Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine [3 ed.] 9783030621209, 3030621200

Table of contents :
Preface to the 3rd edition
Acknowledgements
Contents
Contributors
Part I: Principles of DW Imaging
1: Basics of Diffusion Measurements by MRI
1.1 Diffusion: A Probabilistic Process
1.2 Diffusion Imaging in MR
1.3 Diffusion Imaging of the Brain
1.4 Principles of Magnetic Resonance Diffusion Imaging
1.5 Apparent Diffusion Coefficient
1.6 Diffusion Represents a Molecular Event
1.7 Requirements in Clinical Diffusion Imaging
1.8 Setting the b Value in Clinical DW Imaging
1.9 Future Trends in Clinical Diffusion Imaging
References
2: Diffusion-Weighted and Tensor Imaging of the Normal Brain
2.1 Introduction
2.2 Adult Brain
2.2.1 Low Signal in Basal Ganglia
2.2.2 Diffusion-Weighted Imaging of Gray and White Matter
2.2.3 Choroid Plexus
2.3 Pediatric Brain
2.3.1 Diffusion-Weighted Imaging and ADC of the Pediatric Brain
2.4 Diffusion Tensor Imaging and White Matter Anatomy
2.4.1 Association Fibers
2.4.2 Projection Fibers
2.4.3 Commissural Fibers
2.4.4 Fibers of the Brain Stem and Cerebellum
2.4.5 Clinical Importance of White Matter Fiber Anatomy
2.5 Conclusion
References
3: Pitfalls and Artifacts of Diffusion-Weighted Imaging
3.1 Introduction
3.2 Influence of ADC and T2 on the DW Appearance
3.2.1 Concepts
3.2.2 Apparent Diffusion Coefficient Maps
3.2.3 Exponential Images
3.3 Clinical Conditions
3.3.1 T2 Shine-Through
3.3.2 T2 Washout
3.3.3 T2 Blackout
3.4 Artifacts
3.4.1 Eddy Current Artifacts
3.4.2 Susceptibility Artifacts
3.4.3 N/2 Ghosting Artifact (Nyquist Ghost)
3.4.4 Chemical Shift
3.4.5 Motion Artifacts
3.5 Conclusion
References
4: Diffusion Tensor and Kurtosis
4.1 Preface
4.1.1 Theoretical Considerations for Diffusion Tensor and Diffusion Kurtosis Imaging
4.1.1.1 Diffusion-Weighted Imaging
4.1.1.2 Diffusion Tensor Imaging Based on a Gaussian Distribution
4.1.1.3 Non-Gaussian Diffusion and Diffusion Kurtosis Imaging
4.1.2 Studies Using Diffusion Kurtosis Imaging for Central Nervous System Diseases
4.1.2.1 Cerebrovascular Diseases
4.1.2.2 Brain Tumors
4.1.2.3 Demyelinating Diseases
4.1.2.4 Neurodegenerative Diseases
4.1.2.5 Development and Developmental Abnormalities
4.1.3 Glymphatic System and Diffusion Imaging
4.2 Conclusion
References
5: Intravoxel Incoherent Motion Imaging
5.1 Intravoxel Incoherent Motion Concept
5.2 IVIM Imaging
5.3 Methods of IVIM Analysis
5.4 Neuroimaging Applications of IVIM Imaging
5.4.1 Physiological Applications
5.4.2 IVIM Imaging of Brain Tumors
5.4.3 IVIM Imaging of Gliomas
5.4.4 Other Brain Tumors
5.4.5 Stroke and Brain Ischemia
5.4.6 Head and Neck Lesions
5.5 Pitfalls and Technical Limitations
References
6: Functional MRI and Diffusion Tensor Imaging
6.1 Introduction
6.2 Functional MRI
