M, Naidich TP, Leeds NE.  Central nervous system lymphoma characterization by diffusionweighted imaging and MR spectroscopy. J Neuroimaging. 2008;18(4):411–7. 204. Taillibert S, Guillevin R, Menuel C, Sanson M, Hoang-Xuan K, Chiras J, Duffau H. Brain lymphoma: usefulness of the magnetic resonance spectroscopy. J Neuro-Oncol. 2008;86(2):225–9. 205. Iranzo A, Moreno A, Pujol J, Marti-Fabregas J, Domingo P, Molet J, Ris J, Cadafalch J.  Proton magnetic resonance spectroscopy pattern of progressive multifocal leukoencephalopathy in AIDS. J Neurol Neurosurg Psychiatry. 1999;66(4):520–3. 206. Chang L, Ernst T, Tornatore C, Aronow H, Melchor R, Walot I, Singer E, Cornford M.  Metabolite abnormalities in progressive multifocal leukoencephalopathy by proton magnetic resonance spectroscopy. Neurology. 1997;48(4):836–45. 207. Anlar O.  Treatment of multiple sclerosis. CNS Neurol Disord Drug Targets. 2009;8(3):167–74. 208. Neumann H. Molecular mechanisms of axonal damage in inflammatory central nervous system diseases. Curr Opin Neurol. 2003;16(3):267–73. 209. Matthews PM.  Brain imaging of multiple sclerosis: the next 10 years. Neuroimaging Clin N Am. 2009;19(1):101–12. 210. Narayanan S, De Stefano N, Francis GS, Arnaoutelis R, Caramanos Z, Collins DL, Pelletier D, Arnason BGW, Antel JP, Arnold DL.  Axonal metabolic recovery in multiple sclerosis patients treated with interferon beta-1b. J Neurol. 2001;248(11):979–86. 211. Khan O, Shen Y, Bao F, Caon C, Tselis A, Latif Z, Zak I. Long-­ term study of brain 1H-MRS study in multiple sclerosis: effect of glatiramer acetate therapy on axonal metabolic function and feasibility of long-Term H-MRS monitoring in multiple sclerosis. J Neuroimaging. 2008;18(3):314–9. 212. Sijens PE, Mostert JP, Oudkerk M, De Keyser J. (1)H MR spectroscopy of the brain in multiple sclerosis subtypes with analysis of the metabolite concentrations in gray and white matter: initial findings. Eur Radiol. 2006;16(2):489–95. 213. De Stefano N, Narayanan S, Francis GS, Arnaoutelis R, Tartaglia MC, Antel JP, Matthews PM, Arnold DL.  Evidence of axonal damage in the early stages of multiple sclerosis and its relevance to disability. Arch Neurol. 2001;58(1):65–70. 214. Filippi M, Bozzali M, Rovaris M, Gonen O, Kesavadas C, Ghezzi A, Martinelli V, Grossman RI, Scotti G, Comi G, Falini A. Evidence for widespread axonal damage at the earliest clinical stage of multiple sclerosis. Brain. 2003;126(Pt 2):433–7. 215. Sarchielli P, Presciutti O, Pelliccioli GP, Tarducci R, Gobbi G, Chiarini P, Alberti A, Vicinanza F, Gallai V.  Absolute quantification of brain metabolites by proton magnetic resonance spectroscopy in normal-appearing white matter of multiple sclerosis patients. Brain. 1999;122(Pt 3):513–21. 216. Chard DT, Griffin CM, McLean MA, Kapeller P, Kapoor R, Thompson AJ, Miller DH.  Brain metabolite changes in cortical grey and normal-appearing white matter in clinically early relapsing-­ remitting multiple sclerosis. Brain. 2002;125(Pt 10):2342–52. 217. Bruhn H, Frahm J, Merboldt KD, Hanicke W, Hanefeld F, Christen HJ, Kruse B, Bauer HJ.  Multiple sclerosis in children: cerebral metabolic alterations monitored by localized proton magnetic resonance spectroscopy in vivo. Ann Neurol. 1992;32(2):140–50. 218. Mader I, Seeger U, Weissert R, Klose U, Naegele T, Melms A, Grodd W.  Proton MR spectroscopy with metabolite-nulling reveals elevated macromolecules in acute multiple sclerosis. Brain. 2001;124(Pt 5):953–61. 219. De Stefano N, Matthews PM, Antel JP, Preul M, Francis G, Arnold DL.  Chemical pathology of acute demyelinating lesions and its correlation with disability. Ann Neurol. 1995;38(6):901–9. 220. Srinivasan R, Sailasuta N, Hurd R, Nelson S, Pelletier D. Evidence of elevated glutamate in multiple sclerosis using magnetic resonance spectroscopy at 3 T. Brain. 2005;128(Pt 5):1016–25.

A. Horská et al. 221. Narayana PA, Doyle TJ, Lai D, Wolinsky JS. Serial proton magnetic resonance spectroscopic imaging, contrast-enhanced magnetic resonance imaging, and quantitative lesion volumetry in multiple sclerosis. Ann Neurol. 1998;43(1):56–71. 222. Bitsch A, Bruhn H, Vougioukas V, Stringaris A, Lassmann H, Frahm J, Bruck W. Inflammatory CNS demyelination: histopathologic correlation with in  vivo quantitative proton MR spectroscopy. AJNR Am J Neuroradiol. 1999;20(9):1619–27. 223. De Stefano N, Matthews PM, Arnold DL.  Reversible decreases in N-acetylaspartate after acute brain injury. Magn Reson Med. 1995;34(5):721–7. 224. De Stefano N, Filippi M. MR spectroscopy in multiple sclerosis. J Neuroimaging. 2007;17(1):31S–5S. 225. Wattjes MP, Harzheim M, Lutterbey GG, Klotz L, Schild HH, Traber F.  Axonal damage but no increased glial cell activity in the normal-appearing white matter of patients with clinically isolated syndromes suggestive of multiple sclerosis using high-­ field magnetic resonance spectroscopy. AJNR Am J Neuroradiol. 2007;28(8):1517–22. 226. Fu L, Matthews PM, De Stefano N, Worsley KJ, Narayanan S, Francis GS, Antel JP, Wolfson C, Arnold DL.  Imaging axonal damage of normal-appearing white matter in multiple sclerosis. Brain. 1998;121(Pt 1):103–13. 227. Falini A, Calabrese G, Filippi M, Origgi D, Lipari S, Colombo B, Comi G, Scotti G. Benign versus secondary-progressive multiple sclerosis: the potential role of proton MR spectroscopy in defining the nature of disability. AJNR Am J Neuroradiol. 1998;19(2):223–9. 228. De Stefano N, Narayanan S, Francis SJ, Smith S, Mortilla M, Tartaglia MC, Bartolozzi ML, Guidi L, Federico A, Arnold DL. Diffuse axonal and tissue injury in patients with multiple sclerosis with low cerebral lesion load and no disability. Arch Neurol. 2002;59(10):1565–71. 229. Inglese M, Li BS, Rusinek H, Babb JS, Grossman RI, Gonen O.  Diffusely elevated cerebral choline and creatine in relapsing-­ remitting multiple sclerosis. Magn Reson Med. 2003;50(1):190–5. 230. Kapeller P, McLean MA, Griffin CM, Chard D, Parker GJ, Barker GJ, Thompson AJ, Miller DH.  Preliminary evidence for neuronal damage in cortical grey matter and normal appearing white matter in short duration relapsing-remitting multiple sclerosis: a quantitative MR spectroscopic imaging study. J Neurol. 2001;248(2):131–8. 231. Sarchielli P, Presciutti O, Tarducci R, Gobbi G, Alberti A, Pelliccioli GP, Chiarini P, Gallai V. Localized (1)H magnetic resonance spectroscopy in mainly cortical gray matter of patients with multiple sclerosis. J Neurol. 2002;249(7):902–10. 232. Adalsteinsson E, Langer-Gould A, Homer RJ, Rao A, Sullivan EV, Lima CA, Pfefferbaum A, Atlas SW.  Gray matter N-acetyl aspartate deficits in secondary progressive but not relapsing-remitting multiple sclerosis. AJNR Am J Neuroradiol. 2003;24(10):1941–5. 233. Cifelli A, Arridge M, Jezzard P, Esiri MM, Palace J, Matthews PM.  Thalamic neurodegeneration in multiple sclerosis. Ann Neurol. 2002;52(5):650–3. 234. Wylezinska M, Cifelli A, Jezzard P, Palace J, Alecci M, Matthews PM. Thalamic neurodegeneration in relapsing-remitting multiple sclerosis. Neurology. 2003;60(12):1949–54. 235. Inglese M, Liu S, Babb JS, Mannon LJ, Grossman RI, Gonen O.  Three-dimensional proton spectroscopy of deep gray matter nuclei in relapsing-remitting MS.  Neurology. 2004;63(1) :170–2. 236. Sastre-Garriga J, Ingle GT, Chard DT, Ramio-Torrenta L, McLean MA, Miller DH, Thompson AJ.  Metabolite changes in normal-appearing gray and white matter are linked with disability in early primary progressive multiple sclerosis. Arch Neurol. 2005;62(4):569–73.

10  Magnetic Resonance Spectroscopy: Clinical Applications 237. Lassmann H. Acute disseminated encephalomyelitis and multiple sclerosis. Brain. 2010;133(Pt 2):317–9. 238. Bizzi A, Ulug AM, Crawford TO, Passe T, Bugiani M, Bryan RN, Barker PB.  Quantitative proton MR spectroscopic imaging in acute disseminated encephalomyelitis. AJNR Am J Neuroradiol. 2001;22(6):1125–30. 239. Gabis LV, Panasci DJ, Andriola MR, Huang W.  Acute disseminated encephalomyelitis: an MRI/MRS longitudinal study. Pediatr Neurol. 2004;30(5):324–9. 240. Mader I, Wolff M, Nagele T, Niemann G, Grodd W, Kuker W. MRI and proton MR spectroscopy in acute disseminated encephalomyelitis. Childs Nerv Syst. 2005;21(7):566–72. 241. Balasubramanya KS, Kovoor JM, Jayakumar PN, Ravishankar S, Kamble RB, Panicker J, Nagaraja D.  Diffusion-weighted imaging and proton MR spectroscopy in the characterization of acute disseminated encephalomyelitis. Neuroradiology. 2007;49(2):177–83. 242. Ben Sira L, Miller E, Artzi M, Fattal-Valevski A, Constantini S, Ben BD. 1H-MRS for the diagnosis of acute disseminated encephalomyelitis: insight into the acute-disease stage. Pediatr Radiol. 2010;40(1):106–13. 243. Papanagiotou P, Grunwald IQ, Farmakis G, Hartmann KM, Politi M, Roth C, Reith W. Magnetresonanzspektroskopie bei entzündlichen Hirnerkrankungen. Radiologe. 2008;48(6):582–7. 244. Woermann FG, Vollmar C.  Clinical MRI in children and adults with focal epilepsy: a critical review. Epilepsy Behav. 2009;15(1):40–9. 245. Cascino GD.  Neuroimaging in epilepsy: diagnostic strategies in partial epilepsy. Semin Neurol. 2008;28(4):523–32. 246. Achten E, Deblaere K. Seizures, complex, partial. In: Baert AL, editor. Encyclopedia of diagnostic imaging. Berlin: Springer; 2008. p. 1660–6. 247. Jackson GD, Berkovic SF, Duncan JS, Connelly A.  Optimizing the diagnosis of hippocampal sclerosis using MR imaging. AJNR Am J Neuroradiol. 1993;14(3):753–62. 248. Van Paesschen W, Connelly A, Johnson CL, Duncan JS. The amygdala and intractable temporal lobe epilepsy: a quantitative magnetic resonance imaging study. Neurology. 1996;47(4):1021–31. 249. Ende GR, Laxer KD, Knowlton RC, Matson GB, Schuff N, Fein G, Weiner MW.  Temporal lobe epilepsy: bilateral hippocampal metabolite changes revealed at proton MR spectroscopic imaging. Radiology. 1997;202(3):809–17. 250. Breiter SN, Arroyo S, Mathews VP, Lesser RP, Bryan RN, Barker PB.  Proton MR spectroscopy in patients with seizure disorders. AJNR Am J Neuroradiol. 1994;15(2):373–84. 251. Capizzano AA, Vermathen P, Laxer KD, Matson GB, Maudsley AA, Soher BJ, Schuff NW, Weiner MW.  Multisection proton MR spectroscopy for mesial temporal lobe epilepsy. AJNR Am J Neuroradiol. 2002;23(8):1359–68. 252. Simister RJ, Woermann FG, McLean MA, Bartlett PA, Barker GJ, Duncan JS.  A short-echo-time proton magnetic resonance spectroscopic imaging study of temporal lobe epilepsy. Epilepsia. 2002;43(9):1021–31. 253. Ng TC, Comair YG, Xue M, So N, Majors A, Kolem H, Luders H, Modic M.  Temporal lobe epilepsy: presurgical localization with proton chemical shift imaging. Radiology. 1994;193(2):465–72. 254. Hetherington HP, Kuzniecky RI, Pan JW, Vaughan JT, Twieg DB, Pohost GM.  Application of high field spectroscopic imaging in the evaluation of temporal lobe epilepsy. Magn Reson Imaging. 1995;13(8):1175–80. 255. Vermathen P, Laxer KD, Schuff N, Matson GB, Weiner MW.  Evidence of neuronal injury outside the medial temporal lobe in temporal lobe epilepsy: N-acetylaspartate concentration reductions detected with multisection proton MR spectroscopic imaging--initial experience. Radiology. 2003;226(1):195–202.

287 256. Mueller SG, Suhy J, Laxer KD, Flenniken DL, Axelrad J, Capizzano AA, Weiner MW. Reduced extrahippocampal NAA in mesial temporal lobe epilepsy. Epilepsia. 2002;43(10):1210–6. 257. Doelken MT, Stefan H, Pauli E, Stadlbauer A, Struffert T, Engelhorn T, Richter G, Ganslandt O, Doerfler A, Hammen T. (1)H-MRS profile in MRI positive- versus MRI negative patients with temporal lobe epilepsy. Seizure. 2008;17(6):490–7. 258. Helms G, Ciumas C, Kyaga S, Savic I.  Increased thalamus levels of glutamate and glutamine (Glx) in patients with idiopathic generalised epilepsy. J Neurol Neurosurg Psychiatry. 2006;77(4):489–94. 259. Vermathen P, Laxer KD, Matson GB, Weiner MW. Hippocampal structures: anteroposterior N-acetylaspartate differences in patients with epilepsy and control subjects as shown with proton MR spectroscopic imaging. Radiology. 2000;214(2):403–10. 260. Connelly A, Van Paesschen W, Porter DA, Johnson CL, Duncan JS, Gadian DG. Proton magnetic resonance spectroscopy in MRI-­ negative temporal lobe epilepsy. Neurology. 1998;51(1):61–6. 261. Colon AJ, Hofman P, Ossenblok PP, Jansen JF, Ter Beek LC, Berting R, Stam CJ, Boon P. MRS-lateralisation index in patients with epilepsy and focal cortical dysplasia or a MEG-focus using bilateral single voxels. Epilepsy Res. 2010;89(1):148–53. 262. Hetherington HP, Kuzniecky RI, Vives K, Devinsky O, Pacia S, Luciano D, Vasquez B, Haut S, Spencer DD, Pan JW. A subcortical network of dysfunction in TLE measured by magnetic resonance spectroscopy. Neurology. 2007;69(24):2256–65. 263. Brazdil M, Marecek R, Fojtikova D, Mikl M, Kuba R, Krupa P, Rektor I.  Correlation study of optimized voxel-based morphometry and (1)H MRS in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. Hum Brain Mapp. 2009;30(4):1226–35. 264. Park SA, Kim GS, Lee SK, Lim SR, Heo K, Park SC, Chang JW, Kim DI, Lee BI.  Interictal epileptiform discharges relate to 1H-MRS-detected metabolic abnormalities in mesial temporal lobe epilepsy. Epilepsia. 2002;43(11):1385–9. 265. Kuzniecky R, Palmer C, Hugg J, Martin R, Sawrie S, Morawetz R, Faught E, Knowlton R. Magnetic resonance spectroscopic imaging in temporal lobe epilepsy: neuronal dysfunction or cell loss? Arch Neurol. 2001;58(12):2048–53. 266. Hugg JW, Kuzniecky RI, Gilliam FG, Morawetz RB, Fraught RE, Hetherington HP.  Normalization of contralateral metabolic function following temporal lobectomy demonstrated by 1H magnetic resonance spectroscopic imaging. Ann Neurol. 1996;40(2) :236–9. 267. Cendes F, Andermann F, Dubeau F, Matthews PM, Arnold DL.  Normalization of neuronal metabolic dysfunction after surgery for temporal lobe epilepsy. evidence from proton MR spectroscopic imaging. Neurology. 1997;49(6):1525–33. 268. Vermathen P, Ende G, Laxer KD, Walker JA, Knowlton RC, Barbaro NM, Matson GB, Weiner MW. Temporal lobectomy for epilepsy: recovery of the contralateral hippocampus measured by (1)H MRS. Neurology. 2002;59(4):633–6. 269. Faerber EN, Poussaint TY. Magnetic resonance of metabolic and degenerative diseases in children. Top Magn Reson Imaging. 2002;13(1):3–21. 270. Barkovich AJ. An approach to MRI of metabolic disorders in children. J Neuroradiol. 2007;34(2):75–88. 271. Schulze A.  Creatine deficiency syndromes. Mol Cell Biochem. 2003;244(1-2):143–50. 272. Stockler S, Schutz PW, Salomons GS.  Cerebral creatine deficiency syndromes: clinical aspects, treatment and pathophysiology. Subcell Biochem. 2007;46:149–66. 273. Stockler S, Holzbach U, Hanefeld F, Marquardt I, Helms G, Requart M, Hanicke W, Frahm J.  Creatine deficiency in the brain: a new, treatable inborn error of metabolism. Pediatr Res. 1994;36(3):409–13.

288 274. Verbruggen KT, Sijens PE, Schulze A, Lunsing RJ, Jakobs C, Salomons GS, van Spronsen FJ.  Successful treatment of a guanidinoacetate methyltransferase deficient patient: findings with relevance to treatment strategy and pathophysiology. Mol Genet Metab. 2007;91(3):294–6. 275. Sijens PE, Verbruggen KT, Meiners LC, Soorani-Lunsing RJ, Rake JP, Oudkerk M. 1H chemical shift imaging of the brain in guanidino methyltransferase deficiency, a creatine deficiency syndrome; guanidinoacetate accumulation in the gray matter. Eur Radiol. 2005;15(9):1923–6. 276. Bianchi MC, Tosetti M, Fornai F, Alessandri MG, Cipriani P, De Vito G, Canapicchi R.  Reversible brain creatine deficiency in two sisters with normal blood creatine level. Ann Neurol. 2000;47(4):511–3. 277. Cecil KM, Salomons GS, Ball WS Jr, Wong B, Chuck G, Verhoeven NM, Jakobs C, DeGrauw TJ.  Irreversible brain creatine deficiency with elevated serum and urine creatine: a creatine transporter defect? Ann Neurol. 2001;49(3):401–4. 278. Martin E, Capone A, Schneider J, Hennig J, Thiel T. Absence of N-acetylaspartate in the human brain: impact on neurospectroscopy? Ann Neurol. 2001;49(4):518–21. 279. Harding C. Progress toward cell-directed therapy for phenylketonuria. Clin Genet. 2008;74(2):97–104. 280. Moller HE, Ullrich K, Weglage J.  In vivo proton magnetic resonance spectroscopy in phenylketonuria. Eur J Pediatr. 2000;159(2):S121–5. 281. Pietz J, Kreis R, Boesch C, Penzien J, Rating D, Herschkowitz N. The dynamics of brain concentrations of phenylalanine and its clinical significance in patients with phenylketonuria determined by in  vivo 1H magnetic resonance spectroscopy. Pediatr Res. 1995;38(5):657–63. 282. Briere JJ, Favier J, El Ghouzzi V, Djouadi F, Benit P, Gimenez AP, Rustin P. Succinate dehydrogenase deficiency in human. Cell Mol Life Sci. 2005;62(19-20):2317–24. 283. Brockmann K, Bjornstad A, Dechent P, Korenke CG, Smeitink J, Trijbels JM, Athanassopoulos S, Villagran R, Skjeldal OH, Wilichowski E, Frahm J, Hanefeld F. Succinate in dystrophic white matter: a proton magnetic resonance spectroscopy finding characteristic for complex II deficiency. Ann Neurol. 2002;52(1):38–46. 284. Chuang DT, Chuang JL, Wynn RM. Lessons from genetic disorders of branched-chain amino acid metabolism. J Nutr. 2006;136(1 Suppl):243S–9S. 285. Heindel W, Kugel H, Wendel U, Roth B, Benz-Bohm G. Proton magnetic resonance spectroscopy reflects metabolic decompensation in maple syrup urine disease. Pediatr Radiol. 1995;25(4):296–9. 286. Jan W, Zimmerman RA, Wang ZJ, Berry GT, Kaplan PB, Kaye EM.  MR diffusion imaging and MR spectroscopy of maple syrup urine disease during acute metabolic decompensation. Neuroradiology. 2003;45(6):393–9. 287. Kure S, Tada K, Narisawa K.  Nonketotic hyperglycinemia: biochemical, molecular, and neurological aspects. Jpn J Hum Genet. 1997;42(1):13–22. 288. Shah DK, Tingay DG, Fink AM, Hunt RW, Dargaville PA. Magnetic resonance imaging in neonatal nonketotic hyperglycinemia. Pediatr Neurol. 2005;33(1):50–2. 289. Heindel W, Kugel H, Roth B. Noninvasive detection of increased glycine content by proton MR spectroscopy in the brains of two infants with nonketotic hyperglycinemia. AJNR Am J Neuroradiol. 1993;14(3):629–35. 290. Gabis L, Parton P, Roche P, Lenn N, Tudorica A, Huang W.  In vivo 1H magnetic resonance spectroscopic measurement of brain glycine levels in nonketotic hyperglycinemia. J Neuroimaging. 2001;11(2):209–11. 291. Matalon R, Michals-Matalon K.  Biochemistry and molecular biology of Canavan disease. Neurochem Res. 1999;24(4) :507–13.

A. Horská et al. 292. Austin SJ, Connelly A, Gadian DG, Benton JS, Brett EM.  Localized 1H NMR spectroscopy in Canavan’s disease: a report of two cases. Magn Reson Med. 1991;19(2):439–45. 293. Barker PB, Bryan RN, Kumar AJ, Naidu S. Proton NMR spectroscopy of Canavan’s disease. Neuropediatrics. 1992;23(5):263–7. 294. Wittsack HJ, Kugel H, Roth B, Heindel W.  Quantitative measurements with localized 1H MR spectroscopy in children with Canavan’s disease. J Magn Reson Imaging. 1996;6(6):889–93. 295. McErlean A, Abdalla K, Donoghue V, Ryan S. The dentate nucleus in children: normal development and patterns of disease. Pediatr Radiol. 2010;40(3):326–39. 296. Saneto RP, Friedman SD, Shaw DW. Neuroimaging of mitochondrial disease. Mitochondrion. 2008;8(5-6):396–413. 297. Finsterer J. Central nervous system imaging in mitochondrial disorders. Can J Neurol Sci. 2009;36(2):143–53. 298. De Stefano N, Matthews PM, Ford B, Genge A, Karpati G, Arnold DL.  Short-term dichloroacetate treatment improves indices of cerebral metabolism in patients with mitochondrial disorders. Neurology. 1995;45(6):1193–8. 299. Bianchi MC, Sgandurra G, Tosetti M, Battini R, Cioni G. Brain magnetic resonance in the diagnostic evaluation of mitochondrial encephalopathies. Biosci Rep. 2007;27(1-3):69–85. 300. Bianchi MC, Tosetti M, Battini R, Manca ML, Mancuso M, Cioni G, Canapicchi R, Siciliano G. Proton MR spectroscopy of mitochondrial diseases: analysis of brain metabolic abnormalities and their possible diagnostic relevance. AJNR Am J Neuroradiol. 2003;24(10):1958–66. 301. Lin DD, Crawford TO, Barker PB. Proton MR spectroscopy in the diagnostic evaluation of suspected mitochondrial disease. AJNR Am J Neuroradiol. 2003;24(1):33–41. 302. Boddaert N, Romano S, Funalot B, Rio M, Sarzi E, Lebre AS, Bahi-Buisson N, Valayannopoulos V, Desguerre I, Seidenwurm D, Brunelle F, Brami-Zylberberg F, Rotig A, Munnich A, de Lonlay P. 1H MRS spectroscopy evidence of cerebellar high lactate in mitochondrial respiratory chain deficiency. Mol Genet Metab. 2008;93(1):85–8. 303. Inao S, Marmarou A, Clarke GD, Andersen BJ, Fatouros PP, Young HF.  Production and clearance of lactate from brain tissue, cerebrospinal fluid, and serum following experimental brain injury. J Neurosurg. 1988;69(5):736–44. 304. Dinopoulos A, Cecil KM, Schapiro MB, Papadimitriou A, Hadjigeorgiou GM, Wong B, de Grauw T, Egelhoff JC. Brain MRI and proton MRS findings in infants and children with respiratory chain defects. Neuropediatrics. 2005;36(5):290–301. 305. Phelan JA, Lowe LH, Glasier CM.  Pediatric neurodegenerative white matter processes: leukodystrophies and beyond. Pediatr Radiol. 2008;38(7):729–49. 306. Eichler FS, Barker PB, Cox C, Edwin D, Ulug AM, Moser HW, Raymond GV.  Proton MR spectroscopic imaging predicts lesion progression on MRI in X-linked adrenoleukodystrophy. Neurology. 2002;58(6):901–7. 307. Oz G, Tkac I, Charnas LR, Choi IY, Bjoraker KJ, Shapiro EG, Gruetter R.  Assessment of adrenoleukodystrophy lesions by high field MRS in non-sedated pediatric patients. Neurology. 2005;64(3):434–41. 308. Bizzi A, Castelli G, Bugiani M, Barker PB, Herskovits EH, Danesi U, Erbetta A, Moroni I, Farina L, Uziel G. Classification of childhood white matter disorders using proton MR spectroscopic imaging. AJNR Am J Neuroradiol. 2008;29(7):1270–5. 309. Haussinger D, Schliess F.  Astrocyte swelling and protein tyrosine nitration in hepatic encephalopathy. Neurochem Int. 2005;47(1-2):64–70. 310. Vaquero J, Butterworth RF. The brain glutamate system in liver failure. J Neurochem. 2006;98(3):661–9. 311. Kreis R, Ross BD, Farrow NA, Ackerman Z. Metabolic disorders of the brain in chronic hepatic encephalopathy detected with H-1 MR spectroscopy. Radiology. 1992;182(1):19–27.

10  Magnetic Resonance Spectroscopy: Clinical Applications 312. Ross BD, Danielsen ER, Bluml S.  Proton magnetic resonance spectroscopy: the new gold standard for diagnosis of clinical and subclinical hepatic encephalopathy? Dig Dis. 1996;14(1):30–9. 313. Taylor-Robinson SD, Buckley C, Changani KK, Hodgson HJ, Bell JD. Cerebral proton and phosphorus-31 magnetic resonance spectroscopy in patients with subclinical hepatic encephalopathy. Liver. 1999;19(5):389–98. 314. Mechtcheriakov S, Schocke M, Kugener A, et al. Chemical shift magnetic resonance spectroscopy of cingulate grey matter in patients with minimal hepatic encephalopathy. Neuroradiology. 2005;47(1):27–34. 315. Binesh N, Huda A, Thomas MA, Wyckoff N, Bugbee M, Han S, Rasgon N, Davanzo P, Sayre J, Guze B, Martin P, Fawzy F. Hepatic encephalopathy: a neurochemical, neuroanatomical, and neuropsychological study. J Appl Clin Med Phys. 2006;7(1):86–96. 316. Hass HG, Nagele T, Seeger U, Hosl F, Gregor M, Kaiser S. Detection of subclinical and overt hepatic encephalopathy and treatment control after L-ornithine-L-aspartate medication by magnetic resonance spectroscopy ((1)H-MRS). Z Gastroenterol. 2005;43(4):373–8. 317. Naegele T, Grodd W, Viebahn R, Seeger U, Klose U, Seitz D, Kaiser S, Mader I, Mayer J, Lauchart W, Gregor M, Voigt K. MR imaging and (1)H spectroscopy of brain metabolites in hepatic encephalopathy: time-course of renormalization after liver transplantation. Radiology. 2000;216(3):683–91. 318. Cha S.  Neuroimaging in neuro-oncology. Neurotherapeutics. 2009;6(3):465–77. 319. Omuro AM, Leite CC, Mokhtari K, Delattre JY.  Pitfalls in the diagnosis of brain tumours. Lancet Neurol. 2006;5(11):937–48. 320. Hollingworth W, Medina LS, Lenkinski RE, Shibata DK, Bernal B, Zurakowski D, Comstock B, Jarvik JG. A systematic literature review of magnetic resonance spectroscopy for the characterization of brain tumors. AJNR Am J Neuroradiol. 2006;27(7):1404–11. 321. Horská A, Barker PB.  Imaging of brain tumors: MR spectroscopy and metabolic imaging. Neuroimaging Clin N Am. 2010;20(3):293–310. 322. Moller-Hartmann W, Herminghaus S, Krings T, Marquardt G, Lanfermann H, Pilatus U, Zanella FE. Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology. 2002;44(5):371–81. 323. Howe FA, Opstad KS. 1H MR spectroscopy of brain tumours and masses. NMR Biomed. 2003;16(3):123–31. 324. Chawla S, Wang S, Wolf RL, Woo JH, Wang J, O’Rourke DM, Judy KD, Grady MS, Melhem ER, Poptani H.  Arterial spin-­ labeling and MR spectroscopy in the differentiation of gliomas. AJNR Am J Neuroradiol. 2007;28(9):1683–9. 325. Bruhn H, Frahm J, Gyngell ML, Merboldt KD, Hanicke W, Sauter R, Hamburger C. Noninvasive differentiation of tumors with use of localized H-1 MR spectroscopy in  vivo: initial experience in patients with cerebral tumors. Radiology. 1989;172(2):541–8. 326. Langkowski JH, Wieland J, Bomsdorf H, Leibfritz D, Westphal M, Offermann W, Maas R. Pre-operative localized in vivo proton spectroscopy in cerebral tumors at 4.0 Tesla–first results. Magn Reson Imaging. 1989;7(5):547–55. 327. Tamiya T, Kinoshita K, Ono Y, Matsumoto K, Furuta T, Ohmoto T.  Proton magnetic resonance spectroscopy reflects cellular proliferative activity in astrocytomas. Neuroradiology. 2000;42(5):333–8. 328. Shimizu H, Kumabe T, Shirane R, Yoshimoto T.  Correlation between choline level measured by proton MR spectroscopy and Ki-67 labeling index in gliomas. AJNR Am J Neuroradiol. 2000;21(4):659–65. 329. Tedeschi G, Lundbom N, Raman R, Bonavita S, Duyn JH, Alger JR, Di Chiro G.  Increased choline signal coinciding with malignant degeneration of cerebral gliomas: a serial proton

289 magnetic resonance spectroscopy imaging study. J Neurosurg. 1997;87(4):516–24. 330. Hourani R, Brant LJ, Rizk T, Weingart JD, Barker PB, Horska A. Can proton MR spectroscopic and perfusion imaging differentiate between neoplastic and nonneoplastic brain lesions in adults? AJNR Am J Neuroradiol. 2008;29(2):366–72. 331. Alger JR, Frank JA, Bizzi A, Fulham MJ, DeSouza BX, Duhaney MO, Inscoe SW, Black JL, van Zijl PC, Moonen CT. Metabolism of human gliomas: assessment with H-1 MR spectroscopy and F-18 fluorodeoxyglucose PET. Radiology. 1990;177(3):633–41. 332. Barker PB, Glickson JD, Bryan RN. In vivo magnetic resonance spectroscopy of human brain tumors. Top Magn Reson Imaging. 1993;5(1):32–45. 333. Herholz K, Heindel W, Luyten PR, den Hollander JA, Pietrzyk U, Voges J, Kugel H, Friedmann G, Heiss WD. In vivo imaging of glucose consumption and lactate concentration in human gliomas. Ann Neurol. 1992;31(3):319–27. 334. Negendank W, Sauter R. Intratumoral lipids in 1H MRS in vivo in brain tumors: experience of the Siemens cooperative clinical trial. Anticancer Res. 1996;16(3B):1533–8. 335. Howe FA, Barton SJ, Cudlip SA, Stubbs M, Saunders DE, Murphy M, Wilkins P, Opstad KS, Doyle VL, McLean MA, Bell BA, Griffiths JR. Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med. 2003;49(2):223–32. 336. Majós C, Alonso J, Aguilera C, et al. Utility of proton MR spectroscopy in the diagnosis of radiologically atypical intracranial meningiomas. Neuroradiology. 2003;45(3):129–36. 337. Kuesel AC, Sutherland GR, Halliday W, Smith IC. 1H MRS of high grade astrocytomas: mobile lipid accumulation in necrotic tissue. NMR Biomed. 1994;7(3):149–55. 338. Di Costanzo A, Scarabino T, Trojsi F, Popolizio T, Catapano D, Giannatempo GM, Bonavita S, Portaluri M, Tosetti M, d’Angelo VA, Salvolini U, Tedeschi G. Proton MR spectroscopy of cerebral gliomas at 3 T: spatial heterogeneity, and tumour grade and extent. Eur Radiol. 2008;18(8):1727–35. 339. Hattingen E, Raab P, Franz K, Lanfermann H, Setzer M, Gerlach R, Zanella FE, Pilatus U.  Prognostic value of choline and creatine in WHO grade II gliomas. Neuroradiology. 2008;50(9): 759–67. 340. Castillo M, Smith JK, Kwock L. Correlation of myo-inositol levels and grading of cerebral astrocytomas. AJNR Am J Neuroradiol. 2000;21(9):1645–9. 341. Saraf-Lavi E, Bowen BC, Pattany PM, Sklar EM, Murdoch JB, Petito CK. Proton MR spectroscopy of gliomatosis cerebri: case report of elevated myoinositol with normal choline levels. AJNR Am J Neuroradiol. 2003;24(5):946–51. 342. Majos C, Aguilera C, Cos M, Camins A, Candiota AP, Delgado-­Goni T, Samitier A, Castaner S, Sanchez JJ, Mato D, Acebes JJ, Arus C.  In vivo proton magnetic resonance spectroscopy of intraventricular tumours of the brain. Eur Radiol. 2009;19(8):2049–59. 343. Costanzo A, Scarabino T, Trojsi F, et al. Multiparametric 3T MR approach to the assessment of cerebral gliomas: tumor extent and malignancy. Neuroradiology. 2006;48(9):622–31. 344. Remy C, Grand S, Lai ES, Belle V, Hoffmann D, Berger F, Esteve F, Ziegler A, Le Bas JF, Benabid AL, Decorps M, Segebarth CM. 1H MRS of human brain abscesses in  vivo and in  vitro. Magn Reson Med. 1995;34(4):508–14. 345. Garg M, Gupta RK.  MR spectroscopy in intracranial infection. In: Gillard JH, Waldman AD, Barker PB, editors. Clinical MR neuroimaging: diffusion, perfusion and spectroscopy. Cambridge: Cambridge University Press; 2004. p. 380–406. 346. Saindane AM, Cha S, Law M, Xue X, Knopp EA, Zagzag D. Proton MR spectroscopy of tumefactive demyelinating lesions. AJNR Am J Neuroradiol. 2002;23(8):1378–86.