6.3 HRF and BOLD Imaging
6.4 Regional Organization of Brain Systems
6.4.1 Primary Sensorimotor Cortex
6.4.2 Primary Language Areas
6.4.3 Primary Visual and Association Cortex
6.4.4 Supplementary Motor Areas
6.5 Paradigms and Design
6.6 Functional Mapping in Brain Tumor Surgery
6.7 Pearls and Pitfalls of Interpretation
6.8 Diffusion Tensor Imaging
6.9 White Matter Tracts
6.9.1 Projection Fibers
6.9.2 Association Fibers
6.9.3 Commissural Fibers
6.9.4 Fibers of Brainstem and Cerebellum
6.10 Diffusion Fiber Tracking
6.11 Presurgical Brain Mapping with DTI
References
7: Physics of Advanced Diffusion Imaging
7.1 Pitfalls of the Diffusion Tensor Model
7.2 Diffusion Signal of Crossing Fibers
7.3 Modifications to the Single Exponential Decay Assumption
7.4 Biophysical Compartment Models
7.5 Q-Space Imaging Approaches
7.6 Summary of Advanced Diffusion Protocols
7.7 Future Development of Advanced Diffusion Studies
References
Part II: Clinical Applications of DW Imaging
8: Brain Edema
8.1 Characterization and Classification of Brain Edema
8.2 Cytotoxic or Cellular Edema
8.3 Pathophysiology of Cytotoxic Edema
8.3.1 Energy Failure
8.3.2 Excitotoxic Brain Injury
8.3.3 Cytokinopathy/Cytokine Storm and Excitotoxic Mechanisms (Fig. 8.14)
8.4 Diffusion-Weighted Imaging and Cytotoxic Edema
8.4.1 Conditions that Cause Cytotoxic Edema, and Reversibility
8.4.1.1 Cytotoxic Edema of Neurons and Glial Cells
8.4.1.2 Cytotoxic Edema and Cortical Spreading Depolarization/Depression
8.4.1.3 Axonal Swelling
8.4.1.4 Intramyelinic Edema
8.5 Vasogenic or Interstitial Edema
8.5.1 Conditions that Cause Vasogenic Edema
8.6 Diffusion Tensor Imaging and Edema
8.7 Conclusion
8.7.1 Cytotoxic or Cellular Edema
8.7.2 Vasogenic Edema
8.8 Brain Edema—Treatment
8.8.1 Introduction
8.8.2 Types of Cerebral Edema and Diagnosis
8.8.3 Mechanisms of Cerebral Edema by Pathology
8.8.3.1 Tumor-Associated Edema
8.8.3.2 Stroke-Associated Edema
8.8.3.3 Intracranial Hemorrhage-Associated Edema
8.8.3.4 Traumatic Brain Injury-Related Edema
8.8.4 Diagnosis and Clinical Monitoring of Cerebral Edema
8.8.5 Medical Management of Cerebral Edema
8.8.5.1 General Measures
8.8.5.2 Tumor-Related Edema
8.8.5.3 Stroke-Related Edema
8.8.5.4 Traumatic Brain Injury-Related Edema
8.8.6 Surgical Management of Cerebral Edema
8.8.6.1 Tumor-Related Edema
8.8.6.2 Stroke-Related Edema
8.8.6.3 Traumatic Brain Injury-Associated Edema
8.8.7 Future of Edema Management
8.8.8 Summary of Cerebral Edema Treatment
References
9: Infarction
9.1 Introduction
9.1.1 Diffusion-Weighted Imaging (DWI)
9.2 Diffusion-Weighted Imaging and Pathophysiology of Cerebral Infarction
9.3 Apparent Diffusion Coefficient (ADC)
9.3.1 Explanation for Restricted Diffusion
9.4 Time Course of Infarction
9.4.1 Hyperacute (