290 347. Al-Okaili RN, Krejza J, Wang S, Woo JH, Melhem ER. Advanced MR imaging techniques in the diagnosis of intraaxial brain tumors in adults. Radiographics. 2006;26(Suppl 1):S173–89. 348. Butzen J, Prost R, Chetty V, Donahue K, Neppl R, Bowen W, Li SJ, Haughton V, Mark L, Kim T, Mueller W, Meyer G, Krouwer H, Rand S. Discrimination between neoplastic and nonneoplastic brain lesions by use of proton MR spectroscopy: the limits of accuracy with a logistic regression model. AJNR Am J Neuroradiol. 2000;21(7):1213–9. 349. Rand SD, Prost R, Haughton V, Mark L, Strainer J, Johansen J, Kim TA, Chetty VK, Mueller W, Meyer G, Krouwer H. Accuracy of single-voxel proton MR spectroscopy in distinguishing neoplastic from nonneoplastic brain lesions. AJNR Am J Neuroradiol. 1997;18(9):1695–704. 350. De Stefano N, Caramanos Z, Preul MC, Francis G, Antel JP, Arnold DL. In vivo differentiation of astrocytic brain tumors and isolated demyelinating lesions of the type seen in multiple sclerosis using 1H magnetic resonance spectroscopic imaging. Ann Neurol. 1998;44(2):273–8. 351. Venkatesh SK, Gupta RK, Pal L, Husain N, Husain M.  Spectroscopic increase in choline signal is a nonspecific marker for differentiation of infective/inflammatory from neoplastic lesions of the brain. J Magn Reson Imaging. 2001;14(1): 8–15. 352. Wilkinson ID, Griffiths PD, Wales JK. Proton magnetic resonance spectroscopy of brain lesions in children with neurofibromatosis type 1. Magn Reson Imaging. 2001;19(8):1081–9. 353. Vuori K, Kankaanranta L, Hakkinen AM, Gaily E, Valanne L, Granstrom ML, Joensuu H, Blomstedt G, Paetau A, Lundbom N.  Low-grade gliomas and focal cortical developmental malformations: differentiation with proton MR spectroscopy. Radiology. 2004;230(3):703–8. 354. Majos C, Aguilera C, Alonso J, Julia-Sape M, Castaner S, Sanchez JJ, Samitier A, Leon A, Rovira A, Arus C.  Proton MR spectroscopy improves discrimination between tumor and pseudotumoral lesion in solid brain masses. AJNR Am J Neuroradiol. 2009;30(3):544–51. 355. Hourani R, Horska A, Albayram S, Brant LJ, Melhem E, Cohen KJ, Burger PC, Weingart JD, Carson B, Wharam MD, Barker PB. Proton magnetic resonance spectroscopic imaging to differentiate between nonneoplastic lesions and brain tumors in children. J Magn Reson Imaging. 2006;23(2):99–107. 356. Arnold DL, Shoubridge EA, Villemure JG, Feindel W. Proton and phosphorus magnetic resonance spectroscopy of human astrocytomas in vivo. Preliminary observations on tumor grading. NMR Biomed. 1990;3(4):184–9. 357. Gill SS, Thomas DG, Van BN, Gadian DG, Peden CJ, Bell JD, Cox IJ, Menon DK, Iles RA, Bryant DJ. Proton MR spectroscopy of intracranial tumours: in  vivo and in  vitro studies. J Comput Assist Tomogr. 1990;14(4):497–504. 358. Meyerand ME, Pipas JM, Mamourian A, Tosteson TD, Dunn JF.  Classification of biopsy-confirmed brain tumors using single-voxel MR spectroscopy. AJNR Am J Neuroradiol. 1999;20(1):117–23. 359. Preul MC, Leblanc R, Caramanos Z, Kasrai R, Narayanan S, Arnold DL. Magnetic resonance spectroscopy guided brain tumor resection: differentiation between recurrent glioma and radiation change in two diagnostically difficult cases. Can J Neurol Sci. 1998;25(1):13–22. 360. Stadlbauer A, Gruber S, Nimsky C, Fahlbusch R, Hammen T, Buslei R, Tomandl B, Moser E, Ganslandt O. Preoperative grading of gliomas by using metabolite quantification with high-­ spatial-­resolution proton MR spectroscopic imaging. Radiology. 2006;238(3):958–69. 361. Pfisterer WK, Nieman RA, Scheck AC, Coons SW, Spetzler RF, Preul MC. Using ex vivo proton magnetic resonance spectroscopy

A. Horská et al. to reveal associations between biochemical and biological features of meningiomas. Neurosurg Focus. 2010;28(1):E12. 362. Majos C, Alonso J, Aguilera C, Serrallonga M, Coll S, Acebes JJ, Arus C, Gili J.  Utility of proton MR spectroscopy in the diagnosis of radiologically atypical intracranial meningiomas. Neuroradiology. 2003;45(3):129–36. 363. Castillo M, Kwock L. Proton MR spectroscopy of common brain tumors. Neuroimaging Clin N Am. 1998;8(4):733–52. 364. Law M, Cha S, Knopp EA, Johnson G, Arnett J, Litt AW. High-­ grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology. 2002;222(3):715–21. 365. Fan G, Sun B, Wu Z, Guo Q, Guo Y. In vivo single-voxel proton MR spectroscopy in the differentiation of high-grade gliomas and solitary metastases. Clin Radiol. 2004;59(1):77–85. 366. Chiang IC, Kuo YT, Lu CY, Yeung KW, Lin WC, Sheu FO, Liu GC.  Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings. Neuroradiology. 2004;46(8):619–27. 367. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-­ Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW.  The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131(6):803–20. 368. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765–73. 369. Balss J, Meyer J, Mueller W, Korshunov A, Hartmann C, von Deimling A. Analysis of the IDH1 codon 132 mutation in brain tumors. Acta Neuropathol. 2008;116(6):597–602. 370. Parsons DW, Jones S, Zhang X, Lin JC-H, Leary RJ, Angenendt P, Mankoo P, Carter H, Siu IM, Gallia GL, Olivi A, McLendon R, Rasheed BA, Keir S, Nikolskaya T, Nikolsky Y, Busam DA, Tekleab H, Diaz LA, Hartigan J, Smith DR, Strausberg RL, Marie SKN, Shinjo SMO, Yan H, Riggins GJ, Bigner DD, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu VE, Kinzler KW. An integrated genomic analysis of human glioblastoma multiforme. Science. 2008;321(5897):1807–12. 371. Watanabe T, Nobusawa S, Kleihues P, Ohgaki H. IDH1 mutations are early events in the development of astrocytomas and oligodendrogliomas. Am J Pathol. 2009;174(4):1149–53. 372. Ward PS, Patel J, Wise DR, Abdel-Wahab O, Bennett BD, Coller HA, Cross JR, Fantin VR, Hedvat CV, Perl AE, Rabinowitz JD, Carroll M, Su SM, Sharp KA, Levine RL, Thompson CB.  The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting α-ketoglutarate to 2-hydroxyglutarate. Cancer Cell. 2010;17(3):225–34. 373. Dang L, White DW, Gross S, Bennett BD, Bittinger MA, Driggers EM, Fantin VR, Jang HG, Jin S, Keenan MC, Marks KM, Prins RM, Ward PS, Yen KE, Liau LM, Rabinowitz JD, Cantley LC, Thompson CB, Vander Heiden MG, Su SM.  Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature. 2009 Dec 10;462(7274):739–44. 374. Waitkus MS, Diplas BH, Yan H. Isocitrate dehydrogenase mutations in gliomas. Neuro-Oncology. 2016;18(1):16–26. 375. Lehnert W, Hunkler D.  Possibilities of selective screening for inborn errors of metabolism using high-resolution 1H-FT-NMR spectrometry. Eur J Pediatr. 1986;145(4):260–6. 376. Pope WB, Prins RM, Albert Thomas M, Nagarajan R, Yen KE, Bittinger MA, Salamon N, Chou AP, Yong WH, Soto H, Wilson N, Driggers E, Jang HG, Su SM, Schenkein DP, Lai A, Cloughesy TF, Kornblum HI, Wu H, Fantin VR, Liau LM.  Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1

10  Magnetic Resonance Spectroscopy: Clinical Applications mutant glioma patients using magnetic resonance spectroscopy. J Neuro-Oncol. 2012;107(1):197–205. 377. Crisi G, Filice S, Michiara M, Crafa P, Lana S. 2-Hydroxyglutarate detection by short echo time magnetic resonance spectroscopy in routine imaging study of brain glioma at 3.0 T. J Comput Assist Tomogr. 2018;42(3):469–74. 378. Natsumeda M, Igarashi H, Nomura T, Ogura R, Tsukamoto Y, Kobayashi T, Aoki H, Okamoto K, Kakita A, Takahashi H, Nakada T, Fujii Y. Accumulation of 2-hydroxyglutarate in gliomas correlates with survival: a study by 3.0-tesla magnetic resonance spectroscopy. Acta Neuropathol Commun. 2014;2(1):158. 379. Nagashima H, Tanaka K, Sasayama T, Irino Y, Sato N, Takeuchi Y, Kyotani K, Mukasa A, Mizukawa K, Sakata J, Yamamoto Y, Hosoda K, Itoh T, Sasaki R, Kohmura E. Diagnostic value of glutamate with 2-hydroxyglutarate in magnetic resonance spectroscopy for IDH1 mutant glioma. Neuro-Oncology. 2016;18(11):1559–68. 380. Andronesi OC, Kim GS, Gerstner E, Batchelor T, Tzika AA, Fantin VR, Vander Heiden MG, Sorensen AG.  Detection of 2-hydroxyglutarate in IDH-mutated glioma patients by in  vivo spectral-editing and 2D correlation magnetic resonance spectroscopy. Sci Transl Med. 2012;4(116):116. 381. Branzoli F, Di Stefano AL, Capelle L, Ottolenghi C, Valabrègue R, Deelchand DK, Bielle F, Villa C, Baussart B, Lehéricy S, Sanson M, Marjańska M.  Highly specific determination of IDH status using edited in  vivo magnetic resonance spectroscopy. Neuro-­ Oncology. 2018;20(7):907–16. 382. Verma G, Mohan S, Nasrallah MP, Brem S, Lee JY, Chawla S, Wang S, Nagarajan R, Thomas MA, Poptani H.  Non-invasive detection of 2-hydroxyglutarate in IDH-mutated gliomas using two-dimensional localized correlation spectroscopy (2D L-COSY) at 7 Tesla. J Transl Med. 2016;14(1):274. 383. Choi C, Ganji SK, DeBerardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, Yang X-L, Mashimo T, Raisanen JM, Marin-Valencia I, Pascual JM, Madden CJ, Mickey BE, Malloy CR, Bachoo RM, Maher EA. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med. 2012;18(4):624–9. 384. Choi C, Ganji S, Hulsey K, Madan A, Kovacs Z, Dimitrov I, Zhang S, Pichumani K, Mendelsohn D, Mickey B, Malloy C, Bachoo R, DeBerardinis R, Maher E. A comparative study of short- and longTE 1 H MRS at 3 T for in vivo detection of 2-­hydroxyglutarate in brain tumors. NMR Biomed. 2013;26(10):1242–50. 385. Tietze A, Choi C, Mickey B, Maher EA, Parm Ulhøi B, Sangill R, Lassen-Ramshad Y, Lukacova S, Østergaard L, von Oettingen G. Noninvasive assessment of isocitrate dehydrogenase mutation status in cerebral gliomas by magnetic resonance spectroscopy in a clinical setting. J Neurosurg. 2018;128(2):391–8. 386. Zhou M, Zhou Y, Liao H, Rowland BC, Kong X, Arvold ND, Reardon DA, Wen PY, Lin AP, Huang RY.  Diagnostic accuracy of 2-hydroxyglutarate magnetic resonance spectroscopy in newly diagnosed brain mass and suspected recurrent gliomas. Neuro-­ Oncology. 2018;20(9):1262–71. 387. de la Fuente MI, Young RJ, Rubel J, Rosenblum M, Tisnado J, Briggs S, Arevalo-Perez J, Cross JR, Campos C, Straley K, Zhu D, Dong C, Thomas A, Omuro AA, Nolan CP, Pentsova E, Kaley TJ, Oh JH, Noeske R, Maher E, Choi C, Gutin PH, Holodny AI, Yen K, DeAngelis LM, Mellinghoff IK, Thakur SB.  Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-­ mutant glioma. Neuro-Oncology. 2016;18(2):283–90. 388. Berrington A, Voets NL, Plaha P, Larkin SJ, McCullagh J, Stacey R, Yildirim M, Schofield CJ, Jezzard P, Cadouxhudson T, Ansorge O, Emir UE.  Improved localization for 2-­ hydroxyglutarate detection at 3 T using long-TE semiLASER. Tomography. 2016;2(2):94–105.

291 389. Berrington A, Voets NL, Larkin SJ, de Pennington N, McCullagh J, Stacey R, Schofield CJ, Jezzard P, Clare S, Cadoux-Hudson T, Plaha P, Ansorge O, Emir UE. A comparison of 2-­hydroxyglutarate detection at 3 and 7 T with long-TE semi-LASER. NMR Biomed. 2018;31(3):e3886. 390. Bisdas S, Chadzynski GL, Braun C, Schittenhelm J, Skardelly M, Hagberg GE, Ethofer T, Pohmann R, Shajan G, Engelmann J, Tabatabai G, Ziemann U, Ernemann U, Scheffler K.  MR spectroscopy for in  vivo assessment of the oncometabolite 2-­hydroxyglutarate and its effects on cellular metabolism in human brain gliomas at 9.4T. J Magn Reson Imaging. 2016;44(4):823–33. 391. Ganji SK, An Z, Tiwari V, McNeil S, Pinho MC, Pan E, Mickey BE, Maher EA, Choi C. In vivo detection of 2-hydroxyglutarate in brain tumors by optimized point-resolved spectroscopy (PRESS) at 7T. Magn Reson Med. 2017;77(3):936–44. 392. Emir UE, Larkin SJ, de Pennington N, Voets N, Plaha P, Stacey R, Al-Qahtani K, McCullagh J, Schofield CJ, Clare S, Jezzard P, Cadoux-Hudson T, Ansorge O.  Noninvasive quantification of 2-hydroxyglutarate in human gliomas with IDH1 and IDH2 mutations. Cancer Res. 2016;76(1):43–9. 393. Shen X, Voets N, Larkin S, de Pennington N, Plaha P, Stacey R, McCullagh J, Schofield C, Clare S, Jezzard P, Cadoux-Hudson T, Ansorge O, Emir U. A noninvasive comparison study between human gliomas with IDH1 and IDH2 mutations by MR spectroscopy. Meta. 2019;9(2):35. 394. Tiwari V, Daoud EV, Hatanpaa KJ, Gao A, Zhang S, Ganji SK, Jack M, Lewis CM, Askari P, Baxter J, Levy M, Dimitrov I, Thomas BP, Marco C, Madden CJ, Pan E, Patel TR, De Berardinis RJ, Sherry DA, Mickey BE, Malloy CR, Maher EA, Choi C. Glycine by MR spectroscopy is an imaging biomarker of glioma aggressiveness. Neuro-Oncology. 2020;22(7):1018–29. 395. Branzoli F, Pontoizeau C, Tchara L, Di Stefano AL, Kamoun A, Deelchand DK, Valabrègue R, Lehéricy S, Sanson M, Ottolenghi C, Marjańska M. Cystathionine as a marker for 1p/19q codeleted gliomas by in  vivo magnetic resonance spectroscopy. Neuro-­ Oncology. 2019;21(6):765–74. 396. Yang I, Aghi MK. New advances that enable identification of glioblastoma recurrence. Nat Rev Clin Oncol. 2009;6(11):648–57. 397. Taylor JS, Langston JW, Reddick WE, Kingsley PB, Ogg RJ, Pui MH, Kun LE, Jenkins JJ 3rd, Chen G, Ochs JJ, Sanford RA, Heideman RL. Clinical value of proton magnetic resonance spectroscopy for differentiating recurrent or residual brain tumor from delayed cerebral necrosis. Int J Radiat Oncol Biol Phys. 1996;36(5):1251–61. 398. Wald LL, Nelson SJ, Day MR, Noworolski SE, Henry RG, Huhn SL, Chang S, Prados MD, Sneed PK, Larson DA, Wara WM, McDermott M, Dillon WP, Gutin PH, Vigneron DB. Serial proton magnetic resonance spectroscopy imaging of glioblastoma multiforme after brachytherapy. J Neurosurg. 1997;87(4):525–34. 399. Chernov MF, Hayashi M, Izawa M, Usukura M, Yoshida S, Ono Y, Muragaki Y, Kubo O, Hori T, Takakura K. Multivoxel proton MRS for differentiation of radiation-induced necrosis and tumor recurrence after gamma knife radiosurgery for brain metastases. Brain Tumor Pathol. 2006;23(1):19–27. 400. Rock JP, Hearshen D, Scarpace L, Croteau D, Gutierrez J, Fisher JL, Rosenblum ML, Mikkelsen T.  Correlations between magnetic resonance spectroscopy and image-guided histopathology, with special attention to radiation necrosis. Neurosurgery. 2002;51(4):912–9. 401. Li X, Vigneron DB, Cha S, Graves EE, Crawford F, Chang SM, Nelson SJ. Relationship of MR-derived lactate, mobile lipids, and relative blood volume for gliomas in vivo. AJNR Am J Neuroradiol. 2005;26(4):760–9. 402. Pružincová Ľ, Šteňo J, Srbecký M, et al. MR imaging of late radiation therapy- and chemotherapy-induced injury: a pictorial essay. Eur Radiol. 2009;19(11):2716–27.

292 403. Hangel G, Cadrien C, Lazen P, Furtner J, Lipka A, Heckova E, Hingerl L, Motyka S, Gruber S, Strasser B, Kiesel B, Mischkulnig M, Preusser M, Roetzer T, Wohrer A, Widhalm G, Rossler K, Trattnig S, Bogner W.  High-resolution metabolic imaging of high-grade gliomas using 7T-CRT-FID-MRSI. Neuroimage Clin. 2020;28:102433. 404. Wilson M, Andronesi O, Barker PB, Bartha R, Bizzi A, Bolan PJ, Brindle KM, Choi IY, Cudalbu C, Dydak U, Emir UE, Gonzalez RG, Gruber S, Gruetter R, Gupta RK, Heerschap A, Henning A, Hetherington HP, Huppi PS, Hurd RE, Kantarci K, Kauppinen RA, Klomp DWJ, Kreis R, Kruiskamp MJ, Leach MO, Lin AP, Luijten PR, Marjanska M, Maudsley AA, Meyerhoff DJ, Mountford CE, Mullins PG, Murdoch JB, Nelson SJ, Noeske R, Oz G, Pan JW, Peet AC, Poptani H, Posse S, Ratai EM, Salibi N, Scheenen TWJ, Smith ICP, Soher BJ, Tkac I, Vigneron DB, Howe FA.  Methodological consensus on clinical proton MRS of the brain: Review and recommendations. Magn Reson Med. 2019;82(2):527–50. 405. Oz G, Alger JR, Barker PB, Bartha R, Bizzi A, Boesch C, Bolan PJ, Brindle KM, Cudalbu C, Dincer A, Dydak U, Emir UE, Frahm

A. Horská et al. J, Gonzalez RG, Gruber S, Gruetter R, Gupta RK, Heerschap A, Henning A, Hetherington HP, Howe FA, Huppi PS, Hurd RE, Kantarci K, Klomp DW, Kreis R, Kruiskamp MJ, Leach MO, Lin AP, Luijten PR, Marjanska M, Maudsley AA, Meyerhoff DJ, Mountford CE, Nelson SJ, Pamir MN, Pan JW, Peet AC, Poptani H, Posse S, Pouwels PJ, Ratai EM, Ross BD, Scheenen TW, Schuster C, Smith IC, Soher BJ, Tkac I, Vigneron DB, Kauppinen RA. Clinical proton MR spectroscopy in central nervous system disorders. Radiology. 2014;270(3):658–79. 406. Garcia-Gomez JM, Luts J, Julia-Sape M, Krooshof P, Tortajada S, Robledo JV, Melssen W, Fuster-Garcia E, Olier I, Postma G, Monleon D, Moreno-Torres A, Pujol J, Candiota AP, Martinez-­ Bisbal MC, Suykens J, Buydens L, Celda B, Van Huffel S, Arus C, Robles M.  Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. MAGMA. 2009;22(1):5–18. 407. Heerschap A. In vivo magnetic resonance spectroscopy in clinical oncology. In: Shields A, Price P, editors. Cancer drug discovery and development: in vivo imaging of cancer therapy. Totowa: Humana Press Inc; 2007.

Chemical Exchange Saturation Transfer (CEST) Imaging

11

Daniel Paech and Lisa Loi

I ntroduction to Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI) Endogenous low-concentration metabolites or exogenously administered agents, containing either exchangeable protons or molecules, can be imaged using selective radiofrequency (RF) saturation followed by indirect detection via the water signal with large signal amplification [1, 2]. Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) makes use of the spontaneously occurring chemical exchange between solute-bound protons and protons of free bulk water. Magnetization transfer from saturated low-concentration solutes to free water results in a decrease of the water magnetization, which is proportional to the local concentration of the metabolite of interest. Alterations of local tissue properties (e.g., pH changes in ischemic stroke) also affect the proton exchange rates, making CEST MRI additionally sensitive to changes in the microenvironment. In CEST MRI, successive acquisition of off-resonant saturation at different frequency offsets around the water resonance at Δ(Delta)ω(omega)  =  0  ppm yields the so-called Z-spectrum (Fig. 11.1),where the intensity of the water signal is plotted as a function of irradiation frequency defining the water frequency as zero-reference [3]. For an adequate pixel-wise determination of the water resonance, it is essential to correct for B0-field inhomogeneities; e.g., by using the “water saturation shift referencing” or “WASSR” approach [4]. Particularly at ultra-high field strength, additional correction of B1-field inhomogeneities is crucial for the correct interpretation of CEST data as the effect strength strongly depends on the applied saturation power [5].

D. Paech (*) · L. Loi Department of Radiology, German Cancer Research Center, Heidelberg, Germany e-mail: [email protected]; [email protected]; [email protected]

Amide proton transfer (APT) imaging is the most frequently studied CEST contrast resonating around Δ(Delta)ω(omega) = +3.5 ppm relative to the resonance of free water. This technique has first been applied by Zhou et al. to study pH-alterations caused by ischemic stroke [6]. CEST signal intensities are most commonly quantified by calculating the magnetization transfer ratio asymmetry (MTRasym). This approach is based on the assumption that magnetization transfer effects are symmetric with respect to water resonance. Consequently, e.g., APT CEST effects at Δ(Delta)ω(omega) = +3.5 ppm result in a positive magnetization transfer difference, the so-called APT-weighted (APT-­ w) CEST contrast [6]:

MTR asym ( ∆ω ) =  Ssat ( −∆ω ) − Ssat ( + ∆ω )  / S0

For APT-w CEST imaging based on the MTRasym approach, relatively high RF saturation power (e.g., B1  =  2.1  μ[mu]T) has been reported to maximize the APT effect [7]. In contrast, detection of relayed Nuclear Overhauser Effect (rNOE)-mediated CEST effects, located upfield from the water resonance at approximately at Δ(Delta)ω(omega)  =  −2 to −5  ppm, is improved by using lower saturation power (e.g., B1 = 0.6 μ[mu]T) [8, 9]. Besides the APT effect around +3.5 ppm from water, multiple exchangeable groups (resonating between 1 and 6 ppm from water) may contribute to the MTRasym contrast [2]. In addition, contributions from T1- and T2-relaxation [10, 11], conventional semi-solid magnetization transfer (MT) effects [12], and downfield rNOE CEST signals [13] affect the MTRasym metric. Therefore, more sophisticated approaches have been proposed enabling separation of multiple CEST pools, e.g., by using Lorentzian fit analysis on data with sufficiently high spectral resolution (Fig. 11.1) [9, 14, 15]. Direct water proton saturation (spillover) and semi-solid MT effects can be reduced by applying the inverse difference metric introduced by Zaiss et al. in 2013 [16]. Further, correction of T1-relaxation time can be achieved by using the relaxation-compensated metric, which yields the apparent

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Faro, F. B. Mohamed (eds.), Functional Neuroradiology, https://doi.org/10.1007/978-3-031-10909-6_11

293

294

D. Paech and L. Loi 1 water

0.9 0.8

Z (∆ω) [a.u.]

0.7

Data Fit

0.6 0.5 0.4 0.3

rNOE

0.2

amides

amines

MT

0.1 0

10

8

6

4

2

0 ∆ω [ppm]

–2

–4

–6

–8

–10

Fig. 11.1  Z-spectrum acquired in the human brain of a healthy volunteer at 7.0 T. The most prominent resonances at positive frequencies can be assigned to the amide protons (dark blue line) and amines (orange line) resonating around Δ(Delta)ω(omega)  =  +3.5  ppm and Δ(Delta)ω(omega) = +2.2 ppm. A broader resonance can be observed upfield from the water resonance (Δ[Delta]ω[omega]  =  − 2  ppm to −5  ppm), which is caused by relayed nuclear Overhauser effects

(rNOE, green line). The water resonance (per definition at 0 ppm, light blue line) and the very broad semi-solid magnetization transfer (MT, red line) are additionally fitted by Lorentzian functions. Technical parameters: Read out: B0 = 7 T, FoV 195 × 170, matrix 128 × 112, slice thickness 5 mm, TE = 3.61 ms, TR = 7.4 ms, flip angle 10°. Saturation: 120 Gaussian-shaped pulses à 15  ms; B1  =  0.9  μ(mu)T; in total 60 offsets

exchange-dependent relaxation rate (AREX) [10, 14, 17–19]. Regarding the last mentioned correction method, it has been shown that T1-normalization may not be necessary in clinical MRI systems with lower field strength, when appropriate sequence parameters are chosen [20]. Ultimately, downfield resonating rNOE effects can be estimated and reduced yielding the downfield rNOE suppressed (dns-) APT metric [13]. Future prospective large cohort clinical studies need to investigate whether the interfering effects in conventional APT-wMTRasym contrasts add up constructively or if more complex approaches are needed to increase the specificity and the diagnostic value of CEST MRI. Recently, consensus recommendations on clinical APTw imaging approaches have been provided by leading experts in the field to provide a rationale for optimized APTw imaging at 3 T [21].

alterations detected with CEST MRI. The following sections of this chapter provide an overview of the studies published in the literature (see Table 11.1 for summary of key studies).

Neurooncological Applications of CEST MRI Neurooncological imaging represents one of the major fields of CEST MRI applications. There is a fast growing body of evidence that APT(-w) imaging may add complementary information when included in conventional MRI protocols [22, 23]. CEST MRI approaches have been shown to allow differentiation between different World Health Organization (WHO) tumor grades and molecular tumor subtypes. Furthermore, early therapy response and prognostication have been shown to be associated with protein concentration

 ndogenous CEST Contrasts E in Neurooncological Applications In 2003, Zhou et al. described for the first time the application of APT-w CEST MRI in brain tumors [24]. The study was conducted in a rat 9 L gliosarcoma model and showed increased APT signal intensities that were assigned to malignant brain tumor tissue. Since then, APT-w MRI has intensively been investigated, both in animal studies and patients with brain tumors. Increased APT signals in tumors are generally explained by higher concentrations of mobile proteins and peptides in malignant tumors [22, 25, 26]. APT signals have been shown to positively correlate with WHO tumor grade in preoperative imaging of glioma patients [27–30]. In particular, the ability of APT-w CEST MRI to differentiate between high- and low-grade glioma was also reported for non-enhancing lesions [31]. Multiple studies found a positive correlation of APT signals with cell density and proliferation (Ki-67-index) in brain tumors [27, 28, 32–34]. Investigations of CEST MRI at ultra-high field strength (7 T) recently confirmed these findings employing the relaxation-­ compensated and downfield rNOE suppressed (dns-) APT metric in patients with glioma [35] (Fig. 11.2).