Citation preview

Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine Toshio Moritani Aristides A. Capizzano Editors Third Edition

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Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine

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Toshio Moritani  •  Aristides A. Capizzano

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Editors

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Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine Third Edition

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Editors Toshio Moritani Department of Radiology University of Michigan Director of Clinical Neuroradiology Research, Division of Neuroradiology Ann Arbor, MI USA

Aristides A. Capizzano Department of Radiology Division of Neuroradiology University of Michigan Ann Arbor, MI USA

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ISBN 978-3-030-62119-3    ISBN 978-3-030-62120-9 (eBook) https://doi.org/10.1007/978-3-030-62120-9

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© Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved 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.

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This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To Lila, my mom, for being always there, to my wife, Nicoll, for her relentless patience and to Angela and Emma, source of my inspiration. Aristides A. Capizzano I dedicate this 3rd edition to my father, Hideo Moritani, and my mother, Tomoko Moritani, who recently passed away from long fight against their illness. I would like to thank my wife, Yumiko Moritani, for her strong support and editorial assistance with devotion. Toshio Moritani

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Preface to the 3rd edition

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The routine application of diffusion-weighted imaging (DWI) in Neuroradiology has been expanded not only to the brain but also to head and neck, spine, and spinal cord. Accordingly, in addition to the cases presented in the 2nd edition of “Diffusion-Weighted MR Imaging of the Brain”, we have included head and neck and spine DWI cases in this 3rd edition; therefore, the title has been changed to “Diffusion-Weighted MR Imaging of the Brain, Head and Neck, and Spine”. Remarkable progress has continuously been made in the field of diffusion imaging in the last 10 years, as exemplified by diffusion tensor imaging (DTI), fiber tractography, high b-value DWI, intravoxel incoherent motion (IVIM), diffusion kurtosis, Q-space imaging, and oscillating-gradient spin-­ echo (OGSE) diffusion. The emphasis of this 3rd edition, however, has remained in the clinical aspect of image interpretation, as has been the tradition in the two previous editions of this work. This is supported by the wealth of clinical cases presented, the vast majority from the authors’ several year clinical experience at the Universities of Iowa and Michigan. More recently, new technology and knowledge of genetics, epigenetics, and molecular biology of various neurologic and oncologic diseases have been rapidly progressing, even with faster pace than the progress of new imaging technologies. Numerous studies have been published especially in the last several years. The advancements of both molecular biology and cutting-­edge imaging have greatly impacted on the diagnosis, management, and treatment of many diseases. Without the same up-to-date background knowledge, it is sometimes hard for radiologists and clinicians to discuss diagnosis, management, and treatment. In this 3rd edition, we asked radiation oncologists, neurologists, clinicians, and neurosurgeons to contribute the essential most recent management and treatment for each chapter. This 3rd edition consists of four parts. Part 1 (Chaps. 1, 2, 3, 4, 5, 6, and 7) was written by world experts in their respective fields, encompassing the following topics: basic and advanced physics of diffusion imaging (Chaps. 1 and 7), DWI and DTI anatomy (Chap. 2), pitfalls and artifacts (Chap. 3), diffusion kurtosis, Q-space imaging, glymphatic system and DTI (Chap. 4), intravoxel incoherent motion (IVIM) (Chap. 5), and functional MRI (Chap. 6). Part 2 (Chaps. 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, and 23) was divided into imaging and treatment sections in each chapter. The authors of the treatment sections summarized the clinically important topics for interpreting the imaging findings. Each chapter presents correlations vii