11  Chemical Exchange Saturation Transfer (CEST) Imaging

295

Table 11.1  Summary of key literature Authors Ward, KM et al., 2000

Article A new class of contrast agents for MRI based on proton chemical exchange dependent saturation transfer (CEST). J Magn Reson. 2000 Mar;143(1):79–87

Zhang et al., 2001 Zhou, J et al., 2003 Zhou, J et al., 2003 Van Zijl, PCM et al., 2007 Sun, PZ et al., 2007

A novel europium (III)-based MRI contrast agent. Journal of the American Chemical Society, 123(7), 1517–1518 Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nature medicine. 2003;9(8):1085–90 Amide proton transfer (APT) contrast for imaging of brain tumors. Magn Reson Med. 2003 Dec;50(6):1120–6

Summary First article proposing to use exchangeable protons for MRI contrast under physiological conditions and introduced the idea of using exogenous compounds as CEST agents for MRI. In vitro demonstration that selective radiofrequency (RF) saturation enables detection of protons of interest The first demonstration of a paramagnetic CEST agent as a MRI agent First application of pH-sensitive APT CEST imaging for detecting acute stroke in ischemic rat models

Initial application of APT CEST MRI in brain tumors (preclinical). Demonstration that APT MRI reflects endogenous cellular protein and peptide content in intracranial rat 9 L gliosarcomas MRI detection of glycogen in vivo by using chemical exchange In vitro and in vivo detection of glycogen using CEST MRI. Glycogen metabolism could be followed in isolated, saturation transfer imaging (glycoCEST). Proceedings of the perfused mouse livers at 4.7 T before and after administration of National Academy of Sciences, 104(11), 4359–4364 glucagon Twenty-one rats underwent permanent middle cerebral artery Detection of the ischemic penumbra using pH-weighted occlusion and ischemic evolution over the first 3.5 h postMRI. Journal of Cerebral Blood Flow & Metabolism, 27(6), occlusion was studied using multiparametric MRI. The study 1129–1136 showed that pH-weighted CEST MRI provides information complementary to PWI and DWI in the delineation of ischemic tissue Review article focusing on the basic magnetic resonance principles Van Zijl, Chemical exchange saturation transfer (CEST): what is in a name and what isn’t? Magnetic resonance in medicine, 65(4), underlying CEST and similarities to and differences with PCM conventional magnetization transfer contrast. The basic theory, 927–948 et al., design criteria, and experimental issues for exchange transfer 2011 imaging are discussed Investigation of the possibility of using simple D-glucose as an Natural D-glucose as a biodegradable MRI contrast agent for Chan, infusable biodegradable MRI agent for cancer detection in two detecting cancer. Magnetic resonance in medicine, 68(6), KW human breast cancer cell lines, MDA-MB-231 and MCF-7, 1764–1773 et al., implanted orthotopically in nude mice 2012 Walker-­ In vivo imaging of glucose uptake and metabolism in tumors. Demonstration that glucose chemical exchange saturation transfer (glucoCEST) is sensitive to tumor glucose accumulation in Samuel, Nature medicine, 19(8), 1067–1072 colorectal tumor models and allows distinguishing tumor types S et al., with differing metabolic characteristics and pathophysiologies 2013 Haris M Imaging of glutamate neurotransmitter alterations in Application of glutamate-­sensitive CEST MRI (GluCEST) to Alzheimer’s disease. NMR Biomed. 2013;26(4):386–91 et al., detect early stages of Alzheimer’s disease in the brain of APP-PS1 2013 transgenic mouse models Review article considering analytical solutions of the Bloch– Zaiss, M Chemical exchange saturation transfer (CEST) and MR Z-spectroscopy in vivo: a review of theoretical approaches and McConnell (BM) equation system for the theoretical description et al., of CEST and the equivalent off-resonant spinlock (SL) methods. Physics in Medicine & Biology, 58(22), R221 2013 experiments. Overview of reported CEST effects in vivo and applications on clinical MRI systems The ability of APT imaging to predict the histological grade of Togao, O Amide proton transfer imaging of adult diffuse gliomas: adult diffuse gliomas was tested in a cohort of 36 patients with correlation with histopathological grades. Neuro-oncology, et al., histopathologically proven diffuse glioma 16(3), 441–448 2014 Feasibility study on the application of CEST MRI to detect Chemical exchange saturation transfer MR imaging of Li C Parkinson’s disease in 27 patients and 22 healthy controls at Parkinson’s disease at 3 Tesla. Eur Radiol. et al., 2014;24(10):2631–9 2014 3 T. Region-­specific investigation of CEST signals in the substantia nigra and the basal ganglia of Parkinson’s disease patients compared to normal controls Investigation of APT CEST and GlucoCEST MRI in the rTg4510 Wells JA In vivo imaging of tau pathology using multi-parametric quantitative MRI. Neuroimage. 2015;111:369–78 mouse model of tauopathy to assess neurodegenerative diseases et al., 2015 Glutamate chemical exchange saturation transfer (GluCEST) MRI Glutamate imaging (GluCEST) lateralizes epileptic foci in Davis was applied to patients with non-lesional temporal lobe epilepsy nonlesional temporal lobe epilepsy. Sci Transl Med. 2015; et al., based on conventional MRI and its feasibility to correctly 7(309):309ra161. 2015 lateralize the temporal lobe seizure focus on glutamate-­based images was shown (continued)

D. Paech and L. Loi

296 Table 11.1 (continued) Authors Zaiss, M et al., 2015

Article Relaxation-compensated CEST-MRI of the human brain at 7 T: unbiased insight into NOE and amide signal changes in human glioblastoma. Neuroimage, 112, 180–188

Summary Correction algorithm to compensate semi-solid magnetization transfer (MT), as well as T1 scaling and spillover in CEST data yielding the apparent exchange-dependent relaxation (AREX). First application to a study cohort of ten patients with glioblastoma Xu, X Dynamic glucose-­enhanced (DGE) MRI: translation to human Dynamic glucose enhanced (DGE) image data from four normal et al., scanning and first results in glioma patients. Tomography, 1(2), volunteers and three glioma patients showed a strong signal 2015 enhancement in blood vessels, while a spatially varying 105 enhancement was found in brain tumors Paech, D T1ρ-weighted dynamic glucose-enhanced MR imaging in the Adiabatically prepared chemical exchange–sensitive spin-lock imaging at 7.0 T performed in nine patients with glioblastoma and et al., human brain. Radiology, 285(3), 914–922 four healthy controls. Pathophysiologically increased glucose 2017 concentration in glioblastoma was demonstrated following intravenous administration O’Grady Glutamate-sensitive imaging and evaluation of cognitive Investigation of glutamate-­sensitive chemical exchange saturation KP et al., impairment in multiple sclerosis. Mult Scler. 2019 transfer (GluCEST) MRI in 20 patients with multiple sclerosis Oct;25(12):1580–1592 2019 revealed increased GluCEST signals in patients with accumulated disability and a positive correlation with symbol digit modality test and choice reaction time scores

Gdce T1-w

T2-w

APT

NOE 20

4

HGG

8

dns-APT

C1

d1

e1

0

f1

g1

20

0 4

LGG

8

0

C2

d2

e2

0

f2

0

g2

0

Fig. 11.2  Predictability of World Health Organization (WHO) tumor grade (HGG vs. LGG) in newly diagnosed untreated glioma. Two patients with HGG (GBM, c1–g1) and LGG (oligodendroglioma II, c2– g2): ci: GdCE T1-w, di: T2-w (TSE), relaxation-compensated multipool CEST MRI at 7 T with APT (ei), NOE (fi), and dns-APT (gi) contrasts shown (unit: %). Only a small spot-like contrast enhancement displays in the tumor region of the patient with HGG (c1), while a clear hyperin-

tensity can be observed at dns-APT imaging (g1, white arrow). (Reproduced with permission from Paech D, Windschuh J, Oberhollenzer J, Dreher C, Sahm F, Meissner JE, et al. Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-compensated multipool CEST MRI at 7.0 T. Neuro Oncol. 2018 Nov 12;20(12):1661–1671)

Another CEST contrast that gained attention in brain tumor imaging is mediated by the relayed nuclear Overhauser enhancement (rNOE)-mediated effect. Decreased rNOE signals are consistently observed in the tumor region of patients with newly diagnosed malignant brain tumors [8, 9, 14]. Further, correlation with tumor grade [36] and cellularity [37] were found for rNOE signals. Thus, both APT(-w) and rNOE imaging may aid more reliable differentiation between tumor and healthy brain tissue.

 ssessment of Histopathologic Tumor Subtypes A with CEST Assessment of genetic tumor subtypes such as isocitrate dehydrogenase (IDH)1 or IDH2 mutations versus wild type forms in patients with glioma are crucial for the therapy planning and prognostication [38]. In clinical routine, invasive procedures such as tumor biopsy or surgical resection are necessary to determine the IDH mutation status, as conventional imaging techniques cannot reliably provide this

11  Chemical Exchange Saturation Transfer (CEST) Imaging

information. Therefore, intensive efforts are made to develop novel imaging approaches that enable noninvasive determination of the IDH mutation status. For instance, magnetic resonance spectroscopy (MRS) has gained considerable attention for the detection of 2-hydroxyglutarate (2-HG) in vivo [39, 40] through its association with the IDH mutation status. However, spatial resolution is limited and the acquisition remains challenging, impeding widespread clinical adoption. Recently, diverse APT CEST MRI approaches have been investigated as alternative noninvasive methods to determine the IDH-mutation status in patients with newly diagnosed glioma [35, 41]. These studies found increased APT signals in IDH wild-type glioma versus tumors with IDH mutation [35, 41]. It is assumed that mutations in IDH gene-encoded enzymes cause disturbances of cellular metabolism, including alteration of amino acid concentrations and global downregulation of protein expression [41]. Supported by the findings of an ultra-high field CEST MRI study, Paech et al. recently suggested that the IDH mutation status may have a stronger influence on the APT signal than the WHO tumor grade [35]. Glioma patients with methylated O6-methylguanine-­ DNA methyltransferase (MGMT) promoter have better outcomes compared to patients with unmethylated MGMT promoter because of the increased chemosensitivity of these tumors [42]. Therefore, MGMT promoter methylation status is another molecular marker of key interest in the diagnostic work-up of glioma patients [35, 43, 44]. Jiang et al. reported significantly decreased APT signals in patients with methylated, compared to unmethylated MGMT promoters [43]. The same trends were also found using the relaxation-­ compensated APT metric at 7 T; however, statistical significance was not reached [35].

297

changes during treatment with temozolomide in an orthotopic tumor mouse model of human glioblastoma [50]. In humans, Park et al. found significantly increased APT signals in tumors of patients with progressive glioma compared to tumors of patients with treatment-related changes [51, 52]. APT values were also reported to be moderately correlated with the choline-to-creatine ratio and moderately correlated with the choline-to-N-acetylaspartate ratio, obtained with magnetic resonance spectroscopy (MRS) [52]. APT- and rNOE-mediated CEST effects were also shown to allow a differentiation of treatment response from progression in glioma patients immediately after or already during radio-chemotherapy [53, 54]. Furthermore, APT and rNOE CEST imaging enabled pre-treatment discrimination of responders to first-line chemo-radiation therapy versus patients with early progression [53, 55]. Recently, a study found early reduction in mean APT-w CEST signals during antiangiogenic treatment (4–6  weeks after initiation) to be associated with treatment response in patients with recurrent glioma [56]. Consistent with these results, APT CEST signals have additionally been shown to be associated with long-term outcome by means of progression-free survival (PFS) and overall survival in patients with newly diagnosed high-grade glioma (WHO grades III–IV) [57].

 xogenous CEST Contrasts: Glucose-Enhanced E MRI of Brain Tumors

The application of contrast agents is of high diagnostic value in neurooncological imaging. MRI contrast agents are generally based on the paramagnetic metal gadolinium (Gd). However, several studies recently reported accumulation of gadolinium in deep gray matter nuclei after serial application of linear gadolinium-based contrast agents (GBCA) [58–63]. Therapy Response Assessment, Prognostication, Moreover, there is a known risk of developing nephrogenic and Outcome Prediction with CEST systemic fibrosis (NSF) for patients with renal failure [64]. Early therapy response assessment and prognostication of Therefore, novel MRI techniques using biodegradable conpatients with glioma are major challenges in clinical routine. trast agents are highly desirable. A promising new approach The updated Response Assessment in Neuro-Oncology is based on the intravenous administration of natural (RANO) criteria require repeated post-treatment MRI exam- D-glucose, which can be detected using CEST [65–67] or inations with gadolinium contrast [45], in order to account chemical exchange sensitive spin-lock (CESL) [68, 69]. The for possible pseudo progression early after treatment principle of these approaches is to measure dynamic signal [45–47]. changes after the intravenous administration of d-glucose (in A preclinical study performed by Zhou et al. in 2011 indi- humans, e.g., 100 mL, concentration: 20% [70]) with high cated that APT-w CEST MRI may enable differentiation temporal and spatial resolution. between tumor recurrence and radiation necrosis in a tumor The ability of these approaches to detect increased glumodel of orthotopic glioma (SF188/V+ glioma and 9 L glio- cose concentrations in tumors was demonstrated in patients sarcoma) in rats [48]. Another animal study demonstrated with glioma at 7  T [70–73]. These studies revealed higher the ability of APT-w MRI to detect early therapy response-­ glucose concentrations in tumor regions compared to healthy related changes in U87 tumor-bearing rats following radio- brain tissue [70–73]. Moreover, increased glucose concentherapy [49]. In accordance, APT-w CEST MRI has been trations were also detected in areas beyond the disrupted shown to be sensitive to early therapy response-induced blood–brain barrier (BBB) (Fig. 11.3).

298

D. Paech and L. Loi

a

b

T2-w

c

GdCE-T1-w

DGEρ fusion

d

ROI #1: Tumor on DGEr ROI #2: Tumor on GdCE-T1w

12

ROI #3: normal appearing WM

DGEρ [%]

8

4

0

glucose injection

–4 0

200

DGEρ (0 s)

e

400

DGEρ (196 s)

f

time [s]

600

DGEρ (399 s)

g

800

1000

DGEρ (588 s)

h

DGEρ (791 s)

i

+10%

–10%

Fig. 11.3 (a) T2-w image acquired at 7  T, (b) Gd-enhanced T1-­ weighted (GdCE-T1w) image acquired at 3 T, (c) fusion of the GdCE-­ T1w image and the T1ρ-weighted dynamic glucose enhancement (DGEρ). (d) DGEρ time curves with a temporal resolution of less than 7 s in a tumor-ROI selected on DGEρ (ROI #1), a second tumor-ROI selected on the GdCE-T1w image (ROI #2), and a ROI in normal appearing white matter (ROI #3). Increasing DGEρ values are obtained in both tumor-ROIs following glucose injection. The red arrow marks an abrupt signal drop induced by patient motion. (e–i) DGEρ images

(average of 5 consecutive images) at different time points after glucose injection. Note the hyperintense region at the bottom of the tumor area (black arrow; (g)), which is not visible in the GdCE-T1w image (b). (Figure reprinted under terms of Creative Commons license from Schuenke P, Paech, D, Koehler C, Windschuh J, Bachert P, Ladd ME, et al. Fast and Quantitative T1ρ-weighted Dynamic Glucose Enhanced MRI. Sci. Rep. 2017;7:42093. http://creativecommons.org/licenses/ by/4.0/)

11  Chemical Exchange Saturation Transfer (CEST) Imaging

The origin of the signal changes in dynamic glucose-­ enhanced (DGE) MRI is still under debate. In general, a glucose concentration change in the intravascular space, the extravascular and extracellular space (EES), and the intracellular space can contribute to the signal. The latest results in brain tumor studies suggest that signal changes on DGE-­ MRI are mainly attributable to BBB leakage and tissue perfusion [70, 72, 74]. Furthermore, both CEST and CESL may be additionally altered by local pH, since an acidic tumor microenvironment can enhance DGE signals through proton exchange rate modulation. Recently, glucose-enhanced CE-sensitive MRI has also been implemented in glioma patients at clinical field strengths of 3 T employing the CEST [75] and CESL [76] techniques. However, small effect sizes were observed at 3 T compared to previous results at 7 T MRI. Therefore, it is currently in question if a robust DCE imaging approach can be established at field strengths less than 7 T.

 H-Sensitive CEST MRI in Neurooncological p Applications Cancer cells commonly show an altered metabolism and tend to have increased intracellular pH values and decreased extracellular pH values [77, 78]. These changes in the tumor microenvironment particularly result from increased expression and/or upregulated activity of H+-ATPases [79–82], Na+-H+ exchangers [83–85], carbonic anhydrases IX and XII [86, 87], monocarboxylate-H+ efflux cotransporters of the SLCA16A family [88, 89], and lactate dehydrogenases [90, 91], which lead to an increased transport of protons (H+) and lactate across the cell membrane in the extracellular space. The reversed proton gradient [92, 93] causes an acidification of the extracellular compartment (pHe) and an alkalization of the intracellular space (pHi) with distinct consequences [93–95]. Thus, pH-weighted contrast methods may represent a valuable imaging technique to gain additional insights into various tumor characteristics. Since the APT exchange rate is known to be base-­ catalyzed for pH values above ∼5 [6, 96, 97], this pH dependence can be used to generate pH-sensitive APT CEST contrasts, first demonstrated by Zhou et al. (2003) in ischemic rat brain models [6]. In contrast to ischemia, tumors show strong alterations of protein and peptide concentrations, which are thought to be the major contributor to the APT-w contrast. Several studies have targeted fast exchanging amine protons (around Δ[Delta]ω[omega] = +3.0 ppm) in order to obtain in vivo pH maps of tumors [98, 99]. As amide and amine groups of neutral amino acids and glutamine are abundant in active tumor regions [100, 101], Harris et al. [98, 99] demonstrated that a pH-weighted CEST contrast can be obtained in patients with glioma by using amine

299

CEST. Decreased pHe values were associated with shorter PFS [98]. Consequently, noninvasive windows into pH alterations in tumors provided by novel imaging techniques may have an impact on early identification of malignant transformation in tumors, therapy planning, and prognostication [92, 102].

 ssessment of Ischemic and Hemorrhagic A Stroke Using CEST MRI Stroke is a frequent neurological disorder and a leading cause of death and disability in the western countries. There are two main types of brain stroke: ischemic (>80%) and hemorrhagic stroke. For both types, early diagnosis and therapy are crucial. In patients with ischemic stroke, an accurate detection of the ischemic penumbra and an early restoration of sufficient blood flow in these areas are essential to limit the extent of tissue damage. Prior to treatment, imaging by means of computed tomography (CT) (currently main modality) and/or MRI using diffusion-weighed imaging (DWI) and perfusion-weighted imaging (PWI) is decisive in cases of suspected stroke. However, on both CT and conventional MRI, a clear differentiation of ischemic acidosis-based penumbra and benign oligemia remains a challenge in the hyperacute stroke period [103, 104].

 H-Sensitive APT Imaging of Acute Ischemic p and Hemorrhagic Stroke APT-w CEST MRI has been demonstrated to enable a detection of ischemia in acute ischemic stroke patients [105–107]. Insufficiently perfused brain tissue becomes acidotic due to an anaerobic metabolism during early ischemia. Consequently, the base-catalyzed exchange rate of amides and free water protons results in decreasing APT signals. Thus, pH-sensitive APT imaging could be of significant diagnostic value for early stroke imaging, as pH-changes are considered to be one of the first tissue changes during hyperacute ischemic stroke. Zhou et al. first applied pH-sensitive APT-w CEST imaging to detect acute stroke in ischemic rat brain models. The pH dependence of the APT signal was calibrated in situ, using phosphorus spectroscopy. Comparison of the MTRasym spectrum between ischemic and contralateral regions showed a reduction for the 2–5  ppm offset range [6]. Since then, APT-w CEST MRI has been intensively investigated in different brain ischemic models [108–113]. Sun et al. applied the pH-sensitive APT-w approach in rats after induction of middle cerebral artery occlusion (MCAO) and found a strong correlation of pH-w signal intensity with tissue lactate content by means of 1H MR spectroscopy [114]. They further

300

D. Paech and L. Loi

demonstrated that several animals solely showed pH alterations and hypoperfusion on cerebral blood flow (CBF) images, while T1- and T2-weighted images were inconspicuous in the hyperacute stage. Moreover, the penumbra detected by pH-sensitive APT-w CEST MRI in the hyperacute period matched very well with the infarcted region on T2-w images after 24 h [115]. These findings suggest that APT-w CEST MRI may enable early differentiation of ischemic tissue, ischemic acidosis penumbra, and benign oligemia in animal models [116–118]. The translation of pH-based imaging technique to clinical applications yielded similar results: In 2011, Zhao et al. first applied pH-sensitive APT-w CEST MRI to four stroke patients at 3  T and found hypointense APT signals in the infarcted region compared to the normal-appearing brain tissue [119]. Tietze et  al. reported significant differences between ischemic brain regions and normal-appearing-­ white-matter in a study cohort of ten acute stroke patients using pH-sensitive APT-w CEST MRI [106]. More recently, changes of tissue pH were explored in ischemic stroke patients at different phases using APT-w CEST MRI. Depending on the onset time (≤6 h: hyperacute stage, 6–48 h: acute stage, 48–96 h: early subacute stage, 96–168 h: T1W1

T2W1

late subacute stage) patients were assigned to four different groups [105]. APT signals were significantly lower in ischemic tissue over all four stages. Moreover, the results indicated that tissue acidification during stroke decreases as the onset to scan time increases [105]. Generally, all investigations of pH-sensitive APT-w CEST MRI in stroke patients demonstrated the ability of identifying ischemic tissue and that the technique may aid differentiation between ischemic core region, acidosis-based penumbra, and benign oligemia in order to improve initial diagnosis and outcome prediction [105, 107, 119–121] (Fig. 11.4).

Intracerebral Hemorrhage In preclinical rat models, the comparison of ischemic brain tissue and hyperacute intracerebral hemorrhage revealed opposite APT signal alterations [122]. While ischemic stroke models showed hypointense contrasts, intracerebral hemorrhage appeared hyperintense compared to contralateral healthy brain tissue. The findings were attributed to the accumulation of red and white blood cells, platelets, and protein-­ rich serums in brain tissue as a consequence of the vessel rupture [122]. This is in line with previous publications describing increased APT signals in blood samples [123]. DWI

APTW

a

b

c

2%

–2%

Fig. 11.4  Conventional MR images and APT-w images of patient with acute ischemic stroke of different National Institutes of Health Stroke Scale (NIHSS) scores and 90-day modified Rankin Scale (mRS) scores. Δ(Delta)APTW = Difference of the APT signal between the acute ischemic region and the contralateral side. (a) M/65  years, NIHSS score was 3 and 90-day mRS score was 0, Δ(Delta)APTW  =  −0.37%. (b) F/69 years, NIHSS score was 5 and 90-day mRS score was 2, Δ(Delta) APTW = 0.82%. (c) M/81 years, NIHSS score was 22 and 90-day mRS

score was 6, Δ(Delta)APTW = 1.93%. Areas of acute ischemic stroke display hypointense on APT-w images. (Reprinted under terms of Creative Commons license from Lin G, Zhuang C, Shen Z, Xiao G, Chen Y, Shen Y, Zong X, Wu R. APT Weighted MRI as an Effective Imaging Protocol to Predict Clinical Outcome After Acute Ischemic Stroke. Front. Neurol. 2018;9:901. https://creativecommons.org/ licenses/by/4.0/ [121])

11  Chemical Exchange Saturation Transfer (CEST) Imaging

The first implementation of APT-w CEST MRI for the early detection of intracerebral hemorrhage in humans was performed by Ma et al. in 2017 [124]. The researchers investigated the APT-w CEST contrast in a study cohort of 33 patients with intracerebral hemorrhage and found significantly increased APT signals in areas of intracerebral hemorrhage at hyperacute, acute, and subacute stages. The authors concluded that APT-wCEST MRI could therefore contribute to noninvasively detect intracerebral hemorrhage at different stages [124].

301

[135–137]. Besides glutamate, myoinositol has been demonstrated as a potential molecular target for CEST imaging in preclinical AD mouse models [138, 139]. This metabolite is considered to be associated with amyloid plaque load, microglial activation, and neuroinflammation [139]. Myoinositol-based CEST (MICEST) MRI was firstly investigated by Haris et al. (2011) in healthy humans at ultra-high field strength (7  T) [140]. In addition, the application of MICEST MRI in an APP-PS1 transgenic mouse model of AD revealed about 50% higher MICEST signals in AD mice compared to wild-type controls, which was consistent with the results obtained through proton spectroscopy and immuCEST MRI of Neurodegenerative Diseases nostaining [138]. Pathological alterations in cerebral d-glucose uptake Alzheimer’s Disease [141] and 2-deoxy-d-glucose (2DG) uptake [142] in AD mouse models were detected using dynamic glucose-­ Alzheimer’s disease (AD) is an irreversible neurological dis- enhanced (DGE) CEST MRI approaches. Recently, on-­ order and the major type of dementia in the elderly. In 2019, resonance variable delay multiple pulse (on VDMP) CEST 46.8  million people worldwide were affected by this age-­ MRI was applied to study d-glucose in a mouse model of AD related disease and AD is gaining increasing importance due tauopathy demonstrating its feasibility in discriminating AD to demographic change [125]. Hence, imaging techniques mice from wild-type mice [141]. for early AD diagnosis and monitoring of disease progression are desired. In several AD animal models, APT-wCEST MRI has been Parkinson’s Disease studied for targeted detection of AD-associated tau-­pathology [126] and amyloid-β(beta) (Aβ[beta]) deposits [127, 128]. Parkinson’s disease (PD) is a common, gradually progressing As a result, significantly reduced APT-w signals were found neurodegenerative disease characterized by a decreased in AD models compared to control groups, which was attrib- dopamine level in the dopaminergic neurons in the substantia uted to the effect of protein aggregation during AD [129]. nigra. Unfortunately, the diagnosis of PD is still based on the Besides protein aggregation, AD can also be associated with clinical manifestations of PD in an advanced stage of the discerebral tissue hypoperfusion and local hypoxia [130] that ease. Therefore, novel imaging techniques are required that additionally result in a reduction of the APT signal due to are sensitive to pathological tissue alterations in the early tissue acidification, as previously discussed in detail. stages of PD. In this context, a feasibility study of CEST MRI The ability of APT(-w) imaging to detect brain tissue to detect PD showed a decrease of the MTRasym value between changes in AD patients and to distinguish between different the offsets of 0 and 4 ppm in regions of the substantia nigra in disease stages was first investigated by Wang et al. in 2015 comparison with normal controls [143]. Furthermore, a pro[131]. In contrast to the animal studies described above, sig- gressive signal intensity decrease from normal controls to nificantly increased APT values were found in the hippocam- early-stage PD and to advanced-stage PD was observed, pus of AD patients compared to normal controls [131]. This which is consistent with the increasing loss of dopaminergic result is consistent with the histological proven accumulation neurons in the course of the disease [144, 145]. PD patients of extracellular amyloid plaques and the appearance of tau with unilateral symptoms showed significantly lower APT-w proteins into intracellular neurofibrillary tangles that are CEST signal intensities in the substantia nigra on the affected characteristic for AD [131]. In addition, APT values were side compared to normal controls [145]. In basal ganglia negatively correlated with patients’ scores in mini-mental (e.g., globus pallidus, putamen, and caudate nucleus) of state examination (MMSE) [131]. patients with PD, increased APT-w signal i­ntensities were Glutamate-sensitive CEST MRI (GluCEST,Δ[Delta]ω[o found, which has been speculated to be caused by an accumumega] = 3 ppm) [132] is another CEST contrast that gained lation of abnormal cytoplasmic proteins [146]. attention as a noninvasive biomarker to detect AD at an early GluCEST MRI has been investigated in a mouse model of stage of disease [133, 134]. Investigations of GluCEST in an PD and elevated glutamate- signals were observed in the striAPP-PS1 transgenic mouse models of AD showed a reduc- atum and motor cortex, which positively correlated with tion of GluCEST signals compared to signals obtained in MRS-derived glutamate concentrations [147, 148]. wild-type controls [133], which is in accordance with studies Furthermore, a negative correlation between striatal GluCEST describing a decrease of glutamate in the early stage of AD signal and motor function was found [148].

302

Multiple Sclerosis With a worldwide prevalence of more than 2.2  million patients and a major cause of disability, multiple sclerosis (MS) is a serious neurodegenerative disease of the central nervous system [149]. MS is characterized by a transmigration of immune cells across the blood–brain barrier and a chronic inflammation. Further, demyelination and axonal degeneration in brain tissue and spinal cord represent key features of MS. In clinical routine, T1- and T2-weighted as well as gadolinium (Gd)-enhanced MRI techniques represent the diagnostic gold standard to detect and monitor progression of the disease. However, morphologically visible changes (BBB disruption and contrast enhancement, demyelination, gliosis, and atrophy) are typically related to an advanced stage of MS, so that conventional MRI is insensitive for pathological changes prior to lesion development. For earlier therapeutic intervention and improved prognosis, reliable identification of biochemical changes during the early course of the disease is crucial [150]. In this context, CEST MRI is suggested to aid detecting early tissue changes in patients with MS. A preclinical study investigated an autoimmune encephalomyelitis (EAE) mouse model prior to the onset of symptoms and found significantly different CEST signals at saturation offsets of 1 and 2 ppm compared to a naïve control group [151]. The expected pathological tissue changes detected by CEST MRI were consistent with follow-up gadolinium-­enhanced MRI at the symptom onset and with immunofluorescent staining that was used to confirm the presence of neuroinflammation. Besides early detection of MS, a recent study has shown the potential of on resonance variable delay multiple pulse (on VDMP) CEST MRI to predict disease progression in EAE models of MS [152]. In patients with MS, Dula et al. found a relatively broad APT signal variation in different lesions; an increase in APT-w signal intensity, relative to healthy tissue, was found in some lesions [153]. Applications of GluCEST MRI in patients with relapsing-remitting MS found a trend toward increased GluCEST signals in the cortical GM of MS patients compared to healthy controls and a significant correlation of GluCEST signals with patient performance in a symbol-­ digit-­modality test and choice-reaction time [154]. These results could be explained by a dysfunctional regulation of glutamate in GM, which is expected to be involved in the pathogenesis of MS [155]. As MS affects the entire central nervous system including the spinal cord, CEST MRI was also applied to assess spinal cord lesions in patients with MS [156, 157]. By et al. reported that respiration correction in the spinal cord is necessary to accurately quantify APT values in MS lesions [157]. The respiration-corrected APT

D. Paech and L. Loi

approach yielded significant differences between normal-­ appearing white matter (NAWM) of MS patients and healthy controls; APT values in MS lesions were not significantly different from NAWM in healthy controls [157].

I maging of Other Neurological Disorders Using Glutamate-Sensitive CEST (GluCEST) MRI Epilepsy Epilepsy is a complex neurological disorder and the fourth most common chronic neurological disease after migraines, Alzheimer’s disease, and Parkinson’s disease [158, 159]. In 60–70% of patients, antiepileptic drugs (AEDs) are effective in suppressing seizures [160]. In patients with drug-resistant epilepsy the identification of epileptogenic brain regions is crucial for a possible neurosurgical resection. Until now, several structural and functional imaging techniques have been applied to localize epileptogenic brain regions, including conventional MRI [161], MRS [162], single-photon emission computed tomography (SPECT) [163], 18-fluoro-­ deoxyglucose positron emission tomography (18F-FDG-PET) [164], and magnetoencephalography (MEG) [165]. However, currently available imaging methods are often not capable of detecting the seizure focus adequately. Both preclinical and human studies provide evidence that glutamatergic dysfunction and elevated glutamate levels are involved in neurological disorders such as epilepsy [166, 167]. Thus, GluCEST MRI may provide valuable information on local alterations of tissue glutamate associated with epileptic foci. In 2015, Davis et al. showed the feasibility of GluCEST MRI to correctly lateralize the temporal lobe seizure focus in patients with previously determined non-­ lesional temporal lobe epilepsy [168]. Furthermore, Neal et al. observed that enhanced peritumoral GluCEST contrasts are associated with recent seizures and drug refractory epilepsy in patients with glioma [169].

Encephalitis Encephalitis is a central nervous system inflammatory disease that is often caused by viral infections (e.g., herpes simplex viruses) and autoimmune processes [170]. In order to adequately counteract disease progression of infectious and autoimmune encephalitis, early diagnosis is mandatory. Recently, the feasibility of GluCEST imaging for the early diagnosis of encephalitis has been investigated in a preclini-

11  Chemical Exchange Saturation Transfer (CEST) Imaging T2w imaging

T2 Flair

303 DWI

GluCEST map 0.05

Encephalitis

0

0.05 0.05

LI group

0

0.05

Fig. 11.5  Conventional T2-w images, T2-Flair, DWI and GluCEST maps of patients with encephalitis and lacunar infarction (LI). (Reprinted under terms of Creative Commons license from Jia Y, Chen Y, Geng K, Cheng Y, Li Y, Qiu J, Huang H, Wang R, Zhang Y, Wu

R.  Glutamate Chemical Exchange Saturation Transfer (GluCEST) Magnetic Resonance Imaging in Pre-clinical and Clinical Applications for Encephalitis. Front. Neurosci.2020;14:750. https://creativecommons.org/licenses/by/4.0/)

cal and clinical setting [171]. Preclinical research on References GluCEST imaging in rats with Staphylococcus aureus-­ 1. Ward KM, Aletras AH, Balaban RS. A new class of contrast agents induced encephalitis was conducted at 7  T, while clinical for MRI based on proton chemical exchange dependent saturation investigations were performed at 3 T [172]. Hyperintensities transfer (CEST). J Magn Reson. 2000;143(1):79–87. on GluCEST contrasts were observed in the affected areas, 2. van Zijl PCM, Yadav NN. Chemical exchange saturation transfer both in mouse models and patients with encephalitis [171, (CEST): what is in a name and what isn’t? Magn Reson Med. 2011;65(4):927–48. 172]. Furthermore, GluCEST MRI has been shown to enable 3. Bryant RG. The dynamics of water-protein interactions. Annu Rev distinguishing between patients with encephalitis and lacuBiophys Biomol Struct. 1996;25(1):29–53. nar infarction [171] (Fig. 11.5). In addition, GluCEST signal 4. Kim M, Gillen J, Landman BA, Zhou J, van Zijl PCM.  Water intensities in patients with encephalitis lesions significantly saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments. Magn Reson Med. decreased after intravenous immunoglobulin therapy com2009;61(6):1441–50. pared to GluCEST values before treatment [171].

Conclusion CEST MRI represents a novel imaging technique providing complementary information to conventional MRI protocols. CEST MRI has proven its value in several neuroradiological applications, especially in neurooncology and cerebral ischemia. Furthermore, the application of multiple CEST approaches in various neurodegenerative diseases and brain disorders, such as MS, epilepsy, and encephalitis has shown the potential of CEST MRI as a noninvasive imaging biomarker that could extent the currently available repertoire of functional and metabolic MRI techniques. However, forthcoming prospective studies in larger study cohorts are needed to prove the added clinical value.

5. Windschuh J, Zaiss M, Meissner JE, Paech D, Radbruch A, Ladd ME, et  al. Correction of B1-inhomogeneities for relaxation-­compensated CEST imaging at 7 T.  NMR Biomed. 2015;28(5):529–37. 6. Zhou J, Payen J-F, Wilson DA, Traystman RJ, van Zijl PCM. Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI. Nat Med. 2003;9(8):1085–90. 7. Zhou J, Hong X, Zhao X, Gao J-H, Yuan J.  APT-weighted and NOE-weighted image contrasts in glioma with different RF ­saturation powers based on magnetization transfer ratio asymmetry analyses. Magn Reson Med. 2013;70(2):320–7. 8. Paech D, Zaiss M, Meissner J-E, Windschuh J, Wiestler B, Bachert P, et  al. Nuclear overhauser enhancement mediated chemical exchange saturation transfer imaging at 7 Tesla in glioblastoma patients. PLoS One. 2014;9(8):e104181. 9. Jones CK, Huang A, Xu J, Edden RA, Schar M, Hua J, et  al. Nuclear overhauser enhancement (NOE) imaging in the human brain at 7T. NeuroImage. 2013;77:114–24. 10. Zaiss M, Xu J, Goerke S, Khan IS, Singer RJ, Gore JC, et  al. Inverse z-spectrum analysis for spillover-, MT-, and T1-corrected

304 steady-state pulsed CEST-MRI—application to pH-weighted MRI of acute stroke. NMR Biomed. 2014;27(3):240–52. 11. Moritz Z, Peter B. Chemical exchange saturation transfer (CEST) and MR z-spectroscopy in vivo: a review of theoretical approaches and methods. Phys Med Biol. 2013;58(22):R221. 12. Zaiss M, Zu Z, Xu J, Schuenke P, Gochberg DF, Gore JC, et al. A combined analytical solution for chemical exchange saturation transfer and semi-solid magnetization transfer. NMR Biomed. 2015;28(2):217–30. 13. Zaiss M, Windschuh J, Goerke S, Paech D, Meissner J-E, Burth S, et al. Downfield-NOE-suppressed amide-CEST-MRI at 7 Tesla provides a unique contrast in human glioblastoma. Magn Reson Med. 2017;77(1):196–208. 14. Zaiss M, Windschuh J, Paech D, Meissner J-E, Burth S, Schmitt B, et al. Relaxation-compensated CEST-MRI of the human brain at 7 T: unbiased insight into NOE and amide signal changes in human glioblastoma. NeuroImage. 2015;112:180–8. 15. Zaiß M, Schmitt B, Bachert P.  Quantitative separation of CEST effect from magnetization transfer and spillover effects by Lorentzian-line-fit analysis of z-spectra. J Magn Reson. 2011;211(2):149–55. 16. Zaiss M, Bachert P. Exchange-dependent relaxation in the rotating frame for slow and intermediate exchange—modeling off-­ resonant spin-lock and chemical exchange saturation transfer. NMR Biomed. 2013;26(5):507–18. 17. Zaiss M, Bachert P. Chemical exchange saturation transfer (CEST) and MRZ-spectroscopy in vivo: a review of theoretical approaches and methods. Phys Med Biol. 2013;58(22):R221–R69. 18. Zimmermann F, Korzowski A, Breitling J, Meissner JE, Schuenke P, Loi L, et  al. A novel normalization for amide proton transfer CEST MRI to correct for fat signal-induced artifacts: application to human breast cancer imaging. Magn Reson Med. 2020;83(3):920–34. 19. Loi L, Zimmermann F, Goerke S, Korzowski A, Meissner JE, Deike-Hofmann K, et al. Relaxation-compensated CEST (chemical exchange saturation transfer) imaging in breast cancer diagnostics at 7T. Eur J Radiol. 2020;129:109068. 20. Zu Z. Towards the complex dependence of MTRasym on T1w in amide proton transfer (APT) imaging. NMR Biomed. 2018;31(7):e3934. 21. Zhou J, Zaiss M, Knutsson L, Sun PZ, Ahn SS, Aime S, et al. Review and consensus recommendations on clinical APTweighted imaging approaches at 3T: application to brain tumors. Magn Reson Med. 2022;88(2):546–74. https://pubmed.ncbi.nlm. nih.gov/35452155/. https://doi.org/10.1002/mrm.29241. 22. Wen Z, Hu S, Huang F, Wang X, Guo L, Quan X, et al. MR imaging of high-grade brain tumors using endogenous protein and peptide-based contrast. NeuroImage. 2010;51(2):616–22. 23. Goerke S, Soehngen Y, Deshmane A, Zaiss M, Breitling J, Boyd PS, et  al. Relaxation-compensated APT and rNOE CEST-MRI of human brain tumors at 3 T.  Magn Reson Med. 2019;82(2):622–32. 24. Zhou J, Lal B, Wilson DA, Laterra J, van Zijl PCM. Amide proton transfer (APT) contrast for imaging of brain tumors. Magn Reson Med. 2003;50(6):1120–6. 25. Jones CK, Schlosser MJ, van Zijl PCM, Pomper MG, Golay X, Zhou J. Amide proton transfer imaging of human brain tumors at 3T. Magn Reson Med. 2006;56(3):585–92. 26. Yan K, Fu Z, Yang C, Zhang K, Jiang S, Lee D-H, et al. Assessing amide proton transfer (APT) MRI contrast origins in 9 L gliosarcoma in the rat brain using proteomic analysis. Mol Imaging Biol. 2015;17(4):479–87. 27. Togao O, Yoshiura T, Keupp J, Hiwatashi A, Yamashita K, Kikuchi K, et al. Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro-Oncology. 2014;16(3):441–8.

D. Paech and L. Loi 28. Bai Y, Lin Y, Zhang W, Kong L, Wang L, Zuo P, et  al. Noninvasive amide proton transfer magnetic resonance imaging in evaluating the grading and cellularity of gliomas. Oncotarget. 2017;8(4):5834–42. 29. Sakata A, Okada T, Yamamoto A, Kanagaki M, Fushimi Y, Okada T, et al. Grading glial tumors with amide proton transfer MR imaging: different analytical approaches. J Neuro-Oncol. 2015;122(2):339–48. 30. Choi YS, Ahn SS, Lee S-K, Chang JH, Kang S-G, Kim SH, et  al. Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume. Eur Radiol. 2017;27(8):3181–9. 31. Togao O, Hiwatashi A, Yamashita K, Kikuchi K, Keupp J, Yoshimoto K, et al. Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging. Eur Radiol. 2017;27(2):578–88. 32. Su C, Liu C, Zhao L, Jiang J, Zhang J, Li S, et  al. Amide proton transfer imaging allows detection of glioma grades and tumor proliferation: comparison with Ki-67 expression and proton MR spectroscopy imaging. Am J Neuroradiol. 2017;38(9):1702–9. 33. Jiang S, Eberhart CG, Zhang Y, Heo H-Y, Wen Z, Blair L, et al. Amide proton transfer-weighted magnetic resonance image-­ guided stereotactic biopsy in patients with newly diagnosed gliomas. Eur J Cancer. 2017;83:9–18. 34. Dreher C, Oberhollenzer J, Meissner J-E, Windschuh J, Schuenke P, Regnery S, et al. Chemical exchange saturation transfer (CEST) signal intensity at 7T MRI of WHO IV° gliomas is dependent on the anatomic location. J Magn Reson Imaging. 2019;49(3):777–85. 35. Paech D, Windschuh J, Oberhollenzer J, Dreher C, Sahm F, Meissner J-E, et  al. Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-­ compensated multi-pool CEST MRI at 7.0 Tesla. Neuro-Oncology. 2018;20(12):1661–71. 36. Heo H-Y, Jones CK, Hua J, Yadav N, Agarwal S, Zhou J, et al. Whole-brain amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging in glioma patients using low-power steady-state pulsed chemical exchange saturation transfer (CEST) imaging at 7T. J Magn Reson Imaging. 2016;44(1):41–50. 37. Paech D, Burth S, Windschuh J, Meissner J-E, Zaiss M, Eidel O, et al. Nuclear overhauser enhancement imaging of glioblastoma at 7 Tesla: region specific correlation with apparent diffusion coefficient and histology. PLoS One. 2015;10(3):e0121220. 38. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, et  al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765–73. 39. Choi C, Ganji SK, DeBerardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, et al. 2-Hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med. 2012;18:624. 40. Pope WB, Prins RM, Albert Thomas M, Nagarajan R, Yen KE, Bittinger MA, et al. Non-invasive detection of 2-­hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy. J Neuro-Oncol. 2012;107(1):197–205. 41. Jiang S, Zou T, Eberhart CG, Villalobos MAV, Heo HY, Zhang Y, et al. Predicting IDH mutation status in grade II gliomas using amide proton transfer-weighted (APTW) MRI. Magn Reson Med. 2017;78(3):1100–9. 42. Hegi ME, Diserens A-C, Gorlia T, Hamou M-F, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997–1003. 43. Jiang S, Rui Q, Wang Y, Heo H-Y, Zou T, Yu H, et al. Discriminating MGMT promoter methylation status in patients with glioblastoma

11  Chemical Exchange Saturation Transfer (CEST) Imaging employing amide proton transfer-weighted MRI metrics. Eur Radiol. 2018;28(5):2115–23. 44. Oue N, Shigeishi H, Kuniyasu H, Yokozaki H, Kuraoka K, Ito R, et al. Promoter hypermethylation of MGMT is associated with protein loss in gastric carcinoma. Int J Cancer. 2001;93(6):805–9. 45. Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et  al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963–72. 46. Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9(5):453–61. 47. de Wit MC, de Bruin HG, Eijkenboom W, Sillevis Smitt PA, van den Bent MJ.  Immediate post-radiotherapy changes in malignant glioma can mimic tumor progression. Neurology. 2004;63(3):535–7. 48. Zhou J, Tryggestad E, Wen Z, Lal B, Zhou T, Grossman R, et al. Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides. Nat Med. 2011;17(1):130–4. 49. Hong X, Liu L, Wang M, Ding K, Fan Y, Ma B, et al. Quantitative multiparametric MRI assessment of glioma response to radiotherapy in a rat model. Neuro-Oncology. 2014;16(6):856–67. 50. Sagiyama K, Mashimo T, Togao O, Vemireddy V, Hatanpaa KJ, Maher EA, et  al. In vivo chemical exchange saturation transfer imaging allows early detection of a therapeutic response in glioblastoma. Proc Natl Acad Sci U S A. 2014;111(12):4542–7. 51. Park KJ, Kim HS, Park JE, Shim WH, Kim SJ, Smith SA. Added value of amide proton transfer imaging to conventional and perfusion MR imaging for evaluating the treatment response of newly diagnosed glioblastoma. Eur Radiol. 2016;26(12):4390–403. 52. Park JE, Kim HS, Park KJ, Kim SJ, Kim JH, Smith SA. Pre- and posttreatment glioma: comparison of amide proton transfer imaging with MR spectroscopy for biomarkers of tumor proliferation. Radiology. 2016;278(2):514–23. 53. Mehrabian H, Myrehaug S, Soliman H, Sahgal A, Stanisz GJ. Evaluation of glioblastoma response to therapy with chemical exchange saturation transfer. Int J Radiat Oncol Biol Phys. 2018;101(3):713–23. 54. Meissner J-E, Korzowski A, Regnery S, Goerke S, Breitling J, Floca RO, et  al. Early response assessment of glioma patients to definitive chemoradiotherapy using chemical exchange saturation transfer imaging at 7 T.  J Magn Reson Imaging. 2019;50(4):1268–77. 55. Regnery S, Adeberg S, Dreher C, Oberhollenzer J, Meissner J-E, Goerke S, et al. Chemical exchange saturation transfer MRI serves as predictor of early progression in glioblastoma patients. Oncotarget. 2018;9(47):28772–83. 56. Park JE, Kim HS, Park SY, Jung SC, Kim JH, Heo HY.  Identification of early response to anti-angiogenic therapy in recurrent glioblastoma: amide proton transfer-weighted and perfusion-weighted MRI compared with diffusion-weighted MRI. Radiology. 2020;295(2):397–406. 57. Paech D, Dreher C, Regnery S, Meissner J-E, Goerke S, Windschuh J, et  al. Relaxation-compensated amide proton transfer (APT) MRI signal intensity is associated with survival and progression in high-grade glioma patients. Eur Radiol. 2019;29(9):4957–67. 58. Radbruch A, Weberling LD, Kieslich PJ, Eidel O, Burth S, Kickingereder P, et al. Gadolinium retention in the dentate nucleus and globus pallidus is dependent on the class of contrast agent. Radiology. 2015;275(3):783–91. 59. Kanda T, Ishii K, Kawaguchi H, Kitajima K, Takenaka D. High signal intensity in the dentate nucleus and globus pallidus on unenhanced T1-weighted MR images: relationship with increasing cumulative dose of a gadolinium-based contrast material. Radiology. 2014;270(3):834–41.

305 60. Kanda T, Osawa M, Oba H, Toyoda K, Kotoku J, Haruyama T, et  al. High signal intensity in dentate nucleus on unenhanced T1-weighted MR images: association with linear versus macrocyclic gadolinium chelate administration. Radiology. 2015;275(3):803–9. 61. McDonald RJ, McDonald JS, Kallmes DF, Jentoft ME, Murray DL, Thielen KR, et  al. Intracranial gadolinium deposition after contrast-enhanced MR imaging. Radiology. 2015;275(3):772–82. 62. Radbruch A, Haase R, Kickingereder P, Bäumer P, Bickelhaupt S, Paech D, et al. Pediatric brain: no increased signal intensity in the dentate nucleus on unenhanced T1-weighted MR images after consecutive exposure to a macrocyclic gadolinium-based contrast agent. Radiology. 2017;283(3):828–36. 63. Radbruch A. Are some agents less likely to deposit gadolinium in the brain? Magn Reson Imaging. 2016;34(10):1351–4. 64. Thomsen HS, Morcos SK, Almén T, Bellin M-F, Bertolotto M, Bongartz G, et al. Nephrogenic systemic fibrosis and gadolinium-­ based contrast media: updated ESUR contrast medium safety committee guidelines. Eur Radiol. 2012;23(2):307–18. 65. Chan KWY, McMahon MT, Kato Y, Liu G, Bulte JWM, Bhujwalla ZM, et al. Natural D-glucose as a biodegradable MRI contrast agent for detecting cancer. Magn Reson Med. 2012;68(6):1764–73. 66. Rivlin M, Horev J, Tsarfaty I, Navon G.  Molecular imaging of tumors and metastases using chemical exchange saturation transfer (CEST) MRI. Sci Rep. 2013;3. 67. Walker-Samuel S, Ramasawmy R, Torrealdea F, Rega M, Rajkumar V, Johnson SP, et al. In vivo imaging of glucose uptake and metabolism in tumors. Nat Med. 2013;19(8):1067–72. 68. Jin T, Mehrens H, Hendrich KS, Kim S-G. Mapping brain glucose uptake with chemical exchange-sensitive spin-lock magnetic resonance imaging. J Cereb Blood Flow Metab. 2014;34(8):1402–10. 69. Schuenke P, Koehler C, Korzowski A, Windschuh J, Bachert P, Ladd ME, et  al. Adiabatically prepared spin-lock approach for T1ρ-based dynamic glucose enhanced MRI at ultrahigh fields. Magn Reson Med. 2017;78(1):215–25. 70. Paech D, Schuenke P, Koehler C, Windschuh J, Mundiyanapurath S, Bickelhaupt S, et al. T1ρ-weighted dynamic glucose-enhanced MR imaging in the human brain. Radiology. 2017;285(3):914–22. 71. Schuenke P, Paech D, Koehler C, Windschuh J, Bachert P, Ladd M, et  al. Fast and quantitative T1ρ-weighted dynamic glucose enhanced MRI. Sci Rep. 2017;7:42093. 72. Xu X, Yadav NN, Knutsson L, Hua J, Kalyani R, Hall E, et  al. Dynamic glucose-enhanced (DGE) MRI: translation to human scanning and first results in glioma patients. Tomography. 2015;1(2):105. 73. Boyd PS, Breitling J, Zimmermann F, Korzowski A, Zaiss M, Schuenke P, et al. Dynamic glucose-enhanced (DGE) MRI in the human brain at 7 T with reduced motion-induced artifacts based on quantitative R1ρ mapping. Magn Reson Med. 2020;84(1):182–91. 74. Tao J, Bistra I, Kevin HT, Michel M, Ping W, Hunter M, et  al. Chemical exchange–sensitive spin-lock (CESL) MRI of glucose and analogs in brain tumors. Magn Reson Med. 2018;80(2):488–95. 75. Xu X, Sehgal AA, Yadav NN, Laterra J, Blair L, Blakeley J, et  al. D-glucose weighted chemical exchange saturation transfer (glucoCEST)-based dynamic glucose enhanced (DGE) MRI at 3T: early experience in healthy volunteers and brain tumor patients. Magn Reson Med. 2020;84(1):247–62. 76. Herz K, Lindig T, Deshmane A, Schittenhelm J, Skardelly M, Bender B, et  al. T1ρ-based dynamic glucose-enhanced (DGEΡ) MRI at 3 T: method development and early clinical experience in the human brain. Magn Reson Med. 2019;82(5):1832–47. 77. Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis? Nat Rev Cancer. 2004;4(11):891–9. 78. Korzowski A, Weinfurtner N, Mueller S, Breitling J, Goerke S, Schlemmer H-P, et al. Volumetric mapping of intra- and extracel-

306 lular pH in the human brain using 31P MRSI at 7T. Magn Reson Med. 2020;84(4):1707–23. 79. Martinez-Zaguilan R, Lynch RM, Martinez GM, Gillies RJ.  Vacuolar-type H (+)-ATPases are functionally expressed in plasma membranes of human tumor cells. Am J Phys Cell Phys. 1993;265(4):C1015–C29. 80. Sennoune SR, Bakunts K, Martínez GM, Chua-Tuan JL, Kebir Y, Attaya MN, et al. Vacuolar H+-ATPase in human breast cancer cells with distinct metastatic potential: distribution and functional activity. Am J Phys Cell Phys. 2004;286(6):C1443–C52. 81. Harguindey S, Orive G, Pedraz JL, Paradiso A, Reshkin SJ. The role of pH dynamics and the Na+/H+ antiporter in the etiopathogenesis and treatment of cancer. Two faces of the same coin—one single nature. Biochim Biophys Acta. 2005;1756(1):1–24. 82. Di Cristofori A, Ferrero S, Bertolini I, Gaudioso G, Russo MV, Berno V, et al. The vacuolar H+ ATPase is a novel therapeutic target for glioblastoma. Oncotarget. 2015;6(19):17514. 83. Miraglia E, Viarisio D, Riganti C, Costamagna C, Ghigo D, Bosia A. Na+/H+ exchanger activity is increased in doxorubicin-resistant human colon cancer cells and its modulation modifies the sensitivity of the cells to doxorubicin. Int J Cancer. 2005;115(6):924–9. 84. Chiang Y, Chou CY, Hsu KF, Huang YF, Shen MR. EGF upregulates Na+/H+ exchanger NHE1 by post-translational regulation that is important for cervical cancer cell invasiveness. J Cell Physiol. 2008;214(3):810–9. 85. Cong D, Zhu W, Shi Y, Pointer KB, Clark PA, Shen H, et  al. Upregulation of NHE1 protein expression enables glioblastoma cells to escape TMZ-mediated toxicity via increased H+ extrusion, cell migration and survival. Carcinogenesis. 2014;35(9):2014–24. 86. Chiche J, Ilc K, Laferrière J, Trottier E, Dayan F, Mazure NM, et al. Hypoxia-inducible carbonic anhydrase IX and XII promote tumor cell growth by counteracting acidosis through the regulation of the intracellular pH. Cancer Res. 2009;69(1):358–68. 87. Swietach Á, Hulikova A, Vaughan-Jones R, Harris A. New insights into the physiological role of carbonic anhydrase IX in tumour pH regulation. Oncogene. 2010;29(50):6509. 88. Miranda-Goncalves V, Honavar M, Pinheiro C, Martinho O, Pires MM, Pinheiro C, et  al. Monocarboxylate transporters (MCTS) in gliomas: expression and exploitation as therapeutic targets. Neuro-Oncology. 2012;15(2):172–88. 89. Pinheiro C, Reis RM, Ricardo S, Longatto-Filho A, Schmitt F, Baltazar F. Expression of monocarboxylate transporters 1, 2, and 4 in human tumours and their association with CD147 and CD44. Biomed Res Int. 2010;2010:427694. 90. Fantin VR, St-Pierre J, Leder P. Attenuation of LDH-A expression uncovers a link between glycolysis, mitochondrial physiology, and tumor maintenance. Cancer Cell. 2006;9(6):425–34. 91. DeBrosse C, Nanga RPR, Bagga P, Nath K, Haris M, Marincola F, et al. Lactate chemical exchange saturation transfer (LATEST) imaging in  vivo: a biomarker for LDH activity. Sci Rep. 2016;6:19517. 92. Wenger KJ, Hattingen E, Franz K, Steinbach JP, Bähr O, Pilatus U.  Intracellular pH measured by 31P-MR-spectroscopy might predict site of progression in recurrent glioblastoma under antiangiogenic therapy. J Magn Reson Imaging. 2017;46(4):1200–8. 93. Fais S, Venturi G, Gatenby B.  Microenvironmental acidosis in carcinogenesis and metastases: new strategies in prevention and therapy. Cancer Metastasis Rev. 2014;33(4):1095–108. 94. Huber V, De Milito A, Harguindey S, Reshkin SJ, Wahl ML, Rauch C, et al. Proton dynamics in cancer. J Transl Med. 2010;8(1):57. 95. Webb BA, Chimenti M, Jacobson MP, Barber DL. Dysregulated pH: a perfect storm for cancer progression. Nat Rev Cancer. 2011;11:671. 96. Sun PZ, Benner T, Copen WA, Sorensen AG. Early experience of translating pH-weighted MRI to image human subjects at 3 Tesla. Stroke. 2010;41(10_suppl_1):S147–51.

D. Paech and L. Loi 97. Bodet O, Goerke S, Behl NG, Roeloffs V, Zaiss M, Bachert P.  Amide proton transfer of carnosine in aqueous solution studied in  vitro by WEX and CEST experiments. NMR Biomed. 2015;28(9):1097–103. 98. Harris RJ, Cloughesy TF, Liau LM, Prins RM, Antonios JP, Li D, et al. pH-weighted molecular imaging of gliomas using amine chemical exchange saturation transfer MRI.  Neuro-Oncology. 2015;17(11):1514–24. 99. Harris RJ, Cloughesy TF, Liau LM, Nghiemphu PL, Lai A, Pope WB, et  al. Simulation, phantom validation, and clinical evaluation of fast pH-weighted molecular imaging using amine chemical exchange saturation transfer echo planar imaging (CEST-EPI) in glioma at 3 T. NMR Biomed. 2016;29(11):1563–76. 100. Souba WW.  Glutamine and cancer. Ann Surg. 1993;218(6):715–28. 101. Kovačević Z, Morris HP. The role of glutamine in the oxidative metabolism of malignant cells. Cancer Res. 1972;32(2):326–33. 102. Maintz D, Heindel W, Kugel H, Jaeger R, Lackner KJ. Phosphorus-31 MR spectroscopy of normal adult human brain and brain tumours. NMR Biomed. 2002;15(1):18–27. 103. Fiehler J, Foth M, Kucinski T, Knab R, von Bezold M, Weiller C, et al. Severe ADC decreases do not predict irreversible tissue damage in humans. Stroke. 2002;33(1):79–86. 104. Rivers CS, Wardlaw JM, Armitage PA, Bastin ME, Carpenter TK, Cvoro V, et al. Do acute diffusion- and perfusion-weighted MRI lesions identify final infarct volume in ischemic stroke? Stroke. 2006;37(1):98–104. 105. Song G, Li C, Luo X, Zhao X, Zhang S, Zhang Y, et al. Evolution of cerebral ischemia assessed by amide proton transfer-weighted MRI. Front Neurol. 2017;8:67. 106. Tietze A, Blicher J, Mikkelsen IK, Ostergaard L, Strother MK, Smith SA, et  al. Assessment of ischemic penumbra in patients with hyperacute stroke using amide proton transfer (APT) chemical exchange saturation transfer (CEST) MRI.  NMR Biomed. 2014;27(2):163–74. 107. Harston GW, Tee YK, Blockley N, Okell TW, Thandeswaran S, Shaya G, et  al. Identifying the ischaemic penumbra using pH-weighted magnetic resonance imaging. Brain. 2015;138(Pt 1):36–42. 108. Wu Y, Zhou IY, Lu D, Manderville E, Lo EH, Zheng H, et al. pH-­ sensitive amide proton transfer effect dominates the magnetization transfer asymmetry contrast during acute ischemia-quantification of multipool contribution to in  vivo CEST MRI.  Magn Reson Med. 2018;79(3):1602–8. 109. Jin T, Wang P, Hitchens TK, Kim SG.  Enhancing sensitivity of pH-weighted MRI with combination of amide and guanidyl CEST. NeuroImage. 2017;157:341–50. 110. Wu L, Jiang L, Sun PZ. Investigating the origin of pH-sensitive magnetization transfer ratio asymmetry MRI contrast during the acute stroke: correction of t(1) change reveals the dominant amide proton transfer MRI signal. Magn Reson Med. 2020;84(5):2702–12. 111. Sun PZ, Wang E, Cheung JS.  Imaging acute ischemic tissue acidosis with pH-sensitive endogenous amide proton transfer (APT) MRI—correction of tissue relaxation and concomitant RF irradiation effects toward mapping quantitative cerebral tissue pH. Neuroimage. 2012;60(1):1–6. 112. Zong X, Wang P, Kim SG, Jin T.  Sensitivity and source of amine-proton exchange and amide-proton transfer magnetic resonance imaging in cerebral ischemia. Magn Reson Med. 2014;71(1):118–32. 113. Jokivarsi KT, Gröhn HI, Gröhn OH, Kauppinen RA. Proton transfer ratio, lactate, and intracellular pH in acute cerebral ischemia. Magn Reson Med. 2007;57(4):647–53. 114. Sun PZ, Cheung JS, Wang E, Lo EH.  Association between pH-­ weighted endogenous amide proton chemical exchange satura-

11  Chemical Exchange Saturation Transfer (CEST) Imaging tion transfer MRI and tissue lactic acidosis during acute ischemic stroke. J Cereb Blood Flow Metab. 2011;31(8):1743–50. 115. Sun PZ, Zhou J, Sun W, Huang J, van Zijl PC. Detection of the ischemic penumbra using pH-weighted MRI. J Cereb Blood Flow Metab. 2007;27(6):1129–36. 116. Zhou J, van Zijl PC. Defining an acidosis-based ischemic penumbra from pH-weighted MRI. Transl Stroke Res. 2011;3(1):76–83. 117. Guo Y, Zhou IY, Chan ST, Wang Y, Mandeville ET, Igarashi T, et  al. pH-sensitive MRI demarcates graded tissue acidification during acute stroke—pH specificity enhancement with magnetization transfer and relaxation-normalized amide proton transfer (APT) MRI. NeuroImage. 2016;141:242–9. 118. Zhou J, Heo HY, Knutsson L, van Zijl PCM, Jiang S.  APT-­ weighted MRI: techniques, current neuro applications, and challenging issues. J Magn Reson Imaging. 2019;50(2):347–64. 119. Zhao X, Wen Z, Huang F, Lu S, Wang X, Hu S, et al. Saturation power dependence of amide proton transfer image contrasts in human brain tumors and strokes at 3 T.  Magn Reson Med. 2011;66(4):1033–41. 120. Msayib Y, Harston GWJ, Tee YK, Sheerin F, Blockley NP, Okell TW, et al. Quantitative CEST imaging of amide proton transfer in acute ischaemic stroke. Neuroimage Clin. 2019;23:101833. 121. Lin G, Zhuang C, Shen Z, Xiao G, Chen Y, Shen Y, et  al. APT weighted MRI as an effective imaging protocol to predict clinical outcome after acute ischemic stroke. Front Neurol. 2018;9:901. 122. Wang M, Hong X, Chang CF, Li Q, Ma B, Zhang H, et  al. Simultaneous detection and separation of hyperacute intracerebral hemorrhage and cerebral ischemia using amide proton transfer MRI. Magn Reson Med. 2015;74(1):42–50. 123. Zheng S, van der Bom IM, Zu Z, Lin G, Zhao Y, Gounis MJ. Chemical exchange saturation transfer effect in blood. Magn Reson Med. 2014;71(3):1082–92. 124. Ma X, Bai Y, Lin Y, Hong X, Liu T, Ma L, et al. Amide proton transfer magnetic resonance imaging in detecting intracranial hemorrhage at different stages: a comparative study with susceptibility weighted imaging. Sci Rep. 2017;7:45696. 125. Anonymous. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 2019;18(1):88–106. 126. Wells JA, O’Callaghan JM, Holmes HE, Powell NM, Johnson RA, Siow B, et al. In vivo imaging of tau pathology using multi-­ parametric quantitative MRI. Neuroimage. 2015;111:369–78. 127. Wang R, Chen P, Shen Z, Lin G, Xiao G, Dai Z, et al. Brain amide proton transfer imaging of rat with Alzheimer’s disease using saturation with frequency alternating RF irradiation method. Front Aging Neurosci. 2019;11:217. 128. Wang R, Wang C, Dai Z, Chen Y, Shen Z, Xiao G, et  al. An amyloid-β targeting chemical exchange saturation transfer probe for in vivo detection of Alzheimer’s disease. ACS Chem Neurosci. 2019;10(8):3859–67. 129. Chen L, Wei Z, Chan KWY, Cai S, Liu G, Lu H, et  al. Protein aggregation linked to Alzheimer’s disease revealed by saturation transfer MRI. NeuroImage. 2019;188:380–90. 130. Yang Y, Zhang J, Xiong L, Deng M, Wang J, Xin J, et  al. Cognitive improvement induced by environment enrichment in chronic cerebral hypoperfusion rats: a result of upregulated endogenous neuroprotection? J Mol Neurosci. 2015;56(2):278–89. 131. Wang R, Li SY, Chen M, Zhou JY, Peng DT, Zhang C, et al. Amide proton transfer magnetic resonance imaging of Alzheimer’s disease at 3.0 Tesla: a preliminary study. Chin Med J. 2015;128(5):615–9. 132. Cai K, Haris M, Singh A, Kogan F, Greenberg JH, Hariharan H, et  al. Magnetic resonance imaging of glutamate. Nat Med. 2012;18(2):302–6. 133. Haris M, Nath K, Cai K, Singh A, Crescenzi R, Kogan F, et al. Imaging of glutamate neurotransmitter alterations in Alzheimer’s disease. NMR Biomed. 2013;26(4):386–91.

307 134. Crescenzi R, DeBrosse C, Nanga RP, Reddy S, Haris M, Hariharan H, et  al. In vivo measurement of glutamate loss is associated with synapse loss in a mouse model of tauopathy. NeuroImage. 2014;101:185–92. 135. Rupsingh R, Borrie M, Smith M, Wells JL, Bartha R.  Reduced hippocampal glutamate in Alzheimer disease. Neurobiol Aging. 2011;32(5):802–10. 136. Antuono PG, Jones JL, Wang Y, Li SJ. Decreased glutamate + glutamine in Alzheimer’s disease detected in vivo with (1)H-MRS at 0.5 T. Neurology. 2001;56(6):737–42. 137. Crescenzi R, DeBrosse C, Nanga RP, Byrne MD, Krishnamoorthy G, D’Aquilla K, et  al. Longitudinal imaging reveals subhippocampal dynamics in glutamate levels associated with histopathologic events in a mouse model of tauopathy and healthy mice. Hippocampus. 2017;27(3):285–302. 138. Haris M, Singh A, Cai K, Nath K, Crescenzi R, Kogan F, et al. MICEST: a potential tool for non-invasive detection of molecular changes in Alzheimer’s disease. J Neurosci Methods. 2013;212(1):87–93. 139. Yanez Lopez M, Pardon MC, Baiker K, Prior M, Yuchun D, Agostini A, et  al. Myoinositol CEST signal in animals with increased Iba-1 levels in response to an inflammatory challenge-­ preliminary findings. PLoS One. 2019;14(2):e0212002. 140. Haris M, Cai K, Singh A, Hariharan H, Reddy R. In vivo mapping of brain myo-inositol. Neuroimage. 2011;54(3):2079–85. 141. Chen L, Wei Z, Chan KW, Li Y, Suchal K, Bi S, et al. D-glucose uptake and clearance in the tauopathy Alzheimer’s disease mouse brain detected by on-resonance variable delay multiple pulse MRI. J Cereb Blood Flow Metab. 2020;41(5):1013–25. 142. Tolomeo D, Micotti E, Serra SC, Chappell M, Snellman A, Forloni G. Chemical exchange saturation transfer MRI shows low cerebral 2-deoxy-d-glucose uptake in a model of Alzheimer’s disease. Sci Rep. 2018;8(1):9576. 143. Li C, Peng S, Wang R, Chen H, Su W, Zhao X, et al. Chemical exchange saturation transfer MR imaging of Parkinson’s disease at 3 Tesla. Eur Radiol. 2014;24(10):2631–9. 144. Li C, Wang R, Chen H, Su W, Li S, Zhao X, et  al. Chemical exchange saturation transfer MR imaging is superior to diffusion-­ tensor imaging in the diagnosis and severity evaluation of Parkinson’s disease: a study on substantia nigra and striatum. Front Aging Neurosci. 2015;7:198. 145. Li C, Chen M, Zhao X, Wang R, Chen H, Su W, et al. Chemical exchange saturation transfer MRI signal loss of the substantia nigra as an imaging biomarker to evaluate the diagnosis and severity of Parkinson’s disease. Front Neurosci. 2017;11:489. 146. Tong J, Wong H, Guttman M, Ang LC, Forno LS, Shimadzu M, et al. Brain alpha-synuclein accumulation in multiple system atrophy, Parkinson’s disease and progressive supranuclear palsy: a comparative investigation. Brain. 2010;133(Pt 1):172–88. 147. Bagga P, Pickup S, Crescenzi R, Martinez D, Borthakur A, D'Aquilla K, et  al. In vivo glucest MRI: reproducibility, background contribution and source of glutamate changes in the MPTP model of Parkinson’s disease. Sci Rep. 2018;8(1):2883. 148. Bagga P, Crescenzi R, Krishnamoorthy G, Verma G, Nanga RP, Reddy D, et  al. Mapping the alterations in glutamate with GluCEST MRI in a mouse model of dopamine deficiency. J Neurochem. 2016;139(3):432–9. 149. Anonymous. Global, regional, and national burden of multiple sclerosis 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 2019;18(3):269–85. 150. Rocca MA, Amato MP, De Stefano N, Enzinger C, Geurts JJ, Penner IK, et  al. Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol. 2015;14(3):302–17. 151. Liu T, Chen Y, Thomas AM, Song X. Cest MRI with distribution-­ based analysis for assessment of early stage disease activity in a mouse model of multiple sclerosis: an initial study. NMR Biomed. 2019;32(11):e4139.

308 152. Thomas AM, Xu J, Calabresi PA, van Zijl PCM, Bulte JWM.  Monitoring diffuse injury during disease progression in experimental autoimmune encephalomyelitis with on resonance variable delay multiple pulse (ONVDMP) CEST MRI. Neuroimage. 2020;204:116245. 153. Dula AN, Asche EM, Landman BA, Welch EB, Pawate S, Sriram S, et al. Development of chemical exchange saturation transfer at 7 T. Magn Reson Med. 2011;66(3):831–8. 154. O’Grady KP, Dula AN, Lyttle BD, Thompson LM, Conrad BN, Box BA, et  al. Glutamate-sensitive imaging and evaluation of cognitive impairment in multiple sclerosis. Mult Scler. 2019;25(12):1580–92. 155. Ciccarelli O, Barkhof F, Bodini B, De Stefano N, Golay X, Nicolay K, et al. Pathogenesis of multiple sclerosis: insights from molecular and metabolic imaging. Lancet Neurol. 2014;13(8):807–22. 156. Dula AN, Pawate S, Dethrage LM, Conrad BN, Dewey BE, Barry RL, et  al. Chemical exchange saturation transfer of the cervical spinal cord at 7 T. NMR Biomed. 2016;29(9):1249–57. 157. By S, Barry RL, Smith AK, Lyttle BD, Box BA, Bagnato FR, et  al. Amide proton transfer CEST of the cervical spinal cord in multiple sclerosis patients at 3t. Magn Reson Med. 2018;79(2):806–14. 158. Hirtz D, Thurman D, Gwinn-Hardy K, Mohamed M, Chaudhuri A, Zalutsky RJN.  How common are the “common” neurologic disorders? Neurology. 2007;68(5):326–37. 159. Thijs RD, Surges R, O’Brien TJ, Sander JW. Epilepsy in adults. Lancet. 2019;393(10172):689–701. 160. Duncan JS, Sander JW, Sisodiya SM, Walker MC. Adult epilepsy. Lancet. 2006;367(9516):1087–100. 161. Camacho DL, Castillo M. MR imaging of temporal lobe epilepsy. Semin Ultrasound CT MR. 2007;28(6):424–36. 162. Aitouche Y, Gibbs SA, Gilbert G, Boucher O, Bouthillier A, Nguyen DK. Proton MR spectroscopy in patients with nonlesional insular cortex epilepsy confirmed by invasive EEG recordings. J Neuroimaging. 2017;27(5):517–23.

D. Paech and L. Loi 163. O’Brien TJ, So EL, Mullan BP, Hauser MF, Brinkmann BH, Bohnen NI, et al. Subtraction ictal SPECT co-registered to MRI improves clinical usefulness of SPECT in localizing the surgical seizure focus. Neurology. 1998;50(2):445–54. 164. la Fougère C, Rominger A, Förster S, Geisler J, Bartenstein P. PET and SPECT in epilepsy: a critical review. Epilepsy Behav. 2009;15(1):50–5. 165. Burgess RC. Magnetoencephalography for localizing and characterizing the epileptic focus. Handb Clin Neurol. 2019;160:203–14. 166. Bradford HF.  Glutamate, GABA and epilepsy. Prog Neurobiol. 1995;47(6):477–511. 167. Çavuş I, Romanyshyn JC, Kennard JT, Farooque P, Williamson A, Eid T, et  al. Elevated basal glutamate and unchanged glutamine and GABA in refractory epilepsy: microdialysis study of 79 patients at the Yale epilepsy surgery program. Ann Neurol. 2016;80(1):35–45. 168. Davis KA, Nanga RP, Das S, Chen SH, Hadar PN, Pollard JR, et  al. Glutamate imaging (GluCEST) lateralizes epileptic foci in nonlesional temporal lobe epilepsy. Sci Transl Med. 2015;7(309):309ra161. 169. Neal A, Moffat BA, Stein JM, Nanga RPR, Desmond P, Shinohara RT, et  al. Glutamate weighted imaging contrast in gliomas with 7  Tesla magnetic resonance imaging. Neuroimage Clin. 2019;22:101694. 170. Kumar R.  Understanding and managing acute encephalitis. F1000Research. 2020;9. 171. Jia Y, Chen Y, Geng K, Cheng Y, Li Y, Qiu J, et  al. Glutamate chemical exchange saturation transfer (GluCEST) magnetic resonance imaging in pre-clinical and clinical applications for encephalitis. Front Neurosci. 2020;14:750. 172. Zhang Z, Bassam B, Thomas AG, Williams M, Liu J, Nance E, et al. Maternal inflammation leads to impaired glutamate homeostasis and up-regulation of glutamate carboxypeptidase II in activated microglia in the fetal/newborn rabbit brain. Neurobiol Dis. 2016;94:116–28.

Part IV Multimodal Functional Neuroradiology

Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

12

Arastoo Vossough and Seyed Ali Nabavizadeh

Introduction Imaging plays an integral role in the management of brain tumors, including tumor diagnosis and classification, treatment planning, and post-treatment surveillance. Conventional magnetic resonance imaging (MRI) with gadolinium-based contrast agents on current high field clinical MR systems provides excellent anatomic and morphologic imaging of brain tumors. Anatomic MRI can determine the location of intracranial masses, presence of edema, mass effect, calcification, cyst formation, hemorrhage, vascularization, and contrast enhancement. Extra-axial and intra-axial brain tumors can also be discriminated quite accurately by anatomic imaging. However, the assessment of tumor type, grade, and extension, and differentiation of tumors from tumor-like conditions can be limited, potentially affecting therapeutic decision-making [1, 2]. Histopathological diagnosis, whether from stereotactic biopsy of masses or surgical resection, remains the reference standard for diagnosis and grading of brain tumors, in conjunction with newer genetic and molecular markers. However, there may be certain limitations. First, if the tissue is obtained by biopsy or incomplete resection, it only provides information about a portion of the neoplasm and not necessarily the entirety of the mass, resulting in potential sampling errors that may lead to inaccurate results, particularly in heterogenous tumors. Additionally, certain lesions cannot be treated surgically and may have a high risk for biopsy. Finally, there is significant variability even among experienced neuropathologists in the diagnosis of certain brain tumors [2]. A. Vossough (*) Department of Radiology, Children’s Hospital of Philadelphia— University of Pennsylvania, Philadelphia, PA, USA e-mail: [email protected] S. A. Nabavizadeh Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA e-mail: [email protected]

There are several advanced MRI techniques that have been used in the past two decades to assess various features of brain tumors. These include diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion imaging, and functional MRI (fMRI). These techniques have become more widespread secondary to emergence of higher magnetic field MR scanners, improved gradient systems, and greater availability of these acquisition and analysis methods. Integration of diagnostic information from various advanced MRI techniques can provide more reliable characterization of intraaxial brain masses, which are utilized in various aspects of brain management including tumor classification and grading, treatment planning, assessing response to treatment, and posttreatment surveillance [1, 3, 4]. In this chapter, first we will briefly review the applications of select advanced MRI techniques (diffusion-weighted imaging, MR spectroscopy, MR perfusion imaging) in brain tumor diagnosis. We will then present a multiparametric algorithmic approach for diagnosis of intra-axial brain masses, and finally conclude this chapter by discussing current challenges.

Magnetic Resonance Spectroscopy Proton magnetic resonance spectroscopy (MRS) is a method that assays a number of chemically distinct proton species present in each voxel by detecting and capitalizing on slight differences in the chemical shift of different metabolites, and generates spectra reflecting their relative quantity in a sampled voxel of tissue. These differences in frequency are displayed on the x-axis of each spectrum in units of parts per million (ppm) of a standard reference compound, rather than in hertz, in order to make comparisons of different spectra taken in different magnetic fields feasible. The y-axis of the graph is often scaled relative to the highest peak, as direct quantification of metabolites is difficult in the clinical setting. MRS technique is adapted to record signals from metabolites present in tissues at much lower concentrations

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Faro, F. B. Mohamed (eds.), Functional Neuroradiology, https://doi.org/10.1007/978-3-031-10909-6_12

311

312

compared to water, which provides the bulk of the signal in conventional MRI.  Therefore, MRS depends on relatively small differences in signal-to-noise ratio and that is why high magnetic field systems have greatly improved MRS in recent years. Different acquisition methods can be used in proton MRS. Using long echo times (TE), the human brain clinical MRS spectrum is dominated by four major metabolites: (1) choline (3.2 ppm), (2) creatine (3.0 ppm), (3) N-acetyl aspartate (2  ppm), and (4) lactate (1.3  ppm). Short TE MRS enables us to define further metabolites with short T2 relaxation times such as myoinositol, lipids, amino acids, and macromolecules, some of which can be important in brain tumor imaging, as will be discussed later [5]. MRS has been used extensively to understand chemical pathology of brain tumors and surrounding tissues. Details of MR spectroscopy techniques are covered in other parts of this book. In this section, we briefly review different application of MRS in brain tumor imaging.

A. Vossough and S. A. Nabavizadeh

often seen in high-grade gliomas and metastases; although it is also seen in some cases of grade II gliomas [11]. Elevation of lipid (Lip) peaks (0.9 ppm and 1.3 ppm) are also a feature of high-grade glioma and metastasis, but is often not seen in low-grade tumors [8, 11]. Lipids are generally better defined at short TE due to their short T2 relaxation time. Myo-­ inositol (mI) peak (3.56 ppm) is generally considered a glial marker. Due to rapid transverse relaxation, it is only observed on short TE spectroscopy. Different investigators have shown high mI in low-grade gliomas and decrease in mI as glioma grade increases [10, 12]. More recently, mutations in the genes isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) have been shown in large percentages of grade II and grade III gliomas and secondary glioblastomas [13, 14]. These are key enzymes in cellular metabolism, epigenetic regulation, DNA repair, and redox states. These mutations cause abnormally high levels of d-2-hydroxyglutarate (2HG) in tumor tissue and are associated with improved responses to tumors. Relevant inhibitors are also being investigated as targets for brain tumor treatMetabolic Markers for Intra-axial Brain Tumors ments. This increased level may be detected in vivo by more advanced spectral-edited magnetic spectroscopy, and be Although numerous peaks are observed in an MRS spec- used for characterization of glial neoplasms. A recent meta-­ trum, the most commonly useful peaks in evaluation of intra-­ analysis demonstrated excellent sensitivity and specificity of axial brain tumors are choline (Cho), creatine (Cr), 2HG MRS for prediction of IDH mutant gliomas [15]. N-acetyl-aspartate (NAA), lactate and lipid (Lac, Lip), and myoinositol (mI) peaks [6]. The choline peak (3.2 ppm) is the sum of several compounds that are active in phospholipid Differential Diagnosis, Preoperative Tumor metabolism (phosphatidylcholine, phosphoethanolamine, Grading, and Biopsy Guidance glycerophosphocholine, and glycerolphosphoethanolamine) [7]. It is generally considered a marker of membrane turn- The role of MRS in differential diagnosis of intra-axial brain over (breakdown and proliferation) and is often increased in masses largely depends on comparison with conventional brain tumors. The total creatine (tCr) peak (3.02 ppm), which and other advanced imaging features, as MRS is rarely speis composed of creatine and phosphocreatine, is a marker of cific enough to support a net diagnosis by itself. One of these metabolic activity. Traditionally, it has been considered a ref- roles is assisting in differentiation of brain abscesses from erence standard for relative quantification of other peaks rim-enhancing brain tumors and tumor cysts, which reportbased on the assumption that it is rather constant in a given edly can be made by the detection of amino acid (0.9 ppm), tissue; however, studies of absolute tCr have partly chal- alanine (1.5 ppm), acetate (1.9 ppm), and succinate (2.4 ppm) lenged this old concept both in normal and tumoral tissues peaks in abscesses [16]. In contrast, the presence of elevated [8, 9]. Studies have shown decreased tCr in a subset of high-­ choline in the cystic component of a rim-enhancing mass is grade gliomas and elevation of tCrin low-grade gliomas and more in favor of a tumoral cyst [17]. Some infracts can also gliomatosis cerebri [8, 10, 11]. The N-acetyl aspartate (NAA) mimic tumors in some stages of their evolution. MRS may peak (2 ppm) is generally considered to be a neuronal marker. support the diagnosis of ischemia, by showing elevated lacIt is found to be decreased in most brain tumors [11]. Due to tate peak in the context of absence or decrease of other difficulties in separation of lactate (Lac, 1.31 ppm), and lipid metabolites [18], but the diagnosis becomes more challeng(Lip, 1.33 ppm) peaks, especially at short TE, they are often ing if choline elevation is encountered due to rapid cell memreported as a combined Lactate + Lipid peak. Separation of brane breakdown, making this application of MRS less lactate is possible by using intermediate or long TE values, useful clinically [17, 19, 20]. Differentiation of active demywhich results in J-coupling of the 1.3 ppm lactate peak with elination from brain tumor can be very difficult as both can its partner at 4.1 ppm resulting in an inverted peak. Presence show contrast enhancement and choline can be elevated in of lactate has traditionally been considered to be an indicator both, due to active membrane destruction/turnover in the forof alteration in glucose metabolism, with increased glycoly- mer and active membrane proliferation in the latter. NAA sis in poorly oxygenated portions of brain tumors. It is most can also be depressed in both entities, due to neuronal injury

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

in both entities and neoplastic tumor cell in the case of tumors [17–20]. Therefore, other advanced imaging methods such as perfusion imaging can be more helpful in differentiation of some forms of active demyelination from brain tumors [4]. MRS has also been used for differentiation between low-grade gliomas and focal cortical developmental malformations by showing more increase of Cho and decrease of NAA in low-grade gliomas than in focal cortical developmental malformations, though elevated choline may not be detected in a minority of low-grade gliomas [21, 22]. Some studies have been performed to investigate the added diagnostic value of MRS, compared to conventional imaging alone in evaluation of brain masses. In one large study, Moller-Hartmann et al. investigated 176 consecutive patients with different brain masses and concluded that addition of MRS significantly increased the proportion of correctly diagnosed cases from 55% to 71% [23]. In addition, there were no cases where a correct diagnosis on MRI was mistakenly discarded due to the MRS findings. In another study, Ando et al. reported that the addition of MRS information to contrast-enhanced MRI findings increased diagnostic sensitivity without altering specificity [24]. During the past decades, there has been an effort to find specific MRS tumor markers for different brain neoplasms. By and large, these studies have revealed a gross correlation between Cho/NAA and Cho/Cr peak height ratios and glioma grade [21, 25, 26]. Similarly, the presence of elevated lipid/lactate suggests the presence of a high-grade tumor [25]. The generally encountered limitation for using MRS in preoperative tumor grading has been significant overlap between high- and low-grade tumors [27], therefore different threshold values have been proposed to improve the accuracy of MRS in tumor grading [1]. Kim et al. showed that both short and intermediate TEs are useful in differentiating high-grade from low-grade gliomas. In this study, the main difference between the spectra with different TEs was that, at short TE, Cho/Cr and Cho/NAA ratios were significantly lower compared to the intermediate TE spectra. This is due to the T2 relaxation time of choline, which is longer than those of Cr and NAA [28]. Although use of short TE MRS can give information about metabolites that are not evident on intermediate and long TE, it has been shown that at short TE, a significant percentage of low-grade tumors may show lack of choline elevation and near-normal Cho/Cr ratios, causing diagnostic confusion in some patients [29]. A few studies have compared the ability of MRS in tumor grading with other advanced imaging techniques and suggested that MR perfusion imaging is more accurate than MRS in tumor grading [1, 30]. The same concepts related to tumor grading can be utilized in biopsy guidance and MRS has been shown to increase the accuracy of stereotactic biopsies by targeting areas with high Cho/NAA ratios in a heterogeneous glial neoplasm [31, 32]. Targeting biopsy to regions with highest

313

lipid content has also been suggested to be helpful in improving the diagnostic yield of biopsy [33]. In a similar fashion, MRS has also been used to guide stereotactic radiosurgery to areas with higher Cho/NAA ratios [34, 35]. Another area of MRS research in the field of brain tumor imaging is differentiation between high-grade gliomas and single metastases. Some studies have focused on comparison of the spectra from the enhancing parts of the tumors with emphasis on different lipid ratios [36, 37], while other studies have evaluated the peritumoral regions [38, 39]. In the literature, the “peritumoral region” often refers to the areas surrounding the enhancing portions of tumor (peri-­ enhancement region). Overall, it seems that evaluation of the peritumoral region is more helpful in differentiation of high-­ grade gliomas from solitary metastases by showing increased choline or choline ratios in high-grade tumors, but not in metastasis [38–40]. It is thought that the peritumoral region in primary high-grade gliomas contains infiltrating neoplastic cells, hence altering the choline ratios, whereas the peritumoral region in a typical metastasis is mostly edema without substantial tumor cell infiltration, and therefore has a more normal appearing MRS spectrum. In one study, the peritumoral region demonstrated the most significant differences in metabolite ratios [41]. The Cho/Cr ratio in glioblastomas was significantly higher than that in metastases. Additionally, elevated Cho/Cr levels were also noted in lymphomas compared to metastases, and lymphoma showed higher lipids+lactate/Cr levels compared with glioblastomas and metastases. Determination of the extent of brain tumors has also been studied using MR spectroscopy. Generally, tumor extent has been investigated based on Cho/NAA ratios in the peritumoral region. For more accurate measurements, an index known as Choline-NAA index (CNI) has been proposed, where CNI is the number of standard deviations between the Cho-to-NAA ratio within a given voxel and that of the control voxels [42]. One of these studies correlated MRS data with histopathologic findings of stereotactic biopsy and showed that a CNI of greater than 2.5 has a high sensitivity and specificity for predicting the presence of tumor in the biopsy sample [43]. Using the Cho-NAA index concept, studies have shown that three-dimensional (3D) MRS can significantly alter radiation therapy target volumes [44, 45]. With more widespread availability of advanced combined neuronavigation technology and intraoperative MRI, MRS may potentially have a stronger therapeutic impact in the future by better defining the true extent of brain tumors during surgery [46]. Use of MRS in order to predict progression in low-grade brain tumors has been addressed in a few studies with conflicting results. Some of these studies have reported that increased choline signal and decreased Cho/NAA ratio may be used to detect early dedifferentiation of low-grade glial

314

tumors [47–49]. However, others did not find MRS to be useful for this purpose [50]. In terms of prognostic value of MRS, a study in low-grade gliomas revealed that elevated tCr was associated with worse outcome compared to those with normal or reduced tCr [51]. Another study of glioblastomas demonstrated that patients with a high volume of elevated CNI have a less favorable prognosis [52]. Finally, a relatively recent concept has been to measure whole-brain NAA in order to assess global burden of a brain tumor. The idea behind this concept is that high-grade gliomas are infiltrative, but their visible degree of infiltration by current conventional and advanced techniques is an underestimation of actual burden. One study has shown a decrease in whole-­ brain NAA in patients with high-grade glioma, with this decrease being approximately 30% more than expected by the visible tumor burden [53]. The actual clinical significance of this measurement, particularly with respect to prognosis prediction, remains to be fully determined.

A. Vossough and S. A. Nabavizadeh

necrosis and recurrent tumor often coexist together in the same region, with different proportions. The other problem, which is inherent to all serial MRS studies, is that spatial variation in metabolite sampling within an individual tumor can be much greater than the change in these peaks over time if there are slight differences in voxel placement between two scans or patient motion. Similarly, any change in acquisition technique can make the assessment of longitudinal change unreliable. Therefore, it is very likely that these parameters cannot be as optimally controlled in actual clinical settings compared to research studies with a small number of selected patients where each MRS study was closely controlled by a highly trained spectroscopist or neuroradiologist.

Diffusion Imaging

Diffusion-weighted imaging (DWI) is a technique that is sensitive to Brownian motion of tissue water. Although DWI Therapeutic Monitoring has emerged as a technique for early detection of acute cerebral ischemia, it has been increasingly used in other disorEvaluation of tumor response to radiotherapy and chemo- ders of the central nervous system (CNS). DWI has been therapy with conventional imaging may take a long time to used extensively in different aspects of brain tumor imaging be reliably detected. Another very important limitation of including diagnosis, treatment planning, and therapeutic anatomical imaging is in the evaluation of treatment response monitoring. Detailed technical aspects of DWI are beyond in low-grade gliomas, which are typically slow growing and the scope of this chapter and will be covered in other chapnon-enhancing. Application of physiologic imaging tech- ters; however, in summary, diffusion sequences are made by niques such as MRS may potentially be helpful in distin- adding an additional pair of gradient pulses to render an MR guishing true tumor recurrence/progression from signal, which is sensitive to the mobility of water molecules. treatment-related changes. Several studies have evaluated Any molecular movement between first and second pulses the role of MRS to assess treatment response. Decreased results in incomplete rephrasing of the signal, which can lead choline levels and concomitant increase in Lip+Lac has been to signal loss. In addition to diffusion rate of water molefound as a marker of response to radiotherapy in some stud- cules, DWI signal intensity also includes a T2-weighted ies [54–56]. Similar changes have also been described as component. Therefore, true reduced diffusion is estimated early markers of treatment following chemotherapy in high-­ and measured by comparing diffusion-weighted (trace) grade gliomas [57, 58]. Serial spectroscopic monitoring of images, with the same image acquisitions without diffusion treatment response to chemotherapy has also been evaluated weighting (b-zero) on a voxel by voxel basis, which results in low-grade gliomas and a decreased choline level has been in generation of an “apparent diffusion coefficient” (ADC) reported, suggesting a potential role for MRS in these types or diffusivity map. Use of higher numbers of diffusion gradiof tumors [59]. ent directions, multiple diffusion strengths (B levels), and One of the other limitations of conventional imaging in non-Gaussian assessment of diffusion has also led to addibrain tumors is distinction between radiation necrosis and tional diffusion-derived techniques such as diffusion tensor recurrence following treatment. Several studies have reported imaging, high angular resolution diffusion imaging that MRS can be useful to differentiate these two entities by (HARDI), diffusion kurtosis imaging (DKI), and others. showing increased Chol/Cr or Cho/NAA ratio over time in These techniques allow additional diffusion-derived paramrecurrent tumor, and have therefore concluded that serial eters to be assessed such as fractional anisotropy, axial difMRS may differentiate these two entities with reasonable fusivity, radial diffusivity, track density, and various indices accuracy [24, 60–63]. However, in real clinical settings there of kurtosis. Many of these parameters have not yet found are at least two problems. The first problem is that radiation their way into routine clinical practice at this time.

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

 ifferential Diagnosis, Preoperative Tumor D Grading, and Biopsy Guidance

315

observed that most of the patients with high- and low-grade gliomas showed areas of increased signal intensity on images obtained with a b value of 1000 s/mm2. However, with a b DWI in conjunction with conventional MRI has been proved value of 3000 s/mm2, the areas of increased signal intensity to be useful in differential diagnosis of intra-axial brain were seen in most patients with high-grade gliomas, while lesions. Quantitative ADC maps have been useful in trying to most of the low-grade gliomas showed no area of increased differentiate tumors from abscesses, demyelinating plaques, signal intensity [79]. Another study by Seo et al. also conand radiation-induced necrosis [64–67]. The viscous and cel- cluded that DWI at b = 3000 s/mm2 is more useful than DWI lular pus in abscesses produce a low ADC that in many cases at b = 1000 s/mm2 in terms of discriminating high-grade and can distinguish these lesions from facilitated diffusion in low-grade gliomas at 3 T [80]. The exact role of high b-value necrotic tumor. Low ADC has also been noted as a typical DWI in grading of gliomas remains to be determined in profeature of radiation necrosis, while demyelinating plaques spective studies, especially in comparison with MR spectrostypically have normal or facilitated diffusion, although some copy and MR perfusion imaging. may have more peripheral areas of restricted diffusion [68]. Another observation that has been encountered in high-­ Another useful application of DWI in brain tumor imaging is grade gliomas is variability in ADCmin, even in tumors of the assessment of cellularity. A low ADC in an intra-axial neo- same grade and histology [81, 82]. Although part of this may plasm can often be seen in lymphoma or metastasis second- be explained by presence of necrosis and hemorrhage, which ary to relative increased cellularity and decreased tissue is very common in these types of tumors, it may also be water. This finding has been noted to be helpful in differenti- indicative of heterogeneity of cellularity among tumors of ating these lesions from many gliomas, which show gener- the same grade and it seems to be a confounding factor in ally higher ADC values, but it should be mentioned that preoperative prediction of histological grade. Reports have decreased diffusivity can be detected in cellular high-grade also suggested that ADCmin may be useful in prediction of gliomas as well [69, 70]. A few reports have been published radiation responsiveness in high-grade gliomas [82]. In cases in which a direct comparison of ADC values with the Ki-67 of highly heterogeneous tumor, attention to the ADC map index was performed, and most found a significant inverse before biopsy and sampling the area of minimum ADC has correlation between ADC values and Ki-67 [71, 72]. been shown to improve the diagnostic yield of biopsy, and Another area of potential application of DWI has been in better correlation with histology [83]. In one study, an inverse preoperative grading of gliomas. Several studies have found correlation was found between relative ADC and various hisan inverse correlation between areas of minimum ADC topathologic features of aggressiveness [84]. Newer tech(ADCmin) within the tumor and glioma grade, and different niques such as diffusion tensor imaging (DTI) and diffusion cutoff values have been proposed [73], but significant over- kurtosis imaging (DKI) have also been applied in the grading lap between high and low grade exists in most of these stud- of brain tumors, mostly in a research setting. One meta-­ ies [25, 74]. A study claimed that using a combination of analysis showed that high-grade gliomas had decreased averminimum ADC and ADC difference values (which is the dif- age mean diffusivity values compared with low-grade ference between minimum and maximum ADC) facilitates gliomas in the tumor core and increased average mean difthe preoperative grading of astrocytic tumors [75]. Despite fusivity values in the peripheral region [85]. High-grade glithese efforts, multiparametric studies showed that using omas had increased FA values compared with low-grade ADC alone is less useful than combined studies with MR gliomas in the tumor core, decreased values in the peripheral spectroscopy and MR perfusion in grading of gliomas [30]. region, and a decreased fractional anisotropy difference Other studies have shown that histogram analysis of ADC between the tumor core and peripheral region. A meta-analvalues is useful in distinguishing low-grade astrocytomas ysis of ten studies reported that DKI metrics had an accuracy and oligodendrogliomas [76], and another study in oligoden- a sensitivity of 0.85 and specificity of 0.92 for distinguishing drogliomas found a relationship between the 1p/19q codele- low-grade and high-grade gliomas [86]. tion genotype and ADC values [77]. There has been recent Application of DWI for delineation of tumor margins has interest in use of high b-value DWI in preoperative grading been addressed in some studies. Generally, these studies of glioma. DWI with a high b value (generally in the range of have not correlated the location of DWI abnormality with 3000–4000 s/mm2 rather than the more typical 1000 s/mm2) histopathology, and instead they compared DWI parameters allows the characteristics of water diffusion to be studied in of peritumoral edema in high-grade tumors with other more detail [78]. The rationale for using these techniques is tumors, based on the presumption that high-grade glioma that diffusing water molecules can be divided into two pools, cells infiltrate surrounding brain, but other tumors (metastaone of which diffuses at a faster rate than the other. Alvarez-­ ses, meningioma) do not. Some of these studies have shown Linera et al. investigated 54 patients with gliomas and com- higher ADC values in the peritumoral edema for metastases pared ADC with b values of 1000 and 3000  s/mm2. They compared to high-grade gliomas [38, 87]. On the other hand,

316

some studies have concluded that DWI cannot reliably differentiate edema with infiltration of tumor cells from pure vasogenic edema [88, 89]. There is only one study that directly correlated ADC in specific locations with histopathologic examination findings of neuroimaging-navigated biopsy specimens, and it showed considerable overlap between the ADC of tumor and peritumoral tissues, failing to provide important additional diagnostic information [90]. A meta-analysis of nine studies with DTI concluded that high-­ grade gliomas may be distinguished from brain metastases by comparing the peritumoral FA and MD values but not by intralesional DTI metrics [91]. Diffusion imaging using high b values may potentially be able to better differentiate tumor tissue from peritumoral edema by demonstrating specific diffusion abnormalities in the evaluation of edema surrounding a mass. However, this remains speculative and requires further studies with histopathologic correlation.

Therapeutic Monitoring and Prognostication Currently, the imaging standard by which response to treatment is determined is change in enhancing tumor size in sequential MRI examinations [92]. However, these changes in tumor size may take many months to become apparent. There has been a research trend to use DWI to measure the response of brain tumors after therapy. The rationale for using DWI is that treatment of a tumor with cytotoxic agents may result in significant cell death, which in turn will reduce the total cellularity and this can be detected as a change in ADC values. Results of a few studies have revealed that serial measurements of ADC at early time points following treatment may be able to assess dynamic response to treatment, and differentiate responsive from non-responsive tumors [93, 94]. Low ADC after chemoradiation therapy has been shown to be a poor prognostic marker [95]. Diffusion imaging has also shown to be useful as a predictor of response in patients with brain metastases treated by stereotactic radiosurgery [96]. A few studies have evaluated the role of DWI in differentiation of radiation necrosis from recurrent tumor and have demonstrated restricted diffusion in some patients with radiation necrosis [65, 67]. One study that combined DWI and MR spectroscopy revealed that DWI does not provide additional information to MR spectroscopy in differentiation of radiation necrosis and recurrent tumor [97]. Al Sayyari A et al. [98] in a retrospective study of 17 patients revealed that susceptibility-weighted MRI-guided apparent diffusion coefficient analysis is helpful in differentiation of recurrent tumor from radiation injury. Determination of the actual added value of DWI in the diagnosis of this entity warrants further studies.

A. Vossough and S. A. Nabavizadeh

The final issue is the role of DWI in predicting patient prognosis. Two prospective studies have shown that preoperative ADC measurements in high-grade gliomas can predict patient prognosis [72, 99]. Another study has shown that ADC measurements within contrast enhancing regions of primary central nervous system lymphoma tumors is predictive of the patients’ clinical outcome, both in terms of progression-­free and overall survival [100]. Ellingson et al. reported application of diffusion analysis in a patient with gliomatosis cerebri, which correlated well with progressive decline in neurological status despite no change in traditional magnetic resonance images [101]. A study of 34 patients with low-grade glioma revealed that the ADC parameters were not a useful predictor of malignant transformation [102]. In phase II clinical trials of antiangiogenic agents for recurrent glioblastoma, baseline ADC was found to be a marker for overall survival in these patients [103]. One recent study did not find added prognostic value of diffusion kurtosis imaging measures in patients with glioblastoma [104]. Utility of more recent advanced diffusion techniques needs to be validated in larger studies.

Perfusion Magnetic Resonance Imaging In the brain MRI literature, perfusion MRI refers to an all-­ encompassing term of various methods to measure hemodynamically derived functional parameters. Perfusion MRI can be done without contrast injection by tagging intravascular protons using various MR labeling schemes (arterial spin labeling) or can be performed via two general approaches using dynamic gadolinium-based contrast injection. The first approach is termed dynamic contrast-enhanced (DCE) MRI and is based on relaxivity measurements using a steady-state T1-weighted sequence during gadolinium contrast administration over a period of several minutes. The second approach is termed dynamic susceptibility contrast (DSC) MRI and is based on susceptibility effects using T2- or more commonly T2*-weighted images acquired over approximately 1–3 min, during which a high concentration bolus of gadolinium chelate rapidly passes through the brain. DSC perfusion imaging is the most commonly studied and clinically used technique in assessment of brain masses and will be discussed in this section. This method was initially described approximately three decades ago and is based on the principle that the signal change that occurs during passage of a high concentration bolus of gadolinium contrast in the vessels causes a difference in susceptibility between the contrast-containing vessels and brain tissue, and that this signal change can be converted to a relaxation rate change proportional to the fraction of blood volume within each voxel [105, 106]. These relative blood volume measurements are used to construct a relative cerebral blood volume (rCBV) map [107].

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

There are multiple technical considerations in the use of DSC imaging in the assessment of brain tumors. Accuracy of rCBV maps can vary substantially depending on the acquisition and postprocessing methods [108]. First is the choice of imaging sequence. In order to increase temporal resolution during dynamic contrast administration, currently most centers use an echo planar imaging (EPI)-based method. Different investigators have used spin echo EPI, gradient echo EPI, or a combination of both [109–111]. It is important to take into consideration the type of acquisition in applying research results for characterization of brain tumors, as the results and thresholds may vary [112]. Spin echo methods are sensitive to smaller vessels and capillaries, whereas gradient echo methods are sensitive to both small and large vessel perfusion [109, 112]. Using spin echo sequences may be helpful near the skull base or at bone or air interfaces, as they are less susceptible to artifacts. Most centers currently use gradient echo sequences for performing clinical DSC perfusion MRI in the assessment of brain tumors. This technique is very sensitive to structures that cause magnetic field inhomogeneity such as blood, calcium, bone, metals, or near air interfaces such as at the skull base. Reducing slice thickness and parallel imaging can be used to decrease these effects, but if larger coverage is needed, the interslice gap could be increased [113]. Another issue in the use of DSC perfusion imaging in tumor assessment is contrast leakage within brain tumors, which can lead to underestimation and inaccuracy of rCBV measurements, and potentially affect clinical interpretation. One way to solve this problem is to use correction algorithms to compensate and correct for leakiness in these tumors. It has been shown that corrected rCBV maps correlated with glioma tumor grade while uncorrected maps did not [114]. Another approach is to employ a dual-echo gradient echo acquisition [108], but this is not widely used. Finally one solution is to administer a small preload dose of contrast prior to performing the DSC perfusion MRI bolus injection to allow for leakage [108]. This preload injection can serve a dual purpose and be used for performing DCE imaging in the same MRI session as well. Simultaneous GRE and SE DSC acquisition allows the potential calculation of vessel size index from the ratio of GRE to SE relaxivity without additional scan time and also obviate the need for preload injection; however, these techniques are not yet standardized [110, 115, 116]. In addition to the established processing techniques, described in previous section, there are additional techniques that can be used to process DSC data. Independent component analysis (ICA) is a technique that applies a data-driven, multivariate approach to categorize voxel time series and it has been mainly used to analyze functional MRI (fMRI) data by examining voxels exhibiting the same temporal response patterns [117]. LaViolette et al. used ICA to classify voxels

317

with perfusion characteristics of both arteries and veins in patients with de novo GBM and patients with recurrent high-­ grade glioma before and after bevacizumab treatment. They demonstrated that arterio-venous overlap (AVOL) volume was significantly greater than the percentage of AVOL in nontumor vasculature. They also demonstrated that patients with decrease in AVOL after treatment showed an increase in overall survival, while rCBV and enhancing volume measures did not significantly differ across groups [118]. Another utilized technique is principal component analysis (PCA) which is a standard dimensionality reduction method [119]. In this type of analysis, the first principal component captures the highest amount of variance, with each succeeding components have the highest variance. Recently, Akbari et al. used PCA to analyze the peritumoral region in patients with glioblastoma using different aspects of DSC MRI time series such as baseline signal, depth and slope of signal decrease, signal recovery, and percentage of signal recovery. Their study demonstrated that PCA shows near-perfect accuracy in separating highly infiltrated tissues from regions that were unlikely to be infiltrated with tumor. They also created a heterogeneity map that predicted subsequent recurrence [119]. DCE perfusion MRI can be analyzed using two general approaches. The first and simpler approach is model-free analysis of the area under the time–signal intensity curve (AUC) during a given time. The advantage of this technique is that it is easier to perform without the need for complex postprocessing models. The second way to analyze DCE data is to use a pharmacokinetic model to quantify different metrics. The most commonly used model is the modified Tofts model. Each voxel in the Tofts model can contain three components: tissue parenchymal cells, blood vessels, and the tissue extracellular extravascular space (EES). The main parameters calculated by this model are Ktrans which is a measure of microvascular permeability, total plasma space volume (Vp), total extravascular-extracellular space volume (Ve), and Kep which is the reflux rate of gadolinium from the EES back into plasma [120]. The baseline T1 value would be needed to obtain concentration-­ time curve pharmacokinetic parameters in DCE imaging [121]. There are generally two approaches for establishing the T1 value. An estimate of baseline T1 value can be derived by using different techniques such as multiple flip angles or inversion recovery techniques [122, 123]. The downside of this approach is that estimation of the baseline T1 would be sensitive to noise from multiple factors such as scale factor miscalibration and motion [121, 124]. Another approach would be to use a fixed baseline T1 value based on available literature. The latter approach can generate more consistent results and can save several minutes of scanner time over the first method. The downside of using a fixed T1 value is that since it is not physiologic to the patient, some of

318

the calculated Ve values may be more than 100%, which is not possible [125]. A recent study published by Nam et al. demonstrated that Ktrans calculated from the fixed T1 acted as a preferable marker to differentiate true progression from pseudoprogression in patients with glioblastoma [123]. Another study by Tietze et al. demonstrated that although a fixed T1 introduced a bias into the DCE calculation, it did not have a major effect on the accuracy for differentiating high-grade from low-grade gliomas [121]. Arterial spin labeling (ASL) is an increasingly used imaging technique for measuring perfusion by using water molecules in blood vessels without the need for exogenous contrast material. This technique provides a potential advantage over DSC and DCE perfusion techniques given the increasing concern with gadolinium deposition in the brain. There are multiple different approaches for ASL image acquisition, but in general baseline control images are acquired through the area of interest followed by reimaging the area of interest after tagging the blood within the vessels in a slab of tissue proximally which would typically be the upper neck in brain ASL. Final images will be generated by subtracting the tagged from the control images in order to tease out the exchange rate of tagged water molecules with the static tissue as a representation of blood flow [126, 127]. Most current clinical scanners have an ASL sequence available as a commercial sequence option and PASL (Pulsed Arterial Spin Labeling) and pCASL (pseudo-Continuous Arterial Spin Labeling) are considered the most commonly used sequences. CASL (Continuous Arterial Spin Labeling), which was the first developed ASL sequence, is now considered obsolete given the difficult implementation and significant tissue energy deposition [126, 127].

 ole of Perfusion Imaging in Preoperative R Tumor Grading and Biopsy Guidance Dynamic susceptibility contrast (DSC) perfusion MRI has been extensively studied in brain tumors [1, 128–131]. Several studies have demonstrated that CBV measurements have clinical value in grading of cerebral gliomas. Typically, the CBV value derived from DSC is not fully quantitative and often calculated as a ratio to the contralateral normal appearing white matter, providing relative CBV (rCBV) values. Maximum rCBV values of low-grade gliomas has been reported between 1.11 and 2.14, whereas maximum rCBV of high-grade gliomas were between 3.54 and 7.32 [1, 113, 128, 132, 133]. In one study, using an rCBV threshold of 1.5 provided a 100% sensitivity for detecting high-grade gliomas. In a larger study, using the rCBV threshold of 1.75 provided a sensitivity of 95% and specificity of 57.5% in differentiation of high-grade and low-grade gliomas. Some

A. Vossough and S. A. Nabavizadeh

low-grade gliomas can have high CBV and therefore confound this accuracy, particularly oligodendrogliomas which will be discussed later in the chapter [134–136]. Perfusion MRI has also been used in distinguishing high-grade gliomas from lymphomas, which could have a similar appearance on conventional MRI, demonstrating that lymphomas have lower mean rCBV values. Nevertheless, in that study the presence of a very leaky blood brain barrier may have contributed to lower-than-expected rCBV measurements [137]. Perfusion imaging has also been used to differentiate high-grade gliomas from solitary metastases [38, 39]. The peritumoral rCBV is shown to be higher in primary high-­ grade gliomas than in metastases, due to the infiltrative nature of high-grade gliomas beyond the enhancing margin. DCE imaging has also been studies for glioma grading. Jia et al. showed that Ktrans and Ve were significantly lower in low-grade gliomas compared to high-grade gliomas, with cutoff values of 0.035  min−1 and 0.13, respectively [138]. Jung et al. demonstrated that histogram analysis of Ktrans, Ve, and Vp obtained from the entire-tumor volume data were useful for grading gliomas; however, the 98th percentile Ktrans was the only variable to independently differentiate high- and low-grade gliomas [125]. In addition, Mills et al. demonstrated that high values of Ktrans were associated with the presence of frank necrosis and high values of Ve were associated with a fibrillary histologic pattern and with increased mitotic activity [139]. A number of studies have demonstrated that ASL imaging can have utility in adult glioma grading [140, 141] and also can predict histopathologic vascular density [141]. In a recent study comparing DSC and ASL imaging, Arisawa et al. demonstrated a strong correlations in the 75th percentile, mean, median, and standard deviation values between the ASL and DSC images; however, the area under the curve values was greater for the DSC images comparing ASL images indicating superiority of DSC imaging in glioma grading [142]. A recent meta-analysis of ASL in differentiating low- and high-grade glioma reported a sensitivity of 085–0.88 and specificity of 0.80 and 0.83, depending on the type of ASL technique [143]. Perfusion MRI has also been used in guiding stereotactic biopsy and radiosurgery in glioma patients. Traditionally, T1-weighted postcontrast MR sequences have been used to direct stereotactic biopsy targeting of enhancing masses, and fluid-attenuated inversion recovery (FLAIR) or T2-weighted sequences for non-enhancing masses. The rationale for using perfusion data is based on the utility in defining the most hypervascular region of the tumor [113, 144]. The region of highest vascularity and presumably highest malignancy does not necessarily correspond to the areas of contrast enhancement (contrast leakage) or there may be varying degrees of perfusion within the contrast enhancing portions of the tumor.

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

Therapeutic Monitoring and Prognostication It is thought that at least half of low-grade astrocytomas will eventually dedifferentiate into high-grade tumors over the years. It has been shown that DSC MR may show increases in rCBV up to 12 months before the development of contrast enhancement in low-grade gliomas, potentially contributing to prediction of malignant transformation [145]. Law and colleagues have also retrospectively compared the value of rCBV measurements in predicting patient outcome in lowand high-grade gliomas [146]. They demonstrated that rCBV was an independent predictor of time to progression and clinical outcome. Using an rCBV threshold of 1.75 (compared to contralateral white matter) they were able to predict median time to progression in patients with gliomas, regardless of whether the tumor was low grade or high grade on pathology. Choi et  al. demonstrated that higher Ktrans and Ve are associated with worse prognosis in patients with glioblastoma [147]. In a study of 24 patients with glioma using ASL imaging, Furtner et al. demonstrated that using maximum tumor blood flow cutoff value of 182 mL/100 g/ min, patients with low-perfused gliomas had significantly longer event-free survival compared to patients with highperfused gliomas independent of the WHO glioma grade [148]. A recent study comparing DSC vs ASL in 69 subjects with WHO Grade 3–4 gliomas demonstrated that rCBV measurements derived from DSC imaging provide the best sensitivity and specificity to predict tumor recurrence and survival time [149]. DSC perfusion imaging has been used in multiple studies as a predictive response marker of different treatment agents, most commonly antiangiogenic agents [150–152]. Baseline rCBV has been shown to correlate with overall survival in patients with high grade glioma receiving bevacizumab treatment [150, 152]. Another study in patients with recurrent GBM demonstrated that baseline rCBV stratified progression-­ free survival and overall survival in bevacizumab-treated patients. This study showed that a rCBV above the cutoff value of 3.92 was associated with halving of the median survival in comparison to rCBV below the cutoff, suggesting that rCBV may be a predictive biomarker in GBM patients in the setting of bevacizumab treatment [151]. DSC perfusion imaging has also been used as an early response biomarker in patients with high grade glioma in the setting of chemoradiation and antiangiogenic treatment. In a study of 36 patients with GBM who were treated with radiation and temozolomide, the percentage change in rCBV at 1  month after chemoradiation correlated with overall survival. Furthermore, increased rCBV after treatment was a strong predictor of poor survival. The study also showed a greater area under the ROC curves for 1-year survival assessed by rCBV than by tumor size [153]. Galban et  al.

319

used parametric response mapping (PRM) which is a voxel-­ wise approach for image analysis. They used rCBV and rCBF maps before treatment and after 1 and 3 weeks of therapy in 44 patients with high grade glioma and showed that the percentage change of rCBV or rCBF based on standard ROI placement did not predict survival, whereas the regional response evaluations made on the basis of PRM were highly predictive of survival [154]. The results of this study were corroborated in a subsequent study that compared three different methodologies including percentage change of whole tumor statistics (i.e., mean, median, and percentiles), the physiological tumor segmentation (low rCBV, medium rCBV, or high rCBV), and PRM in 44 patients with high grade glioma that were imaged pre-therapy and 1 and 3  weeks after initiation of chemoradiation. They demonstrated that PRM was the only analytical approach found to generate a response metric significantly predictive of patient 1-year survival [155]. A multi-center study of DSC perfusion MRI in patients with recurrent GBM receiving bevacizumab combined with irinotecan or temozolomide demonstrated that patients surviving at least 1  year had significantly larger decreases in rCBV at week  2 and 16 of treatment and patients with increased rCBV from baseline had significantly shorter OS than those with decreased rCBV at both week 2 and week 16 [156]. In a recent meta-analysis, Choi et  al. evaluated the value of DSc and DCE perfusion MRI as a predictive/prognostic biomarker in patients with recurrent glioma treated with a bevacizumab-based regimen. Based on analysis of 13 studies, they demonstrated that the pooled hazard ratios between responders and non-responders as determined by rCBV were 0.46 for progression-free survival based on analysis of 226 patients and 0.47 for overall survival based on analysis of 247 patients. This indicates that rCBV is helpful for predicting disease progression and also eventual outcome after treatment [157]. They also demonstrated that most perfusion and permeability MRI parameters (rCBV, Ktrans, CBVmax, Vp, Ve, and Kep) demonstrated a consistent decrease on the follow-up MRI after treatment [157].

Intra-axial Brain Mass Diagnostic Strategy Traditionally, the first step in the characterization of intracranial masses is determination of whether the mass is intra-­ axial or extra-axial. Extra-axial masses may arise from bone, cartilage, meninges, vasculature, cranial nerves, or be metastatic. Conventional high-resolution MRI is often accurate in distinguishing intra-axial from extra-axial masses. While conventional MRI is useful for the characterization of intra-­ axial brain masses, there are significant areas of diagnostic overlap and limitations for the accurate classification of

320

A. Vossough and S. A. Nabavizadeh

brain neoplasms and masses. Use of diagnostic information from advanced MRI techniques has potential to further improve the classification accuracy of conventional anatomic imaging [3, 4]. Imaging experts and researchers in the field of brain tumor imaging have been utilizing added information from advanced MRI for a long time. However, in the clinical arena, integration of all the various advanced MRI techniques (diffusion-weighted imaging, perfusion-weighted imaging, MR spectroscopy) into a relatively well-defined and easy-to-follow diagnostic strategy or algorithm would be desirable for the physician interpreting the imaging of brain masses. An example of such diagnostic algorithmic approach, from the authors’ own institution, in combining conventional and advanced MRI for characterization of intra-axial brain masses in adults is shown in Fig. 12.1 [3, 4]. It is important to recognize that any such diagnostic strategy or algorithm be considered in conjunction with the conventional MRI features and appearances of brain masses, the patients’ clinical

context, and the information from other imaging modalities such as computed tomography and nuclear medicine. Additionally, it is important to realize that any such algorithm is imperfect and will have diagnostic pitfalls and exceptions, and the advanced imaging data that feeds into this algorithm may have technical and postprocessing nuances that should be understood and duly considered for optimal interpretation and management. In particular, parameter cutoff numerical values should not be viewed as definitive perfect delimiters given some degree of inherent variability in determining these types of thresholds. In order to better understand and utilize algorithmic approaches to advanced imaging in brain masses, we will first briefly summarize the typical advanced MRI features of various intra-axial brain masses [3, 4, 83, 158–160]. Again note that the following descriptions are the typical findings in each category and are present in the majority of cases, but there will always be exceptions to the typical imaging features and presentations of these lesions.

Adult Intra-axial Brain Masses

Conventional Contrast MRI Does the lesion enhance with Gd? No

Yes

Magnetic Resonance Spectroscopy Is Cho/NAA elevated (e.g., over 2.2)? No Low grade Neoplasm or Encephalitis

Diffusion MRI Is diffusion faciliated (e.g., over 1.1)?

Yes

Yes

MR Perfusion MR Perfusion Is perfusion increased (e.g., rCBV>1.75)? Is perfusion increased (e.g., rCBV>1.75)? No

Yes

Low Grade Neoplasm

Yes

No

Yes High Grade Neoplasm

No

Abscess Tumefactive Demyelinating Lesion

Is there is necrosis/rim enhancement? No Lymphoma

Magnetic Resonance Spectroscopy Is there peri-enhancement infiltration (e.g., Cho/NAA over 1)? Yes High Grade Glioma

Fig. 12.1  An example of a diagnostic algorithm to assist in classification of unknown intra-axial brain masses. This algorithm combines conventional MRI features (enhancement) with advanced MRI features

No Metastasis

of brain masses (diffusion, perfusion, spectroscopy). (Adapted from [3, 4]. Please note that the thresholds noted in these algorithms are informative and not definitive)

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

High-Grade Gliomas High-grade gliomas typically enhance with contrast, mostly in a heterogeneous fashion, and may have nonenhancing areas of necrosis. Diffusion imaging findings are variable and often the ADC is heterogeneous within these masses, but highly cellular tumors (for example some solid portions of glioblastomas) may have reduced diffusion. On MR spectroscopy, there is typically elevated choline (sometimes markedly), decreased NAA, and increased lipid+lactate levels (especially in glioblastoma). There is no perfect spectroscopic cutoff value to differentiate high-grade and low-grade gliomas, but a choline/NAA cutoff ratio of 2.2 has been suggested to separate high-grade versus low-grade glioma and non-neoplastic conditions [3]. On DWI, the signal characteristics of high-grade tumors is dependent on tumor cellularity [69]. Since different tumors may have different cellularity or even different parts of the same tumor may have various degrees of cellularity, the ADC values are variable. Glioblastomas often have hypercellular regions and are

321

therefore more likely to have areas of reduced diffusion [69, 161]. MR perfusion imaging typically demonstrates elevated blood volume (often greater than 1.75 compared to contralateral white matter) [1]. An example of advanced imaging features a high-grade glioma is shown in Fig.  12.2. There is considerable variability in the appearance of glioblastoma as defined more recently using molecular and genetic criteria.

Low-Grade Gliomas Low grade diffuse fibrillary astrocytomas typically do not enhance with contrast material. Other low-grade primary neoplasms such as pilocytic astrocytomas, mixed neuronal glial cell tumors, and low-grade oligodendrogliomas may demonstrate contrast enhancement. Diffusion imaging findings are again variable and there is overlap between highand low-grade tumors, though generally the ADC value of low-grade tumors is higher than high-grade gliomas [83]. On MR spectroscopy, low-grade gliomas often, but not always,

a

b

c

d

e

f

g

h

Fig. 12.2  Conventional and advanced MRI in primary high-grade glial neoplasm (glioblastoma). (a) Axial FLAIR image demonstrates a mass in the right cerebral hemisphere, with heterogeneous signal. (b) Axial precontrast T1-weighted image shows heterogeneous hypointensity within the lesion. (c) Postcontrast T1-weighted image shows heterogeneous contrast enhancement within the mass. (d) DWI image shows high signal intensity, which on the ADC map (e) is confirmed to be reduced diffusion in the bulk of the mass, and T2 shine-through along the posteromedial aspect of the mass. (f) CBV map derived from

dynamic susceptibility contrast perfusion imaging demonstrates markedly elevated blood volume in large portions of the mass compared to the contralateral white matter. (g) MRS spectrum from a voxel placed over the enhancing portion of the mass demonstrates markedly elevated Cho/Cr and Cho/NAA ratios. (h) MRI spectrum from a voxel placed outside the enhancing portion of the mass demonstrates elevated Cho/ Cr ratio and slightly elevated Cho/NAA ratio, suggesting that there is infiltrating neoplasm beyond the enhancing margins of the mass, therefore suggesting that it is an infiltrating primary high-grade tumor

322

A. Vossough and S. A. Nabavizadeh

have elevated choline and variable decrease in NAA.  The degree of choline elevation is generally less than in high-­ grade gliomas, but there is overlap between the two. Myoinositol is elevated in many low-grade gliomas. On perfusion MRI, most low-grade gliomas, especially fibrillary astrocytomas do not have elevated rCBV, though again oligodendrogliomas and some pilocytic astrocytomas could have elevated rCBV despite being low-grade. An example of a low-grade glioma is shown in Fig. 12.3.

Primary Central Nervous System Lymphoma In immunocompetent patients, lymphomas typically present with single or multiple, often homogeneous enhancing lesions that may mimic high-grade gliomas on conventional MRI.  On MR spectroscopy, they typically have increased choline, reduced NAA, and increased lipid+lactate. In immunosuppressed patients, lymphoma may have peripheral or rim enhancement rather than solid enhancement. In AIDS

a

b

c

d

e

f

Fig. 12.3  Conventional and advanced MRI in a patient with a grade II astrocytoma. (a) Axial FLAIR image demonstrates a hyperintense mass in the right frontal lobe. (b) Postcontrast T1-weighted images demonstrate no contrast enhancement. (c) DWI image shows no restricted diffusion within the mass lesion. (d) CBV map derived from dynamic

susceptibility contrast perfusion imaging demonstrates low blood volume within the mass. (e) MRS performed with TE  =  135  ms on the mass lesion demonstrates elevated Cho/Cr and Cho/NAA ratios. (f) MRS performed with a short TE = 30 ms on the mass lesion demonstrates high myoinositol level at 3.56 ppm (arrow)

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

patients, toxoplasmosis can have elevated lipid and lactate, but often other metabolites are very low to absent on MR spectroscopy [162]. On diffusion-weighted imaging, lymphomas classically have reduced diffusion secondary to high cellularity and increased nucleus to cytoplasm ratio [69]. On perfusion imaging, lymphomas typically have low blood volume compared to high-grade gliomas and metastases, though the CBV of lymphomas could be variable. The CBV of lymphomas is typically higher than that of toxoplasmosis [163]. The enhancement of lymphomas is thought to be due to blood brain barrier destruction and not due to neovascularization [130]. Analysis of time intensity curve of DSC perfusion images has been reported to help in differentiating lymphoma from glioblastoma and metastasis. In lymphoma, percentage signal return is typically higher compared to glioblastoma and metastasis and can even show an overshoot over the baseline signal intensity, which is reflective of a T1 effect secondary to contrast extravasation [164, 165]. In a multiparametric study comparing 28 patients with glioblastoma of atypical appearance (solid enhancement with no visible necrosis) with 19 patients with lymphoma, Kickingereder et al. demonstrated that ADC and rCBV values were significantly lower in patients with PCNSL compared to glioblastoma. In addition, presence of intratumoral susceptibility signal (ITSS) was significantly lower in patients with PCNSL [166]. They also showed that combined multiparametric assessment of mean ADC, mean rCBV, and presence of ITSS significantly improved the differentiating PCNSL and atypical glioblastoma when compared to evaluation of one or two imaging parameters [166]. In a recent study of 42 patients with GBM and 18 patients with PCNSL who underwent conventional MRI, diffusion-­ weighted imaging, and DCE-MRI before surgery, PCNSLs demonstrated significantly lower rADC, but higher Ktrans and Ve compared to GBMs. The combination of rADC and Ktrans significantly improved the diagnostic ability for discriminating between PCNSL and GBM with area under the ROC curve  =  0.930 [167]. An example of a primary CNS lymphoma in an immunocompetent patient is shown in Fig. 12.4.

Brain Metastases Brain metastases can have a variable imaging appearance, depending on the underlying neoplasm, stage of disease, and patient’s treatment status. The majority of brain metastases demonstrate enhancement, which could be solid, patchy, or peripheral. Solitary metastases may mimic primary brain tumors, especially high-grade gliomas. Highgrade gliomas are infiltrative and often there is significant

323

tumor infiltration beyond the enhancing part of the tumor, whereas the area surrounding the enhancing portion of the brain metastasis is thought to typically just represent edema. On MR spectroscopy, features of metastases are similar to high-grade neoplasms and include elevated choline and lipid+lactate, and reduced or absent NAA. Some studies have suggested that there is higher lipid within metastatic lesions [23, 168], but this is of limited use in clinical MR spectroscopy. On the other hand, spectroscopic interrogation of the areas around the enhancing portion of the mass has been shown to be more promising [39, 169]. In one study, a Cho/NAA cutoff ratio of 1 was shown to have excellent accuracy in differentiating the two [169]. On diffusion imaging, the ADC values of metastases are variable and partly dependent on the primary tumor. Many metastases have an elevated ADC, but certain types of tumors, especially hypercellular lesions such as small cell lung cancer metastases, may demonstrate reduced diffusion [170]. Peritumoral diffusion imaging also has shown higher ADC values in metastases compared to primary neoplasms [87]. Similarly on perfusion imaging, there is often elevated blood volume in the enhancing portion of metastases as there is in high-grade primary gliomas; however, there is higher peritumoral blood volume in infiltrative primary neoplasms compared to metastatic lesions [39, 130]. Cha et al. reported that analysis of time intensity curve of DSC perfusion images can differentiate single brain metastasis from glioblastoma. They demonstrated that the percentage of signal intensity recovery is reduced in both enhancing and peritumoral T2 prolongation in patients with metastasis compared to glioblastoma [171]. An example of a typical brain metastasis is shown in Fig. 12.5.

Tumefactive Demyelinating Lesions Tumefactive demyelinating lesions are uncommon, but can mimic brain tumors, and given the marked difference in management, accurate diagnosis of these lesions is important in order to prevent unnecessary surgery as much as feasible. On MR spectroscopy, these lesions demonstrate a nonspecific elevation of choline and sometimes reduced NAA, which is not very helpful in distinguishing these lesions from neoplastic etiologies [172, 173]. Diffusion-weighted imaging is variable. ADC is often elevated, but sometimes acute demyelinating foci could have reduced diffusion. On perfusion imaging, tumefactive demyelinating lesions typically have low blood volume measurements, lower than high-­ grade gliomas and metastases and sometimes lower than normal brain tissue [130, 174]. An example of the typical tumefactive demyelinating lesion is shown in Fig. 12.6.

324

A. Vossough and S. A. Nabavizadeh

a

b

c

d

e

f

Fig. 12.4  Conventional and advanced MRI in a 72-year-old patient with primary central nervous system lymphoma. The patient was not immunosuppressed. (a) Axial FLAIR image shows a hyperintense mass lesion in the left frontal lobe. (b) Postcontrast T1-weighted image demonstrates rather homogeneous enhancement in the mass lesion (arrow) with an area of T1 hypointensity along the medial aspect of the mass (arrowheads). (c) Diffusion-weighted imaging shows high signal in the enhancing portion of the mass (arrow), confirmed to be reduced diffu-

sion (arrow) on the ADC map (d). There is an area of facilitated diffusion in keeping with vasogenic edema along the medial aspect of the mass (arrowheads). (e) CBV map from dynamic susceptibility contrast perfusion imaging demonstrates only slight elevated blood volume in a portion of the enhancing mass lesion compared to the contralateral white matter (ratio less than 1.75). (f) MRS spectrum through the mass shows some elevation of Cho/Cr and Cho/NAA ratios

Encephalitis

acteristics of encephalitides have not been well defined in the literature. Anecdotal experience and reports suggest that it is variable [3, 176, 177].

The imaging features of encephalitides are highly variable among different etiologies that cause encephalitis. On MR spectroscopy, it has been reported that they have nonspecific elevation of choline and myoinositol, reduced NAA, with or without elevated lactate. The MRS spectrum is often similar to those of low-grade gliomas [3, 175]. On diffusion imaging, the ADC values of encephalitides are quite variable, and depends on the etiology and stage of disease. Acute encephalitis may frequently have restricted diffusion. Perfusion char-

Brain Abscesses The imaging features of brain abscesses may also be variable depending on etiology (bacterial, fungal), location, and the patient’s immune status. MR spectroscopy demonstrates increased lactate, amino acids, alanine, acetate, succinate,

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

325

a

b

c

d

e

f

Fig. 12.5  Conventional and advanced MRI in a patient with brain metastases, subsequently found to have undifferentiated carcinoma of the colon. (a) Axial FLAIR image demonstrates an irregular mass in the left frontal lobe with surrounding vasogenic edema. (b) The lesion demonstrates heterogeneous, but mostly avid contrast enhancement on postcontrast T1-weighted images. (c) DWI image demonstrates that the lesion itself is relatively isointense to white matter (metastases can have variable diffusion characteristics based on type of tumor). (d) CBV map from dynamic susceptibility contrast perfusion imaging demonstrates markedly elevated blood volume in the mass lesion compared to the

contralateral white matter (see regions of interest placement). The CBV ratio between the mass and contralateral white matter in this particular case was greater than 8. There is no increased blood volume in the areas surrounding the enhancing portion of the mass. (e) MRS spectrum from a voxel placed over the enhancing portion of the mass demonstrates elevated Cho/Cr and Cho/NAA ratios. (f) MRI spectrum from a voxel placed close to, but outside the enhancing portion of the mass demonstrates normal Cho/Cr and Cho/NAA ratios, suggesting that this is likely a metastatic lesion without substantial infiltrating neoplastic tissue beyond the enhancing portions of the mass

and lack of the normal brain tissue metabolites NAA, creatine, and choline within the abscess [178, 179]. On diffusion imaging, the central necrotic portion of processes often has markedly reduced ADC. The cystic portions of tumors often have facilitated diffusion, but occasionally, some brain tumors, especially those with hemorrhage, may show central restricted diffusion. A meta-analysis of 11 studies showed that diffusion imaging can differentiate brain abscess from other ring-enhancing lesions (not just tumors) with pooled

sensitivity and specificity of 0.95 and 0.94, respectively [180]. On perfusion imaging, obviously the centrally necrotic portion of abscesses will have no or very low blood volume. The walls of abscesses typically have lower CBV measurements compared to high CBV in high-grade gliomas [3, 181]. It should be kept in mind, however, that some infectious lesions such as fungal infections and tuberculomas can have elevated rCBV values because of reactive neovascularization; but rCBV is still typically less than that for HGGs

326

A. Vossough and S. A. Nabavizadeh

a

b

c

d

e

f

Fig. 12.6  Conventional and advanced MRI of a tumefactive demyelinating lesion in a patient subsequently diagnosed with multiple sclerosis. (a) Axial FLAIR image demonstrates a round area of hyperintensity in the right cerebral hemisphere. (b) Postcontrast T1-weighted image shows mild patchy areas of enhancement within the lesion. (c) DWI image shows high signal, which on the ADC map (d) is also hyperin-

tense, suggesting facilitated diffusion (please note that tumefactive demyelinating lesions may sometimes have reduced diffusion as well). (e) CBV map derived from dynamic susceptibility contrast perfusion imaging demonstrates no increase in blood volume within the lesion. (f) MRI spectrum from a voxel placed within the lesion demonstrates elevated Cho/Cr and slightly elevated Cho/NAA ratios

[182, 183]. A recent study using DSC perfusion and DTI in 14 patients with brain infections and 21 patients with necrotic glioblastoma demonstrated that combined analysis of FA from the central core and maximum rCBV from the enhancing region provided the best classification model in distinguishing brain infections from necrotic GBMs, with a sensitivity of 91% and a specificity of 93% [183].

Pitfalls and Special Circumstances Despite the utility of advanced MRI techniques to add to the diagnostic value of conventional MRI, it is important to recognize the limitations of these techniques and the pitfalls associated with acquisition, analysis, and interpretation of these techniques. Familiarity with the basic principles, tech-

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

nical limitations, and artifacts in each of these modalities is essential for successful integration with conventional MRI findings. Within each individual institution, both technical and personnel quality control is essential to ensure optimal use of these techniques. In the acquisition phase, development and implementation of sound imaging protocols is essential. MRI technologists will need additional training for implementing these protocols to minimize artifacts and inaccurate measurements. For example, optimal placement of spectroscopy voxels and grids in order to minimize the deleterious effects of proximity to or inclusion of air, fat, and bone is essential for optimal brain tumor spectroscopic imaging. Voxel size should be optimized to minimize partial volume effects around the tumor, but at the same time be large enough to ensure an adequate signal-to-noise ratio within the acquisition timeframe. Other techniques such as parallel imaging or nonechoplanar techniques may be employed to minimize the deleterious effect of susceptibility and geometric distortion in diffusion imaging. Adequate venous access and contrast injection rates affect the success and reliability of DSC perfusion MRI. Choice of pulse sequence, use of a preload dose of contrast, slice thickness, and use of parallel imaging are important considerations in DSC perfusion MRI. Careful postprocessing analysis of spectroscopy and particularly perfusion data is also essential. The interpreting physician should have at least a basic understanding of the postprocessing steps involved. There are various models and processing methods available, and various commercial and noncommercial software packages have different interfaces and computational models for data analysis. Familiarity with the particular processing software and implementation of an internal institutional quality control of the postprocessing steps is important. Better curve fitting and baseline correction can improve the quality of spectroscopy data. Most institutions do not routinely perform quantitative spectroscopy, which requires a high level of understanding of the nuances behind MR spectroscopy, has its own challenges, and can be very time-consuming. There are various methods for perfusion imaging data analysis, application of corrections, and perfusion map reconstruction. Anecdotally, a simple but common mistake that the authors have observed in the processing of perfusion data is incorrect selection and placement of the various time points on the time concentration curves for perfusion map calculation. Visual inspection of the source time-signal intensity curves is important to assess excessive susceptibility artifacts, motion artifacts, bolus rate and timing, and correct processing of DSC perfu-

327

sion MRI data, since many artifacts cannot be adequately assessed by looking at the postprocessed perfusion parameter maps. Caution must be exercised in interpretation of diffusion and perfusion imaging when there are hemorrhagic areas within tumors. Large areas of hemorrhage may negatively affect accurate rCBV measurement. Caution must be exercised in interpreting diffusion-weighted imaging in the presence of hemorrhage. This could potentially affect differentiation of hemorrhagic brain tumors and abscesses. Significant areas of hemorrhage and calcification may also degrade MR spectroscopy and preclude obtaining a diagnostic spectrum. Detection of small highly perfused tumors in very superficial areas or within the gray matter can be difficult since the CBV of gray matter is already relatively high in normal brain. Differentiating subacute infarcts from brain tumors can occasionally be problematic, since there may be significant edema and mass effect in and around the area of infarct, and there may be heterogeneous reduced diffusion within the area of infarct itself. The patient’s clinical history and course is often helpful in this distinction. Immunosuppressed patients with CNS lymphoma may have more peripherally enhancing lesions rather than solid enhancement, and may mimic high-grade primary neoplasm and other lesions. The clinical history of immunosuppression or HIV disease would be quite helpful in these patients. The use of high dose steroids can affect the enhancement and potentially perfusion characteristics of tumors, and therefore knowledge of the patient's "steroid status" is helpful for more accurate interpretation of brain tumor MRI exams. One common diagnostic pitfall in the imaging of brain tumors is differentiating high-grade and low-grade oligodendrogliomas. Low-grade oligodendrogliomas may enhance and high-grade oligodendrogliomas may not show contrast enhancement [184]. MR perfusion imaging in oligodendrogliomas also can be confusing, as low-grade oligodendrogliomas may have elevated blood volume, mimicking high-grade tumor [134, 185]. An example of low-grade oligodendroglioma with high blood volume within the tumor is shown in Fig. 12.7. Another potential pitfall is the presence of the large veins or developmental venous anomalies (venous angiomas) in the vicinity of brain masses. The presence of developmental venous anomalies can alter perfusion MRI parameters in the area and sometimes pose as areas of increased rCBV, potentially mimicking hypervascular tumor [186]. Similar increases in rCBV may occasionally be observed within capillary telangiectasias.

328

A. Vossough and S. A. Nabavizadeh

a

b

c

d

e

f

Fig. 12.7  Conventional and advanced MRI in a patient with grade II oligodendroglioma with 1p/19q chromosomal codeletion. (a) Axial FLAIR image demonstrates a hyperintense mass lesion in the left frontal lobe. (b) Postcontrast T1-weighted images demonstrate small faint areas of enhancement within the mass. DWI images (c) and ADC maps (d) do not show reduced diffusion in the mass. (e) CBV map from

dynamic susceptibility contrast imaging demonstrates elevated blood volume within the mass compared to the contralateral white matter (see regions of interest measurements). In this particular case, the ratio was approximately 3.5. Low-grade oligodendrogliomas may enhance and have elevated blood volume within the tumor. (f) MRS performed with TE = 135 shows elevated Cho/Cr and Cho/NAA ratios

Pediatric Brain Tumors

the enhancement and perfusion characteristics of adult high grade gliomas [187]. The most common pediatric posterior fossa tumors are pilocytic astrocytomas, medulloblastomas, and ependymomas. There can be considerable conventional imaging overlap, especially between ependymomas and medulloblastoma. In a study of pediatric cerebellar tumors, it was shown that comparison of NAA/Cho and Cr/Cho ratios by MR spectroscopy can reasonably differentiate between these three tumors [188]. Medulloblastoma typically has very high choline compared to creatine and NAA. In another study, elevated Taurine on MRS (at 3.4 ppm) was shown to

There are important differences between pediatric and adult brain tumors. As such, the previously described diagnostic algorithm for characterization of intra-axial brain tumors in adults may not apply to many pediatric brain tumors. For example, pilocytic astrocytomas are one of the most common pediatric brain tumors, and despite being grade I tumors, a large percentage of these tumors partially enhance with gadolinium contrast and few could also demonstrate some increased blood volume on perfusion imaging, mimicking

12  Functional Imaging-Based Diagnostic Strategy: Intra-axial Brain Masses

be significantly elevated and useful in the differentiation of medulloblastoma from other pediatric brain tumors, although reliable detection of Taurine is not trivial [189]. Diffusion-weighted imaging can be helpful in the differentiation of medulloblastoma from ependymomas or pilocytic astrocytomas in the cerebellum (Fig.  12.8). Both medulloblastoma and the less common atypical teratoid rhabdoid tumors (ATRT) often demonstrate restricted diffusion in their solid components. However, anaplastic ependymomas can also sometimes demonstrate restricted diffusion. Rumboldt et  al. showed that quantitative ADC measurements, and also tumor ADC ratios with respect to normal a

b

d

e

Fig. 12.8  Conventional and advanced MRI in a pediatric patient with a medulloblastoma. (a) Axial CT scan demonstrates a relatively hyperdense mass in the posterior fossa (arrows), along with cystic components and calcifications. Hyperdensity in the solid portion of pediatric posterior fossa masses is suggestive of a high nuclear/cytoplasm ratio. (b) Sagittal T1-weighted MRI demonstrates a large mass, but it is difficult to determine whether it is arising from within the ventricle or vermis. Portions of the mass are protruding way down through foramen Magendie into the cervical canal (arrows) with mass effect on the brain-

329

white matter can differentiate posterior fossa pilocytic ­astrocytomas, ependymomas, and medulloblastomas/ATRT [190]. Another small study concluded that quantitative ADC ratios correlated reasonably well with pediatric tumor classifications into low grade gliomas, embryonal tumors, and non-embryonal high grade tumors [191]. In another small study of supratentorial pediatric brain tumors, it was shown that patients with lower NAA/Cho and Cr/Cho ratios have poorer prognosis [192]. This prognostic significance of the Cho/NAA ratio was confirmed in another cohort of pediatric brain tumors [193]. In a study of ASL perfusion in pediatric tumors, there was 77–88% grading accuracy in combing perc

f

stem. (c) Axial T2-weighted image shows a T2 hyperintense mass with portions of the mass protruding out from the bilateral foramina of Luschka (arrows). Classically this “toothpaste-like” appearance has been described with ependymomas, but can be seen in large medulloblastomas as well. (d) DWI images show high signal within the solid portions of the mass, confirmed to indeed be due to restricted diffusion on the ADC maps (e). (f) MRS spectroscopy demonstrates very marked elevation of the Cho/Cr and Cho/NAA ratio, also a feature often seen in medulloblastoma

330

fusion and degree of contrast enhancement in assessing pediatric low and high grade neoplasms [194].

Post-treatment Imaging In the case of high-grade gliomas, a common clinical scenario is differentiation of post-treatment effects from tumor recurrence, which can be difficult as they may clinically present similarly and also have a similar appearance on conventional MRI.  Contrast enhancement, edema, and mass effect can be seen with post-treatment effects mimicking recurrent tumor contrast enhancement [159, 195]. Another complicating factor is that in many cases, recurrent neoplasm and post-treatment effects can co-exist in the same or nearby regions, with varying proportions [196]. Integrating various advanced imaging techniques with conventional MRI has potential to more accurately determine whether an enhancing region is due to predominately high-grade tumor recurrence or post-treatment effects. This differentiation has become more important since the addition of concurrent and consolidative regimes of temozolomide (TMZ) to radiation treatment as the standard of care for GBM, [197] which has also resulted by an increasing incidence of pseudoprogression (PsP). PsP is an early post-­treatment effect which is most commonly seen in the first 3 months to up to 6 months after chemoradiation and demonstrates as increasing enhancement in the surgical bed which subsides in subsequent studies without any change in treatment. This is in contrast to classic radiation necrosis which is typically a late effect usually occurring 9–12 months or even years following photon-based radiation therapy, and which usually stabilizes or worsens rather than showing spontaneous resolution in contrary to PsP [198–200]. It has been shown that approximately 50% of the high grade glioma patients treated with standard chemoradiation can develop increasing enhancement in the surgical bed concerning for progression: however, in approximately 40% of patients, the enhancement improves or stabilize on subsequent studies indicating an approximately 20% incidence of PsP in high-­grade gliomas patients treated with chemoradiation [201]. The distinction

A. Vossough and S. A. Nabavizadeh

of PsP from true progression becomes more important noting that multiple studies demonstrated improved survival in patients who have PsP [202, 203] even when accounting for MGMT status [204], indicating that PsP can be a potential marker of enhanced antitumor efficacy. As such, it would be important to differentiate these two entities by imaging as PsP indicates success of the current treatment while true tumor progression warrant a change or treatment and even another resection. An early study showed that rCBV >2.6 (relative to contralateral side) is consistent with recurrent tumor and rCBV 0.02) [99]. The depression levels in LBP patients were highly correlated with the NAA levels in the right DLPFC (r = −0.09, p  R

L L > R

a subtraction analysis. Some commonly encountered examples are listed below and in Table 26.3.

 anguage Task: Passively Listening to Words or L Sentences Control Task: Resting As shown in Table 26.1, auditory words activate early auditory cortices and auditory word form areas. Because both resting and passive stimulation are accompanied by spontaneous semantic processes and make no other overt cognitive demands, no other language systems should appear in the contrast. These predictions are confirmed by many studies employing this contrast, which results primarily in activation of the superior temporal gyrus auditory cortex bilaterally (Fig. 26.2a) [152, 184, 185]. The activation is relatively symmetrical and is unrelated to language dominance as measured by Wada testing [20].

 anguage Task: Passively Listening to Words or L Sentences  ontrol Task: Passively Listening to Nonspeech C Because there are no differences in task requirements in this contrast, and because semantic processing occurs in all pas-

Ventrolateral temporal

Ventral occipital

(L > R)

(B)

L > R L

B

L L > R

Angular gyrus

L L

L > R

sive conditions, the activation pattern depends mainly on acoustic differences between the speech and nonspeech stimuli. Studies employing such contrasts reliably show stronger activation by words in the middle and anterior superior temporal sulcus, with some leftward lateralization, and little or no activation elsewhere (Fig.  26.2b) [152, 170, 186–188]. Similar patterns are observed whether the speech stimuli are words or pseudowords [152].

Language Task: Word Generation  ontrol Task: Resting or Fixation C The word generation task requires the participant to think of a word in response to a cue of some kind. In some protocols, a single cue (e.g., a letter, word, or semantic category) is provided only at the beginning of an activation period; in others, a different cue is provided every few seconds. Because the resting state includes no control for sensory processing, early auditory or visual cortices may be activated bilaterally depending on the sensory modality of the cue stimulus (Table 26.1), though the strength of this activation depends on the rate of cue presentation. Unlike resting, word generation makes demands on phonological access, verbal working memory, and lexical search systems (Table  26.2). Speech articulation systems will also be activated if an overt spoken

572

J. R. Binder

a

b

c

d

Fig. 26.2  Group average fMRI activation patterns in neurologically normal, right-handed volunteers during four language paradigms. (a) Passive listening to spoken words contrasted with resting (28 participants). Superior temporal activation occurs bilaterally. (b) Passive listening to spoken words contrasted with passive listening to tones (same 28 participants as in a). Superior temporal sulcus activation occurs bilaterally, more prominent on the left. (c) Semantic decision on auditory words contrasted with a tone decision control task (30 participants). Activation is strongly left lateralized in inferior frontal,

dorsomedial prefrontal, lateral and ventral temporal, angular, and posterior cingulate cortices. (d) Semantic decision on auditory words contrasted with a phonological task using pseudowords (same 30 participants as in c). Activation is strongly left lateralized in dorsomedial prefrontal, angular, ventral temporal, and posterior cingulate cortices. There is no activation of Broca’s or Wernicke’s area. The images are serial axial sections spaced at 15-mm intervals through stereotaxic space, starting at z = −15. The left hemisphere is on the reader’s left

26  fMRI of Language Systems: Methods and Applications

response is used. These predictions are confirmed by many studies employing this contrast, which mainly activates the left inferior frontal gyrus and left greater than right premotor cortex, systems thought to be involved in phonological production, verbal working memory, and lexical search [19, 20, 184, 189–192]. There may be weak activation of left ­posterior temporal or ventral temporal regions due to engagement of auditory or visual word form systems.

Language Task: Word Generation  ontrol Task: Reading or Repeating C This protocol is similar to the previous one, but with a control task using words in the same stimulus modality. The matched control stimuli remove any activation of sensory or word form systems. Both tasks are accompanied by semantic processing (automatic semantic access in the case of the control task, effortful semantic retrieval in the case of word generation) and phonological access processes. The word generation task makes greater demands on lexical search and on working memory; consequently, greater activation is expected in left inferior frontal areas associated with these processes. These predictions match findings in many studies using this contrast [119, 120, 189, 193, 194].

Language Task: Visual Object Naming  ontrol Task: Resting or Fixation C Compared to resting, visual objects activate early visual sensory cortices and object recognition systems bilaterally (Table 26.1) [195, 196]. There may be additional, left lateralized activation in semantic systems of the ventrolateral posterior temporal lobe [197–200]. Unlike resting, naming requires lexical search and phonological access, and, when overt, speech articulation (Table  26.2). These predictions match findings in several studies using this contrast, which show extensive bilateral visual system activation and modest left lateralized inferior frontal activation [19, 199–201].

Language Task: Semantic Decision  ontrol Task: Sensory Discrimination C These tasks can be given in either the visual or auditory modality. If the stimuli used for the sensory discrimination task are nonlinguistic (e.g., tones or nonsense shapes), then the semantic decision task will produce relatively greater activation in auditory or visual word form systems, depending on the sensory modality. In addition, there will be greater activation of phonological access, semantic, and semantic search mechanisms in the semantic decision task. Working memory systems may or may not be activated,

573

depending on whether or not the sensory task also has a working memory demand. These predictions match findings in studies using this contrast, which show left lateralized activation of auditory (middle and anterior superior temporal sulcus) or visual (mid-fusiform gyrus) word form regions and extensive activation of left prefrontal, lateral and ventral left temporal, and left posterior parietal systems involved in semantic processing (Fig. 26.2c) [5, 21, 30, 33, 122, 150, 202–205].

Language Task: Semantic Decision  ontrol Task: Phonological or Phonetic Decision C This protocol is similar to the previous one but incorporates a phonological control to isolate semantic processing. The stimuli used for phonological decision are either words or pseudowords. Thus, there are no physical differences between the stimuli, and no difference in activation of sensory or word form systems is expected. There will be greater activation of semantic systems and semantic search mechanisms in the semantic decision task. These predictions match findings in many studies using this type of contrast, which show activation of left prefrontal, lateral and ventral left temporal, and left posterior parietal systems in semantic retrieval (Fig. 26.2d) [122, 137, 161, 206–210].

Language Task: Sentence or Word Reading  ontrol Task: Viewing Letter Strings or Visual C Nonletters Compared to letter strings and nonletter characters, sentences engage visual word form, syntactic, and phonological access systems. Working memory is engaged to varying degrees depending on the length of the sentences, presentation speed, and task difficulty. Semantic processing also likely increases with task difficulty but probably occurs to some extent even with passive reading. There should be relative left lateralized activation of the occipitotemporal (visual word form) region, posterior superior temporal gyrus and STS (phonological access), and inferior frontal gyrus (phonological control, working memory, syntax). These predictions are consistent with many studies using this contrast [108, 211–216]. These examples cover only a small sample of the possible language activation protocols. There are also numerous published studies employing designs that do not fit neatly into the schema provided here. Many of these represent attempts to further define or fractionate a particular language process or to define further the functional role of a specific brain region. The reader should appreciate that the review given here is merely a coarse outline of some of the most commonly used types of stimuli and tasks. Above all, it is impor-

574

J. R. Binder

tant to note that activations in a particular part of the language system are seldom “all or none,” but vary in a graded way depending on the particular stimuli and tasks used. In many clinical settings, the main goal of language mapping is simply to identify as many language-related areas as possible and to assess hemispheric lateralization of language. A review of Table 26.3 suggests that the “Semantic Decision vs. Sensory Discrimination” paradigm may offer advantages for this purpose in terms of the sheer number of regions activated and leftward lateralization of activation. Binder et al. put this prediction to a quantitative test by comparing the extent and lateralization of activation produced by five language-­related

task contrasts, conducted on the same 26 participants during a single scanning session [150]. These contrasts included: (1) passively listening to words vs. resting, (2) passively listening to words vs. passively listening to tones, (3) performing a semantic decision task with words vs. resting, (4) performing a semantic decision task with words vs. a sensory discrimination task with tones, and (5) performing a semantic decision task with words vs. a phonological task with pseudowords. As shown in Fig.  26.3, the Semantic Decision—Tone Decision contrast produced by far the largest activation volume in the left hemisphere as well as an optimal combination of extensive activation and strong left lateralization [150].

Fig. 26.3  Group average activation volumes (top graph) and laterality indexes (bottom graph) for five fMRI language paradigms [150]. Laterality indexes can vary from −1 (all activation in the right hemisphere) to +1 (all activation in the left hemisphere). Error bars represent

standard error. The Semantic Decision—Tone Decision paradigm produces the greatest left hemisphere activation as well as a strongly left lateralized pattern

26  fMRI of Language Systems: Methods and Applications

575

 anguage Lateralization: Normative, L Reliability, and Validation Studies As with any clinical test, the applicability of language mapping techniques to clinical patients depends on the reliability and validity of the results. It goes without saying that any imaging protocol applied to patients should first be tested in a sample of healthy volunteers. The initial aims of gathering normative data in this case are: (1) to verify the feasibility of the procedure and estimate the likelihood of obtaining uninterpretable results (i.e., test failures), (2) to verify that activation occurs in the expected brain regions and is lateralized to the left hemisphere in right-handed participants, (3) to estimate the range of intersubject variability that occurs in the normal population, and (4) to determine the average test– retest reproducibility of the results. If significant variability in results is observed, a secondary aim is to determine some of the factors (e.g., age, sex, handedness) associated with this variability.

Normative Studies Several language mapping protocols have been carried out in relatively large samples of healthy participants [204, 217– 224]. All of these protocols produced left lateralized activation patterns in right-handed participants. Lateralization has been quantified in most of these studies using some type of left–right difference score. One commonly used version is based on the left–right difference in the number of activated voxels (activation volume), normalized by the total number of activated voxels (i.e., [L − R]/[L + R]). This lateralization index (LI) varies from −1 (all activated voxels in the right hemisphere) to +1 (all activated voxels in the left hemisphere). The result depends on the statistical threshold used to classify voxels as “active” and tends to increase with increasingly stringent thresholds due to the elimination of false-positive voxels in both hemispheres [24, 225]. For this reason, several authors have developed alternative asymmetry measures that do not require thresholding or produce a weighted average across multiple thresholds [226–229]. Others have advocated measures based on magnitude rather than volume of activation [19, 24, 192, 201]. No direct comparisons between these many methods in terms of their ability to predict outcomes has yet been conducted. LIs can be computed for the entire hemisphere or for homologous regions of interest (ROIs). Focusing on language-related ROIs avoids the problem of nonspecific or nonlanguage activation in bilateral sensory, motor, and executive systems that is characteristic of some task contrasts [201, 225]. Figure 26.4 shows the intersubject variability observed for one such LI.  The participants were 100 right-handed healthy adults; they were scanned during a block design

Fig. 26.4  Frequency distribution of a language LI in 100 healthy participants. (Adapted from [217] by permission of Oxford University Press)

fMRI protocol contrasting an auditory word semantic decision task with an auditory nonspeech sensory discrimination task [217]. LIs in this group ranged from strong left dominance (LI  =  0.97) to roughly symmetrical representation (LI = −0.05), with a group median LI of 0.66. Using a dominance classification scheme based on a cutoff LI value of ±0.20, 94% of participants were classified as left dominant, 6% were symmetrical, and none had right dominance. Thus, although LI values ranged widely, the vast majority of participants were left hemisphere dominant. Similar variability in lateralization among healthy, right-handed participants was observed in other large studies [218, 222, 223, 230]. Several studies have attempted to identify subject variables associated with language lateralization. One group of investigators, using fMRI to contrast a visual phonological decision task with a visual letter string orthographic decision task (a contrast likely to activate visual word form and phonological access systems), found significant effects of gender on lateralization, particularly in the frontal lobe, with women showing relative symmetry of activation and men showing leftward lateralization [231, 232]. Other PET [121, 233], fMRI [203, 204, 218, 221, 222, 224, 234], and functional transcranial Doppler [230] studies, together involving several thousand healthy participants, have failed to find differences between men and women in terms of lateralization of language functions [235–237]. Several large series have documented a relative rightward shift of language functions in left-handed and ambidextrous participants compared to right-handed participants [204, 218, 223, 224, 238, 239]. It is important to note, however, that this difference reflects a group tendency only, due to the fact that a larger minority (20–25%) of non-right-handed people are symmetrical or right dominant for language. Most left-handed and ambidextrous participants are, like right-handers, left-dominant for language. These estimates of language dominance and hand-

576

edness effects in healthy participants agree very well with older Wada language studies in patients with late-onset seizures [240, 241]. Several studies reported age effects on language dominance in adults, manifested as a decline in the LI (greater symmetry of language processing) with increasing age [204, 217, 222]. Similar declines in hemispheric specialization have been observed for other cognitive domains [242, 243] and may reflect recruitment of homologous functional regions as compensation for age-related declines in neural functional capacity. Level of education had no effect on LI in the one study in which it was assessed [217]. Two fMRI studies directly compared LI distributions from healthy participants and patients with epilepsy [24, 217]. Both studies included only right-handed individuals to avoid confounding effects of handedness. Patients with ­epilepsy had a higher incidence of atypical (symmetric or right-­lateralized) language dominance; this was particularly true for patients with left-sided seizure foci [24]. Several fMRI studies examined factors within epilepsy cohorts that predict language lateralization. Some, but not all, have observed relationships between atypical language lateralization and left-handedness [217, 244, 245]. In one study, there was a clear relationship between LI and age of onset of seizures (r  =  0.50, p   1000 s/mm2), to reduce sensitivity to venous flow, can be used for this analysis. The authors of this study demonstrated promising results indicating that water diffusion, thought to be reflective of glymphatic flow, correlated significantly with measures of cognitive performance in older adults with Alzheimer’s disease. It is also possible to interrogate the structure and function of the CNS lymphatic system thought to potentially be related to the glymphatic system using intravenous contrast agents of different size. This strategy has been used to visualize potential meningeal lymphatic vessels directly in vivo in both humans and nonhuman primates [6]. Here, T2-weighted FLAIR MRI images were acquired before and after injection of either gadobutrol, a gadolinium-based contrast agent capable of traversing the semipermeable capillary barrier, or larger gadofosveset (molecular weight of approximately 67 kDa), which is not capable of traversing this barrier and remains exclusively intravascular. In both nonhuman primates (marmoset monkeys) and humans, conspicuous hyperintense signal surrounding dural venous sinuses was observed following gadobutrol but not gadofosveset injection, suggestive of vessels distinct from the blood pool. This ­hyperintense signal demonstrated a curvilinear orientation typical of vessels. On T2-weighted FLAIR imaging following the administration of gadobutrol, the venous sinuses themselves and other known blood vascular structures did not enhance, allowing for selective imaging characterization of these distinct vessels. Additionally, immunohistochemistry was performed on the apparent nonhuman primate lymphatic vessels, which demonstrated that these vessels displayed a similar panel of endothelial markers as what is present in peripheral lymphatic vessels. While intrathecal gadolinium contrast injection is not approved by the Food and Drug Administration in the United States, gadolinium contrast is occasionally injected into the intrathecal space in challenging cases where a CSF leak is suspected. This clinical scenario has allowed for an imaging description of normal CSF flow in this pseudo control population. These studies have demonstrated that fluid injected into the intrathecal space in the lumbar region progressively ascends through the subarachnoid space of the spine and enters the basal cisterns of the intracranial compartment [47]. Importantly, contrast is then noted to extend along the surfaces of the brain with particular concentration along the course of the anterior and middle cerebral arteries. The contrast is noted to then progressively spread from the extra-­ axial space to the surface of the brain. Contrast then subsequently extends to the deeper brain parenchyma. These findings are consistent with earlier animal studies documenting glymphatic flow and are highly supportive of a functional glymphatic system in humans.

M. J. Donahue et al.

 otential Relevance of Glymphatic P Dysfunction in Disease Sleep Shortly after animal studies demonstrated that subarachnoid fluid communicates with interstitial fluid within the brain parenchyma, Xie and colleagues demonstrated that glymphatic flow in mice was highly correlated with sleeping and the anesthetized state [41]. Glymphatic flow in mice was essentially nonfunctional in the awake state. The authors of this study demonstrated that the extracellular space of the brain parenchyma was markedly increased during sleep and the anesthetized state as compared the awake state. In contrast, intracellular volume was decreased during sleep and the anesthetized state. Another important observation from this study was that the clearance of Αβ protein was highly correlated with the sleep-wake cycle and the anesthetized state, with clearance occurring essentially exclusively in the sleeping or the anesthetized state. Taken together with previous observations that Αβ protein is often associated with Alzheimer’s disease and that Αβ protein is found within the perivascular space and glymphatic pathway, this study provided compelling evidence that impaired waste clearance in the setting of a dysfunctional glymphatic pathway could contribute to Alzheimer’s pathophysiology.

Normal Pressure Hydrocephalus Normal Pressure Hydrocephalus (NPH) remains a relatively poorly understood disease where there is disproportionate dilatation of the ventricular system relative to the prominence of the subarachnoid space. These patients are classically characterized clinically with the triad of gait apraxia, urinary incontinence and dementia. The presentation however is inconsistent though with many patients not exhibiting all three characteristics. The patients by definition do not have elevated intracranial pressure measurements. In some carefully selected patients, CSF diversion via shunting can significantly reduce symptoms in patients with this condition. MRI has been helpful in the selection of patients for shunting, as higher stroke volumes at the level of the cerebral aqueduct on MRI CSF velocity studies have been shown to have higher rates of clinical improvement following shunting [48]. Despite the presence of a treatment that prompts improved symptomatology in some patients, the pathophysiology of NPH is poorly understood. It has been speculated that NPH is the end result of long term dysfunction of the glymphatic system in those individuals where the traditional route of CSF absorption by the arachnoid villa is compromised [49].

59  Anatomical and Functional Features of the Central Nervous System Lymphatic System

More recently, studies documenting the glymphatic circulation of contrast on MRI following intrathecal gadolinium contrast injection have prompted a more detailed understanding of compromised CSF flow in this population. Abnormal reflux of CSF was demonstrated to fill the enlarged ventricular system following lumbar intrathecal injection of contrast [47]. This is a known feature observed on nuclear medicine cisternography which is often employed in the NPH workup. The major novel finding from this study was that compared to a pseudo control population, NPH patients demonstrated delayed perivascular contrast opacification at the brain surface and delay in subsequent enhancement of the deeper brain parenchyma. This flow of fluid from the subarachnoid space to the periarterial space and then to the brain parenchyma is precisely that of the inflow into the glymphatic system observed in animal studies. Thus, this delayed flow in NPH patients, seen directly in human MR imaging, is highly supportive on an impaired glymphatic system in the NPH population.

Cerebrovascular Disease Several studies have provided relevance of the glymphatic system in cerebrovascular disease. In a nonhuman primate model, the introduction of subarachnoid hemorrhage was demonstrated to result in the occupation of the perivascular space of the remote brain with blood product. Furthermore, introduction of subarachnoid hemorrhage profoundly limited glymphatic flow of fluid, as seen following the injection of gadolinium contrast [50]. It has been hypothesized that blockage of perivascular spaces by blood products is possible, and this hypothesis has been supported by studies that have shown that intraventricular injection of tissue plasminogen activator (tPA) improves CSF flow [50, 51]. Studies such as these suggest that poor fluid exchange between the CSF and interstitial fluid may be implicated in diverse types of CNS dysfunction. Additionally, glymphatic dysfunction may predispose patients with subarachnoid hemorrhage to more serious complications such as vasospasm, which is itself incompletely characterized. While calcium-dependent mechanisms are established as a contributing factor to vasospasm, the development of therapeutic interventions is substantially hindered by a poor understanding of the overall underlying pathophysiology [52]. As seen in the animal studies referenced above, subarachnoid hemorrhage preferentially and progressively concentrates along the glymphatic inflow circuit in the arterial perivascular space. This arterial perivascular space surrounds the vessels which are most prone to vasospasm. Furthermore, the time required for blood to travel from the site of hemorrhage in the subarachnoid space to perivascular space mimics the well-established delay that

1385

is seen between hemorrhage and initiation of vasospasm. Thus, investigation into potential glymphatic compromise as a predisposing factor underlying the development of vasospasm is merited. In ischemic stroke, glymphatic clearance has also been implicated. Using gadolinium injection into the cisterna magna of rodents following middle cerebral artery occlusion, it was shown that subarachnoid CSF flow was reduced in the acute window postocclusion (approximately 3 h) but recovered after arterial recanalization [51]. It has also been demonstrated that following middle cerebral artery occlusion in mice, fluorescent tracers present in the infarct core were found to be transported through the glial scar and out of the brain along perivascular spaces [53]. These studies provide evidence that CSF clearance dysfunction and potential glymphatic flow may be relevant for ischemic stroke outcomes; however, much more work is required to further identify these pathways and if present, understand how they can be leveraged to triage patients for acute stroke therapies. While there is limited convincing data linking cerebral small vessel disease to potential CSF and glymphatic clearance dysfunction, there are multiple avenues in which this link could exist and several studies have provided evidence in support of this possibility, albeit indirectly. White matter lesion burden is largely associated with cognitive performance in older adults. In rats, amount of extravascular imaging tracers, indicative of blood–brain barrier dysfunction, has been shown to be associated with cognitive performance in models of vascular dementia and diabetes [54, 55]. It is logical that reduced interstitial fluid flow and associated solute uptake in the interstitial space could have toxic implications, including development of white matter lesions, lacunes, and other types of small vessel disease. Currently this link remains largely speculative; however, given the poorly characterized etiology of many variants of small vessel disease and their known link with cognitive dysfunction, it is reasonable that glymphatic pathways may have relevance in this condition as well.

Multiple Sclerosis Glymphatic dysfunction has also been implicated in neuroinflammatory disorders. Multiple sclerosis is the most extensively characterized of these conditions and can be characterized by oligodendrocyte demyelination as a result of autoimmune dysfunction. As such, immune cell access to their cellular targets, which is generally prevented by the blood–brain barrier, is fundamental to disease onset and likely progression. Perivascular astroglial compartmentalization may be compromised in patients with multiple sclerosis, and evidence has been provided for this possibility

M. J. Donahue et al.

1386

through rodent models of autoimmune encephalomyelitis in which disease progression was found to be associated with poor compartmentalization of immune cells [56]. In this same model, reduced AQP4 localization is observed, which could be consistent with inadequate localization of immune cells to intended compartments and possible routes for these immune cells to their pathological cellular targets. Finally, the demyelinating disorder neuromyelitis optica shares many phenotypical hallmarks as multiple sclerosis, but neuromyelitis optica patients also test positive for AQP4 auto-­ antibodies. The logical extension of this finding is that if AQP4 function is disrupted in such patients, then it follows that glymphatic flow and CSF clearance dynamics should be altered as well. Though by slightly different mechanisms, the sequela of these neuroinflammatory disorders is that neurotoxic solutes may accumulate in the parenchyma and/or interstitial spaces, and such accumulation may lead to neurodegeneration.

Alzheimer’s Disease Alzheimer’s disease is known to be closely associated with extracellular deposition of Αβ-containing plaques [57], and progressive cognitive impairment. While the precise role of Αβ as an initiating factor in AD pathogenesis is debated [58], Αβ brain burden measured with PET is a well-known hallmark of Alzheimer’s disease pathology, and the origins of Αβ clearance deficiencies have been broadly association with vascular risk factors [59]. However, it remains unknown as to why Αβ accumulation begins in subjects in early clinical Alzheimer’s disease stages, typically referred to as mild cognitive impairment (MCI). A logical explanation is that Αβ accumulation is partly associated with abnormal glymphatic function [8], and furthermore, Αβ production rate has been shown not to be different in Alzheimer’s disease and cognitively intact controls, yet the rate of Aβ clearance has been observed to be reduced in Alzheimer’s disease patients [57]. Aβ clearance is known to be partially attributable to proteolytic degradation, efflux across the blood–brain barrier, phagocytosis by microglia, and clearance along cerebrovascular spaces [8, 57, 60–62]; however, pathology of these systems has not been shown to be uniquely present in subjects progressing to Alzheimer’s disease. Aβ accumulation in MCI patients is of particular interest in clinical research, as the stage represents a potential pathophysiological intervention point. The aging process alone results in Aβ accumulation, e.g., older adults (55+ years) without MCI have been demonstrated to have 17% more Aβ accumulation (per PET) than younger adults [63]. However, older adults (55+ years) with amnestic MCI (aMCI) have been demonstrated to have 22% more Aβ accumulation (per PET) than their non-MCI peers [63]. The Aβ accumulation

(per PET) does not seem limited to aMCI patients, with findings suggesting mixed-dysexecutive MCI older adults have the highest frequency (73%) of reaching amyloid positivity per PET compared to aMCI (63%) or non-MCI controls (34%) [64]. In preliminary data, evidence for reduced glymphatic flow in humans measured using the above DTI-ALPS methods has been suggested. In this work [46], it was shown in elderly adults with cognitive complaints that the degree of cognitive impairment as quantified by a basic mini-mental state exam was inversely correlated with the extent of glymphatic flow as quantified by the DTI-ALPS measure.

Conclusion For approximately three centuries, a peripheral lymphatic clearance system has been known to be present and to be central to waste clearance, fluid balance, and host defense. During this period, it was largely believed that the CNS was devoid of a lymphatic system. Over the past decade, it has been suggested that a so-called glymphatic system may be operative in the CNS, which is believed to function to clear waste products via an alternative pathway of CSF, interstitial fluid, and possibly meningeal lymphatic vessels. Central to this system is movement of CSF along periarterial spaces, into the interstitial space via transport through AQP4 channels located on the astrocytic endfeet, and out of the CNS via perivenous spaces. Importantly, while this system is referred to frequently as a CNS lymphatic clearance system, as proposed it has only very limited overlap with the conventional model of capillary filtrate and interstitial fluid transport via lymphatic capillaries and collectors that occur in the rest of the body. However, operative glymphatic clearance dysfunction may have central relevance to many CNS disorders with unknown etiology, including Alzheimer’s disease and neuroinflammatory conditions. The development of new, sensitive imaging measures capable of interrogating the central processes of the glymphatic system will be imperative to understand the relevance of this system in health and disease.

References 1. Iliff JJ, Chen MJ, Plog BA, Zeppenfeld DM, Soltero M, Yang L, et al. Impairment of glymphatic pathway function promotes tau pathology after traumatic brain injury. J Neurosci. 2014;34(49):16180–93. 2. Iliff JJ, Nedergaard M. Is there a cerebral lymphatic system? Stroke. 2013;44(6 Suppl 1):S93–5. 3. Louveau A, Plog BA, Antila S, Alitalo K, Nedergaard M, Kipnis J.  Understanding the functions and relationships of the glymphatic system and meningeal lymphatics. J Clin Invest. 2017;127(9):3210–9. 4. Aspelund A, Antila S, Proulx ST, Karlsen TV, Karaman S, Detmar M, et al. A dural lymphatic vascular system that drains brain interstitial fluid and macromolecules. J Exp Med. 2015;212(7):991–9.

59  Anatomical and Functional Features of the Central Nervous System Lymphatic System 5. Sun BL, Wang LH, Yang T, Sun JY, Mao LL, Yang MF, et  al. Lymphatic drainage system of the brain: a novel target for intervention of neurological diseases. Prog Neurobiol. 2017;163:118–43. 6. Absinta M, Ha SK, Nair G, Sati P, Luciano NJ, Palisoc M, et al. Human and nonhuman primate meninges harbor lymphatic vessels that can be visualized noninvasively by MRI. Elife. 2017;6:e29738. 7. Rasmussen MK, Mestre H, Nedergaard M. The glymphatic pathway in neurological disorders. Lancet Neurol. 2018;17(11):1016–24. 8. Boespflug EL, Iliff JJ.  The emerging relationship between interstitial fluid–cerebrospinal fluid exchange, amyloid-beta, and sleep. Biol Psychiatry. 2018;83(4):328–36. 9. Simon MJ, Iliff JJ. Regulation of cerebrospinal fluid (CSF) flow in neurodegenerative, neurovascular and neuroinflammatory disease. Biochim Biophys Acta. 2016;1862(3):442–51. 10. Shokri-Kojori E, Wang GJ, Wiers CE, Demiral SB, Guo M, Kim SW, et  al. Beta-amyloid accumulation in the human brain after one night of sleep deprivation. Proc Natl Acad Sci U S A. 2018;115(17):4483–8. 11. Ruddle NH. Lymphatic vessels and tertiary lymphoid organs. J Clin Invest. 2014;124(3):953–9. 12. Stranford S, Ruddle NH.  Follicular dendritic cells, conduits, lymphatic vessels, and high endothelial venules in tertiary lymphoid organs: parallels with lymph node stroma. Front Immunol. 2012;3:350. 13. Loukas M, Abel N, Shane Tubbs R, Grabska J, Birungi J, Anderson RH. The cardiac lymphatic system. Clin Anat. 2011;24(6):684–91. 14. Miller MJ, McDole JR, Newberry RD. Microanatomy of the intestinal lymphatic system. Ann N Y Acad Sci. 2010;1207(Suppl.1):1–11. 15. Alitalo K, Carmeliet P. Molecular mechanisms of lymphangiogenesis in health and disease. Cancer Cell. 2002;1(3):219–27. 16. Choi I, Lee S, Hong YK. The new era of the lymphatic system: no longer secondary to the blood vascular system. Cold Spring Harb Perspect Med. 2012;2(4):a006445. 17. Leak LV, Burke JF. Fine structure of the lymphatic capillary and the adjoining connective tissue area. Am J Anat. 1966;118(3):785–809. 18. Aukland K, Reed RK. Interstitial-lymphatic mechanisms in the control of extracellular fluid volume. Physiol Rev. 1993;73(1):1–78. 19. Mortimer PS, Rockson SG. New developments in clinical aspects of lymphatic disease. J Clin Invest. 2014;124(3):915–21. 20. Swartz MA.  The physiology of the lymphatic system. Adv Drug Deliv Rev. 2001;50(1–2):3–20. 21. Randolph GJ, Miller NE. Lymphatic transport of high-density lipoproteins and chylomicrons. J Clin Invest. 2014;124(3):929–35. 22. Wong BW, Wang X, Zecchin A, Thienpont B, Cornelissen I, Kalucka J, et al. The role of fatty acid beta-oxidation in lymphangiogenesis. Nature. 2017;542(7639):49–54. 23. Levick JR, Michel CC.  Microvascular fluid exchange and the revised starling principle. Cardiovasc Res. 2010;87(2):198–210. 24. Woodcock TE, Woodcock TM. Revised starling equation and the glycocalyx model of transvascular fluid exchange: an improved paradigm for prescribing intravenous fluid therapy. Br J Anaesth. 2012;108(3):384–94. 25. Kinmonth JB, Wolfe JH.  Fibrosis in the lymph nodes in primary lymphoedema. Histological and clinical studies in 74 patients with lower-limb oedema. Ann R Coll Surg Engl. 1980;62(5):344–54. 26. Grada AA, Phillips TJ. Lymphedema: pathophysiology and clinical manifestations. J Am Acad Dermatol. 2017;77(6):1009–20. 27. DiSipio T, Rye S, Newman B, Hayes S. Incidence of unilateral arm lymphoedema after breast cancer: a systematic review and meta-­ analysis. Lancet Oncol. 2013;14(6):500–15. 28. Cormier JN, Askew RL, Mungovan KS, Xing Y, Ross MI, Armer JM. Lymphedema beyond breast cancer: a systematic review and meta-analysis of cancer-related secondary lymphedema. Cancer. 2010;116(22):5138–49. 29. Alitalo K.  The lymphatic vasculature in disease. Nat Med. 2011;17(11):1371–80.

1387

30. Donahue PM, Crescenzi R, Scott AO, Braxton V, Desai A, Smith SA, et al. Bilateral changes in deep tissue environment after manual lymphatic drainage in patients with breast cancer treatment-related lymphedema. Lymphat Res Biol. 2017;15(1):45–56. 31. Pallin DJ, Camargo CA Jr, Schuur JD.  Skin infections and antibiotic stewardship: analysis of emergency department prescribing practices, 2007–2010. West J Emerg Med. 2014;15(3):282–9. 32. Tawakol A, Ishai A, Li D, Takx RA, Hur S, Kaiser Y, et  al. Association of arterial and lymph node inflammation with distinct inflammatory pathways in human immunodeficiency virus infection. JAMA Cardiol. 2017;2(2):163–71. 33. Goldblatt SA, Nadel EM. Cancer cells in the circulating blood: a critical review II. Acta Cytol. 1965;9:6–20. 34. Mosconi L, Berti V, Glodzik L, Pupi A, De Santi S, de Leon MJ. Pre-­ clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging. J Alzheimer’s Dis. 2010;20(3):843–54. 35. Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol. 2010;6(2):67–77. 36. Shprecher D, Schwalb J, Kurlan R.  Normal pressure hydrocephalus: diagnosis and treatment. Curr Neurol Neurosci Rep. 2008;8(5):371–6. 37. Torkildsen O, Myhr KM, Bo L.  Disease-modifying treatments for multiple sclerosis—a review of approved medications. Eur J Neurol. 2016;23(Suppl 1):18–27. 38. Fletcher JM, Lalor SJ, Sweeney CM, Tubridy N, Mills KH. T cells in multiple sclerosis and experimental autoimmune encephalomyelitis. Clin Exp Immunol. 2010;162(1):1–11. 39. Bakshi R, Thompson AJ, Rocca MA, Pelletier D, Dousset V, Barkhof F, et al. MRI in multiple sclerosis: current status and future prospects. Lancet Neurol. 2008;7(7):615–25. 40. Iliff JJ, Wang M, Liao Y, Plogg BA, Peng W, Gundersen GA, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid beta. Sci Transl Med. 2012;4(147):147ra11. 41. Xie L, Kang H, Xu Q, Chen MJ, Liao Y, Thiyagarajan M, et  al. Sleep drives metabolite clearance from the adult brain. Science. 2013;342(6156):373–7. 42. Kress BT, Iliff JJ, Xia M, Wang M, Wei HS, Zeppenfeld D, et al. Impairment of paravascular clearance pathways in the aging brain. Ann Neurol. 2014;76(6):845–61. 43. Pizzo ME, Wolak DJ, Kumar NN, Brunette E, Brunnquell CL, Hannocks MJ, et al. Intrathecal antibody distribution in the rat brain: surface diffusion, perivascular transport and osmotic enhancement of delivery. J Physiol. 2018;596(3):445–75. 44. Rane S, Donahue PM, Towse T, Ridner S, Chappell M, Jordi J, et al. Clinical feasibility of noninvasive visualization of lymphatic flow with principles of spin labeling MRI imaging: implications for lymphedema assessment. Radiology. 2013;269(3):893–902. 45. Crescenzi R, Donahue PM, Braxton VG, Scott AO, Mahany HB, Lants SK, et  al. 3.0 T relaxation time measurements of human lymph nodes in adults with and without lymphatic insufficiency: implications for magnetic resonance lymphatic imaging. NMR Biomed. 2018;31:e4009. 46. Taoka T, Masutani Y, Kawai H, Nakane T, Matsuoka K, Yasuno F, et al. Evaluation of glymphatic system activity with the diffusion MRI technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer’s disease cases. Jpn J Radiol. 2017;35(4):172–8. 47. Ringstad G, Vatnehol SAS, Eide PK.  Glymphatic MRI in idiopathic normal pressure hydrocephalus. Brain J Neurol. 2017;140(10):2691–705. 48. Bradley WG Jr, Scalzo D, Queralt J, Nitz WN, Atkinson DJ, Wong P.  Normal-pressure hydrocephalus: evaluation with cerebrospinal fluid flow measurements at MRI imaging. Radiology. 1996;198(2):523–9.

1388 49. Bradley WG Jr. CSF flow in the brain in the context of normal pressure hydrocephalus. AJNR Am J Neuroradiol. 2015;36(5):831–8. 50. Goulay R, Flament J, Gauberti M, Naveau M, Pasquet N, Gakuba C, et  al. Subarachnoid hemorrhage severely impairs brain parenchymal cerebrospinal fluid circulation in nonhuman primate. Stroke. 2017;48(8):2301–5. 51. Gaberel T, Gakuba C, Goulay R, Martinez De Lizarrondo S, Hanouz JL, Emery E, et  al. Impaired glymphatic perfusion after strokes revealed by contrast-enhanced MRI: a new target for fibrinolysis? Stroke. 2014;45(10):3092–6. 52. Kolias AG, Sen J, Belli A.  Pathogenesis of cerebral vasospasm following aneurysmal subarachnoid hemorrhage: putative mechanisms and novel approaches. J Neurosci Res. 2009;87(1):1–11. 53. Zbesko JC, Nguyen TV, Yang T, Frye JB, Hussain O, Hayes M, et  al. Glial scars are permeable to the neurotoxic environment of chronic stroke infarcts. Neurobiol Dis. 2018;112:63–78. 54. Venkat P, Chopp M, Zacharek A, Cui C, Zhang L, Li Q, et al. White matter damage and glymphatic dysfunction in a model of vascular dementia in rats with no prior vascular pathologies. Neurobiol Aging. 2017;50:96–106. 55. Jiang Q, Zhang L, Ding G, Davoodi-Bojd E, Li Q, Li L, et  al. Impairment of the glymphatic system after diabetes. J Cereb Blood Flow Metab. 2017;37(4):1326–37. 56. Bartholomaus I, Kawakami N, Odoardi F, Schlager C, Miljkovic D, Ellwart JW, et  al. Effector T cell interactions with meningeal vascular structures in nascent autoimmune CNS lesions. Nature. 2009;462(7269):94–8.

M. J. Donahue et al. 57. Mawuenyega KG, Sigurdson W, Ovod V, Munsell L, Kasten T, Morris JC, et  al. Decreased clearance of CNS beta-amyloid in Alzheimer’s disease. Science. 2010;330(6012):1774. 58. Golde TE, Schneider LS, Koo EH.  Anti-abeta therapeutics in Alzheimer’s disease: the need for a paradigm shift. Neuron. 2011;69(2):203–13. 59. Casserly I, Topol E. Convergence of atherosclerosis and Alzheimer’s disease: inflammation, cholesterol, and misfolded proteins. Lancet. 2004;363(9415):1139–46. 60. Louveau A, Smirnov I, Keyes TJ, Eccles JD, Rouhani SJ, Peske JD, et al. Structural and functional features of central nervous system lymphatic vessels. Nature. 2015;523(7560):337–41. 61. Masseguin C, LePanse S, Corman B, Verbavatz JM, Gabrion J. Aging affects choroidal proteins involved in CSF production in Sprague–Dawley rats. Neurobiol Aging. 2005;26(6):917–27. 62. Tarasoff-Conway JM, Carare RO, Osorio RS, Glodzik L, Butler T, Fieremans E, et  al. Clearance systems in the brain—implications for Alzheimer disease. Nat Rev Neurol. 2016;12(4):248. 63. Vandenberghe R, Van Laere K, Ivanoiu A, Salmon E, Bastin C, Triau E, et al. 18f-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial. Ann Neurol. 2010;68(3):319–29. 64. Bangen KJ, Clark AL, Werhane M, Edmonds EC, Nation DA, Evangelista N, et  al. Cortical amyloid burden differences across empirically-derived mild cognitive impairment subtypes and interaction with APOE varepsilon4 genotype. J Alzheimer’s Dis. 2016;52(3):849–61.

TBI Sports Related Injury

60

Mohammad I. Kawas, Christopher A. Sheridan, William C. Flood, Adam P. Sweeney, and Christopher T. Whitlow

Introduction Sports-related traumatic brain injury (TBI) is a major cause of brain injury worldwide, with an estimated 1.6–3.8 million injuries occurring yearly in the United States alone [1, 2]. In contrast to non-sports-related mechanisms of TBI, a larger percentage of sports-related TBI—nearly 90% by some estimates—are classified as mild on the Glasgow Coma Scale (GCS of 13–15), as opposed to moderate (GCS 9–12) or severe (GCS 8–3) [1, 3]. Although disagreement remains regarding the appropriate terminology for use in research and clinical care, sports-related mild TBI (mTBI) will also be referred to as sports-related concussion (SRC) in accordance with colloquial usage and a substantial portion of relevant scientific literature. Despite increasing awareness among the general population over the last decade, sport-­ related concussion likely remains insufficiently recognized and underreported, confounding accurate estimates of incidence among those who participate in some form of impact-­ prone athletic activity [4–9]. Hospital-based studies place the incidence of SRC from 3.5 to 31.5 per 100,000, accounting for 1.2–30.3% of all TBIs [1, 10]. Estimates of SRC per athlete exposure range from 0.17 to 0.99 per 1000 depending on the sport, and may be a more relevant measure of risk [11–13]. In particular, male predominant sports including football, rugby, soccer, wrestling, and lacrosse carry the highest risk of injury per athlete-exposure; among female predominant sports, soccer, rugby, lacrosse and basketball carry the highest risk of injury per athlete-exposure [6, 11, 14–16]. SRC is likely the most common cause of TBI in adoM. I. Kawas · C. A. Sheridan · W. C. Flood Department of Neuroscience, Wake Forest School of Medicine, Winston-Salem, NC, USA e-mail: [email protected]; [email protected]; [email protected] A. P. Sweeney · C. T. Whitlow (*) Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA e-mail: [email protected]; [email protected]

lescents and young adults, accounting for half of emergency department visits for mTBI among those aged 14–18 [9, 17]. In the pediatric population, incidences as high as 304 per 100,000 have been reported, and SRC represents 8.9% of all injuries associated with high-school athletic participation in the United States [14, 17–19]. The most broadly used definition of SRC can be found in the consensus statement produced by the Fifth International Conference on Concussion in Sport: “Sport-related concussion is a traumatic brain injury induced by biomechanical forces. Several common features that may be utilized in clinically defining the nature of a concussive head injury include: • SRC may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an impulsive force transmitted to the head. • SRC typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, signs and symptoms evolve over a number of minutes to hours. • SRC may result in neuropathological changes, but the acute clinical signs and symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. • SRC results in a range of clinical signs and symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive features typically follows a sequential course. However, in some cases symptoms may be prolonged. The clinical signs and symptoms cannot be explained by drug, alcohol, or medication use, other injuries (such as cervical injuries, peripheral vestibular dysfunction, etc.) or other comorbidities (e.g., psychological factors or coexisting medical conditions).” [10] Notably, the Fourth International Conference on Concussion in Sport removed the term “mild” from the consensus definition of SRC, reflecting the fact that while con-

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Faro, F. B. Mohamed (eds.), Functional Neuroradiology, https://doi.org/10.1007/978-3-031-10909-6_60

1389

1390

sidered mild in comparison to moderate and severe TBIs, SRCs can result in significant short- and long-term sequelae in the physical, cognitive, affective, and sleep-related symptom domains [20]. Injuries that occur in sport competition are often evaluated on the sideline by sports medicine staff. The majority of SRCs, however, occur in sport practice or informal recreational activities. Despite increasing ­awareness among athletes, coaches, and support staff resulting from growing media coverage and targeted educational efforts, it remains likely that a significant portion of SRCs either go unreported or reported with unnecessary delay [7, 21]. Acute evaluation of suspected SRC includes ruling out injury to the cervical spine and excluding intracranial hemorrhage, establishing the presence of a biomechanical mechanism of injury if not directly observed, and a comprehensive physical examination coupled with a standardized inquiry of symptoms. Headache, dizziness, and confusion are the most common presenting symptoms, but individual patients report a broader range of symptoms due in part to the diversity of traumatic mechanisms sufficient to cause injury [6, 17, 22, 23]. With respect to injury characteristics, more severe initial symptoms including amnesia, loss of consciousness, and significant balance deficits coupled with relatively high force mechanisms (i.e., a fall from climbing or high-speed cycling collision) are associated with a more prolonged recovery [6, 24, 25]. While adolescent athletes may take longer to symptomatically recover, high-school, collegiate, and professional athletes more frequently report a return to their asymptomatic baseline within 7–10 days after injury, and often return to sport-related activity without restriction within 2  weeks [6, 13, 26]. Fifteen to 30% of those individuals who suffer a concussion experience prolonged symptoms lasting beyond 3 months postinjury [27–29]. Given that the preponderance of medical care is directed toward patients with enduring symptoms, there is considerable research effort focused upon the early identification of and targeted intervention for those patients at highest risk of prolonged recovery [30]. The primary role of imaging in the acute evaluation of SRC is to exclude more severe primary (i.e., intracranial hemorrhage, skull fracture, mass effect, vascular injury) or secondary (i.e., edema) neurological injury that might require surgical intervention. When a patient initially presents to an emergency department or other acute care facility, noncontrast computed tomography (CT) of the head and cervical spine is the optimal first-line method to detect acute findings such as intracranial hemorrhage, skull fracture, or cervical spine injury. It is important to emphasize that the role of imaging in this acute setting is to rule out injuries requiring acute intervention and not to diagnose a concussion. By definition, SRC is not associated with any intracranial findings on computed tomography. In cases where the mechanism of injury, reported symptoms, or physical exam raises clinical suspicion of a more serious injury, evidence-­

M. I. Kawas et al.

based management guidelines for both adult and pediatric patients are paramount in determining if the use of ionizing radiation-based imaging is prudent. If there is clinical evidence of a substantial neurological injury despite a normal head CT, MRI is becoming increasingly recognized as helpful at identifying pathology invisible to computed tomography. In addition to standard T1-weighted and T2-weighted imaging, particularly useful sequences include diffusion-­ weighted imaging (DWI) and susceptibility-weighted imaging (SWI) to identify signs of axonal shear injury (diffuse axonal injury) and/or associated parenchymal micro-­ hemorrhages. T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) sequences are also often useful for detecting small volume subarachnoid hemorrhage that may be occult on CT imaging. For these reasons, SRC currently remains a largely clinical diagnosis with no definitive rapid first-line imaging or serological test that adds significant diagnostic value to a thorough physical examination and detailed history. However, acute diagnosis of a concussion on a clinical basis with a reasonable degree of certainty is less challenging than determining when an athlete is ready to return to sport-­ related activity. The current standard-of-care return-to-­ activity protocols for athletes are assessment of symptoms relative to preinjury baseline via graded checklists such as the SCAT-5 Graded Symptom Checklist, the PostConcussion Symptom Inventory, or the Rivermead Post Concussion Symptom Questionnaire. While these checklists represent symptoms from the physical, cognitive, affective, and sleep domains, symptoms of SRC are often notoriously nonspecific. Moreover, there is a growing body of evidence that demonstrable changes including white matter integrity on anatomic MRI sequences, EEG activity, functional MRI techniques such as BOLD fMRI (blood oxygen level dependent functional MRI), magnetoencephalography, cerebral perfusion analysis, and heart-rate variability persist beyond the point when athletes report a return to their preinjury baseline on symptom checklists. Given the short-term risks and uncertain long-term consequences of unrestricted return to activity, premature or otherwise, a dire need for neuroimaging-­based evaluation of SRC exists to buttress symptom reporting, provide criteria of a high probability of physiological recovery, or elucidate the degree of persistent physiological and/or microstructural abnormalities that should reasonably preclude ongoing sports activity. This chapter will present a detailed discussion of the current and proposed future role of advanced MRI-based methods for investigating the pathophysiology of SRC, and monitoring the clinical course of patients with prolonged symptoms. The discussion will focus on blood-oxygenation-­ level-dependent functional MRI (BOLD fMRI) and arterial spin labeling (ASL) techniques.

60  TBI Sports Related Injury

Blood-Oxygen-Level-Dependent (BOLD) Functional MRI Functional MRI (hereafter abbreviated fMRI) is a noninvasive technique for indirectly measuring neuronal activity. One such method is BOLD functional MRI which estimates neuronal activation by quantifying arterial blood flow recruitment secondary to increased neuronal metabolic demand [31]. This is possible because increased neuronal activity is closely followed by a transient reactionary overshoot of arterial blood supply that exceeds the cellular demand. This increased oxygen supply also overcompensates for the maximum possible neuronal oxygen extraction. The resultant relative decreased ratio of paramagnetic deoxyhemoglobin to diamagnetic oxyhemoglobin transiently alters the T2*weighted MR signal from neuronal resting baseline [32]. Therefore, BOLD fMRI can extrapolate changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) secondary to neuronal activity and provide visually interpretable changes [33, 34]. The fMRI data is collected volumetrically with the addition of temporal resolution (4D), usually in 1–3 s increments. The changes in signal intensity in relation to time can be used to measure activation patterns in task-based fMRI, but can also be used to assess the synchronicity of spontaneous neuronal activity or “functional connectivity” in resting-state fMRI. As previously discussed, standard neuroimaging in the setting of SRC is abundantly unrevealing [35, 36], with less than 1% of exams reported to have a positive finding [37, 38]. In contrast, fMRI has the unique capability to detect changes in brain function and is being increasingly utilized to investigate the pathophysiology of concussion with the hope of ultimately developing reliable biomarkers of significant injury and guiding recovery. In the following section, a review of relevant research using resting-state and task-based fMRI techniques at various postinjury timeframes (acute, subacute, and chronic) will be explored.

Review of Resting-State fMRI Literature For the scientific investigation of resting-state fMRI (RS-fMRI), most studies require the participant to simply fixate on a visual stimulus or close their eyes for 5–10 min. In this context, static connectivity, dynamic connectivity, and regional homogeneity are features of intrinsic brain activity that are frequently explored with resting-state fMRI [39]. Connectivity between brain regions refers to the correlation of their BOLD signals in the low frequency region (0.01– 0.1 Hz) that is believed to reflect the innate fluctuations of neural activity in the absence of active stimuli [40, 41].

1391

Numerous RS-fMRI studies have previously focused on the default-mode network (DMN), which is one of the most frequently studied brain networks, and affects multiple complex functions including episodic memory recall, prospection, and social cognition [42, 43]. The DMN is most active in the absence of deliberate mental activity, commonly referred to as during “rest,” and involves the medial prefrontal, lateral and medial parietal, and lateral and medial temporal cortices [44]. Applying this knowledge to the acute and subacute stages of SRC, studies have reported inconsistent changes in DMN connectivity. One such study reported an increase in connectivity [45], and another discovered differences from control subjects after a physical stress test, notably with lower connectivity in the concussed group [46]. In one longitudinal study, connectivity between the DMN and brain regions associated with attentiveness was lower than control subjects 1  day after the injury, however was paradoxically higher than controls and associated with an improvement in depressive symptoms at 1  month after the injury [47]. This particular study and multiple others [39, 48, 49] highlight the importance of investigating network connectivity changes, especially those of the DMN, with the psychiatric changes associated with SRC. Several RS-fMRI publications have reported alterations in neuronal networks other than the DMN, including those involved in executive function, memory, and attentiveness. Importantly, these alterations were observed during the acute [50], subacute [51, 52], and chronic stages [53–55] (see Table 60.1). More recent studies have evaluated dynamic functional connectivity, which measures time-varying network connectivity [56, 57]. Compared to static connectivity analyses, dynamic analyses provide additional information about the temporal interactions of brain networks. Only two SRC studies have thus far evaluated dynamic connectivity. One found significant longitudinal differences in scale-free dynamics in highly symptomatic concussed athletes when compared to controls, including lower levels during the acute stage and higher levels once clearance was granted to return to activity. Notably, the higher levels returned to preinjury baseline 1 year postconcussion [58]. However, the other study compared players with a history of multiple SRCs to healthy controls and found significant differences in dwell times for numerous connectivity states [59]. As for regional homogeneity (ReHo), a measure of local connectivity, a large longitudinal study of collegiate athletes found that ReHo differences relative to control athletes were first detected at the 1-month postconcussion assessment [60]. Conversely, another study found that portions of the superior and middle frontal gyri had greater ReHo at 24-h postconcussion relative to noninjured controls, and returned to normal by the first asymptomatic visit [39].

M. I. Kawas et al.

1392 Table 60.1  Differences in resting-state network connectivity after SRC Authors Subject Methods Results Slobounov Connectivity rsFMRI in 17 The mTBI group et al. (2011) changes of the asymptomatic revealed lower [51] subacute stage concussed inter-hemispheric in response to universityconnectivity of the a physical level athletes primary visual stress test and 17 cortex, matched hippocampal and controls dorsolateral prefrontal cortex at rest and after the physical stress test Borich et al. Network rsFMRI in 12 Altered functional (2015) [52] connectivity concussed connectivity within differences in athletes and the DMN and adolescent 10 matched concussed athletes athletes with controls also showed subacute increased concussion connectivity in the right frontal pole and the left frontal operculum Czerniak Detection of rsFMRI in 9 Concussed players et al. (2015) residual concussed had increased [53] connectivity universityconnections alterations level athletes between regions months after and 12 that underlie SRC matched executive function controls Churchill Identification rsFMRI in 22 No significant et al. (2017) of brain universitydifferences were [55] regions with level athletes found between the functional with prior two groups. But the connectivity concussions number of prior changes and 21 athletes concussions associated without correlated with with chronic documented connectivity concussion injury changes in the visual attention network and the cerebellum Churchill, Functional rsFMRI in 70 Symptom severity Hutchison connectivity universitycorrelated with et al. (2018) correlates with level athletes; lower connectivity [50] symptom 35 with acute in frontal, severity within concussions temporal, and the first week and 35 insular regions and post injury controls with higher connectivity in the sensorimotor system Guell et al. Changes of rsFMRI in 32 Retired players (2020) [54] whole brain retired rugby revealed a cluster functional players with of voxels in the connectivity history of cerebellar lobule V patterns in multiple that had hyperretired rugby concussions connectivity and players and 36 healthy hypo-connectivity matched to several cortical controls regions when compared to the healthy controls

In contact sports, exposure to head impacts is frequent but does not always result in a clinical concussion. Notably, one study found that both players with and without a history of concussion had increased connectivity between areas in the hippocampi, frontal lobes, and cingulate gyri compared to noncontact sport control subjects [61]. Other studies among children and adolescents discovered that playing a single season of football was associated with changes in r­ esting-­state neural networks [62] and that the delta power spectrum of the DMN reliably differentiated players with high and low head impact exposure as measured by a helmet telemetry system [63].

Review of Task-Based fMRI Studies Task-based fMRI paradigms impose sensory, motor, and/or cognitive tasks and measures subsequent changes in BOLD fMRI signal to determine the degree and location of brain activation required to perform the task(s) [64]. Because SRC is associated with neurocognitive impairments including working memory, attentiveness, and executive functioning [65], task-based fMRI is a useful tool for detecting abnormal brain activation during task performance. The “N-back” task is commonly used to assess working memory (define the N-back task here), and two studies assessing functional activation 1-month postconcussion discovered correlative dependence on the cognitive strenuousness of the task. Relative to controls, analysis revealed hyperactivation within the lateral parietal and dorsolateral prefrontal cortices during intermediate task difficulty, and hypoactivation during low and high difficulty working memory tasks [66, 67]. Another study also reported hypoactivation of the dorsolateral prefrontal cortex during a working memory task in athletes with persistent postconcussion symptoms [68]. Hypoactivation during working memory tasks was also observed in the medial temporal lobes, anterior cingulate gyri, and occipital cortices of both symptomatic and recovered athletes [69]. However, a longitudinal study of collegiate athletes discovered persistent hyperactivation of the dorsolateral prefrontal cortex and inferior parietal lobes for 2 months following SRC, albeit with comparable task performance relative to control subjects [70]. Additional cognitive and motor tasks were also associated with hyperactivation. Specifically, recently concussed but asymptomatic athletes showed hyperactivation in the hippocampi as well as the dorsolateral prefrontal and parietal cortices relative to controls utilizing a virtual reality spatial memory task [71]. Moreover, relative hyperactivation of frontoparietal areas during memory and sensorimotor tasks was found in acutely concussed college football players

60  TBI Sports Related Injury

compared to nonconcussed players, despite comparable overall task performance [72]. Likewise, several brain regions showed increased activation during oculomotor tasks at various severities of SRC even in cases with normal cognitive performance [73–75]. Considered together, these studies suggest that task-based fMRI may be more sensitive than neuropsychological testing in detecting functional disruptions and subsequent compensatory neuronal processes after SRC [72]. The level of resultant hyperactivation may ­ultimately be useful in predicting recovery times of concussed athletes [76]. Several studies have discovered associations between differential activation patterns and self-reported symptom severity in athletes following SRC. Particularly, hyperactivation of frontal and parietal regions during working memory tasks was significantly associated with the intensity of symptoms in the first week following injury [77]. Conversely, two studies by Chen et al. found that persistent SRC symptoms were associated with hypoactivation of prefrontal regions during verbal and visual working memory tasks [68, 78]. These studies strongly support the utility of fMRI as a sensitive and practical evaluation of sport-related concussion. There is a current paucity of published task-based fMRI studies investigating the effects of multiple SRCs. One study investigating former high school football players reported hypoactivation in left hemispheric language regions during a verbal memory task in athletes with a history of two or more SRCs [79]. However, two additional studies demonstrated that active student-athletes with a history of multiple SRCs did not show any activation differences [80, 81]. In order to carefully evaluate the effects of repetitive subconcussive head impacts, Talavage et  al. performed an investigation placing accelerometers in the helmets of high school football players to count and quantify the multiple head impacts endured throughout a sports season. Participants were scanned with MRI before and after the season, and the study demonstrated that players with no clinically declared concussions despite a high number of reported impacts to the head elicited demonstrable hypoactivation of the dorsolateral prefrontal cortex along with impaired performance on an N-bask task [82]. It is therefore imperative that further research is needed to elucidate the long-term effects of single and multiple concussive as well as subconcussive head impacts.

Limitations and Considerations The BOLD (blood-oxygen-level-dependent) fMRI signal is a convolution of the hemodynamic response function (HRF) and is an indirect measure of neuronal activity. Standard analytic approaches assume no baseline regional variability of HRF within the brain, but there is evidence of HRF variabil-

1393

ity that has been demonstrated to significantly affect connectivity measurements among healthy adults [83]. Moreover, several animal studies have suggested that mTBI is associated with microvascular and metabolic disruptions [84–86]. These disruptions involve CBF and neurovascular coupling which directly affect the HRF and potentially amplify the HRF variability within the brain. Additionally, the heterogeneity of SRC further challenges the standard assumption of a homogeneous whole-brain HRF and may prevent BOLD fMRI from detecting relative functional changes, especially in study-based evaluations that by design do not differentiate the specific injury mechanisms and subsequent symptomatology. This confounding aspect can be at least partially ameliorated by using a deconvoluted HRF to minimize the effects of baseline HRF variability [83], and employing multimodal imaging methods can account for the variability of nonneural components of the HRF within and across patients [87]. For example, arterial spin labeling (ASL) is an imaging technique that estimates blood flow, and can be used to calibrate the BOLD signal [69, 88, 89]. Hypercapnic normalization is another potential approach [90] because hypercapnia does not in isolation alter neural activity and neuronal oxygen extraction, but rather rapidly alters blood flow via cerebrovascular reactivity in anticipation of a perceived impending paucity of oxygen delivery [91, 92]. Moving forward, study designs would be well served by employing a multimodal imaging approach coupled with considerations of the injury profile and timing to improve the sensitivity of imaging biomarkers in the hope of more accurate recovery prognostication.

Arterial Spin Labeling (ASL) Arterial spin labeling (ASL) is an MRI perfusion technique that estimates CBF without the injection of an exogenous contrast agent [93–95]. ASL utilizes a technique of transient magnetic alignment and subsequent detection the “labeled” arterial blood water at sequentially ephemeral timeframes to quantify the blood flow in the brain. By creating a flow labeled image and a comparison control image of the same static tissues, researchers can quantify blood-flow recruitment due to its altered magnetic properties [94, 95]. ALS has two primary subcategories: continuous arterial spin labeling (CASL) and pulsed arterial spin labeling (PASL). However, CASL has largely been supplanted by PASL for logistical reasons. And even more recently, pseudo-­ continuous ASL (pCASL), a hybrid of these two techniques, has become the most widely utilized technique and is reliably accepted as the primary method to demonstrate changes in cerebral perfusion. For this technique, arterial blood water is first labeled just below the region of interest by applying a

1394

180° radiofrequency inversion pulse [94]. Doing so will magnetically invert the protons in the column of blood as a means of “labeling.” After the transit time, the labeled blood water will flow to a slice of interest intracranially where it will exchange with the pre-existing tissue water. The inflowing aligned water molecule nuclear spins within the arterial blood will alter the overall brain tissue magnetization and ultimately alter the MR signal and intensity. This allows the signal change to be observed and quantified by subtracting the new image signal from the preinflux baseline [94, 95]. While ASL is often used in resting conditions, it has been frequently paired with a cerebrovascular reactivity (CVR) assessment as a means of examining autonomic flexibility. Researchers will often alter plasma carbon dioxide and/or oxygen concentrations using a variety of methods to quantify the neurovascular response using ASL technique. At least in part due to the unpredictable onset and symptomatology of SRC, many studies utilizing ASL demonstrate variable results. Despite this, some commonalities have emerged. Numerous investigators have successfully demonstrated alterations in cerebrovascular activity in conjunction with participation in a contact sport. Unsurprisingly, result variations also differ due to the time frame in which the examination occurred relative to the incidence of the concussion. While the specific changes of cerebrovascular dynamics differ between studies, significant changes and correlations have been observed, regardless of whether comparison was made to noninjured control via functional imaging and/or cognitive assessments.

 SL Utilized in Patients During the Acute A and Subacute Timeframes (