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between many DW images and pathological tissues. Chapter 8 covered DWI of brain edema with relatively new underlying pathologic and pathophysiologic concepts such as intramyelinic edema, axonal swelling (beading), excitotoxic brain injury, cytokine cascade, and cortical spreading depression by using various illustrative cases. Chapter 9 (infarction) and Chap. 10 (hemorrhage) discussed the time course of signal abnormalities of infarction and hemorrhage with the background pathologies. Chapter 11 (vasculitis/vasculopathy) was largely rewritten to update the latest advances in vascular diseases, including metabolic and inflammatory conditions, embolic diseases, coagulative and functional disorders. Chapter 12 (epilepsy) was substantially updated to include the last ILAE classifications of seizures and the epilepsies and expanded to review the imaging features of epileptogenic lesions including recently described entities such as amygdala enlargement and immune-mediated encephalitis. Chapter 13 (demyelinating disease) was updated to discuss neuromyelitis optica (NMO) spectrum disorders, anti-myelin oligodendrocyte glycoprotein (MOG) antibody-­related demyelination and chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids (CLIPPERS). Chapter 14 (degenerative disease) was wholly rewritten to include discussion of the most prevalent neurodegenerative disorders such as Alzheimer’s, Lewy body, Parkinson diseases, and frontotemporal lobar degeneration (FTLD) as well as neuronal intranuclear inclusion disease (NIID) and hereditary diffuse leukoencephalopathy with axonal spheroids (HDLS). Chapter 15 (toxic and metabolic disease) was updated to include more recently described metabolic conditions such as stroke-like migraine attacks after radiation therapy (SMART) syndrome. We include new cases such as COVID-19 in Chap. 16 (infectious disease), traumatic optic neuropathy in Chap. 17 (trauma), various brain tumors based on the WHO 2016 classification in Chap. 18 (neoplasm), pediatric leukodystrophy and neoplasms in Chap. 19 (pediatrics), and various head and neck tumors and infections in Chap. 20 (head and neck). This 3rd edition includes three new chapters of spine DWI: Chap. 21 (spine infection), Chap. 22 (spine neoplasm), and Chap. 23 (spinal cord lesion) with many DWI cases and discussion of management. In Part 3 (Chaps. 24 and 25), experts of the field discuss the future of the DW imaging including oscillating-gradient spin-echo (OGSE) diffusion. Part 4 (Chap. 26) lists important figures and imaging examples from each chapter, which should help the readers understand pathology, pathophysiology, and DWI cases visually. We hope that this book, “Diffusion-Weighted Imaging of the Brain, Head and Neck and Spine”, will be useful for better understanding of the physics, anatomy, pathology, pathophysiology, and imaging characteristics of diseases in these anatomic fields, and a good companion to the Neuroradiologist in the reading room. Ann Arbor, MI Ann Arbor, MI  August 2020

Toshio Moritani Aristides A. Capizzano

Acknowledgements

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We are sincerely grateful to all the authors and contributors to this book (see the list in authors and contributors section). We wish to thank our Faculty colleagues, residents, fellows, MRI technologists, and other coworkers in the Departments of Radiology and allied Departments at the University of Michigan and the University of Iowa. Our deepest gratitude goes to Ms Danielle Dobbs and Ms Sarah Abate, Media Division, Department of Radiology at the University of Michigan, for the beautiful illustrations. We would like to especially thank Dr Ashok Srinivasan (Director, Neuroradiology, Department of Radiology, University of Michigan), Dr Thomas Chenevert (Professor, Department of Radiology, University of Michigan), Dr Vikas Glani (Chairman, Department of Radiology, University of Michigan), Dr N Reed Dunnick (Former Chairman, Department of Radiology, University of Michigan), Dr Colin P Derdeyn (Chairman, Department of Radiology, University of Iowa), Dr Joan E Maley (Director, Neuroradiology, Department of Radiology, University of Iowa), Dr Yutaka Sato (Director, Pediatric Radiology, Department of Radiology, University of Iowa), and Dr Yuji Numaguchi (Department of Radiology, St. Luke’s International Hospital, Tokyo, Japan), who gave us encouragement and support.

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Contents

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Part I Principles of DW Imaging

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1 Basics of Diffusion Measurements by MRI ����������������������������������   3 Arun Venkataraman and Jianhui Zhong

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2 Diffusion-Weighted and Tensor Imaging of the Normal Brain������������������������������������������������������������������������������������  11 Noriko Salamon and Toshio Moritani

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3 Pitfalls and Artifacts of Diffusion-­Weighted Imaging������������������  29 Toshio Moritani, Akio Hiwatashi, and Jianhui Zhong

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4 Diffusion Tensor and Kurtosis��������������������������������������������������������  43 Toshiaki Taoka

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5 Intravoxel Incoherent Motion Imaging������������������������������������������  67 Takashi Yoshiura

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6 Functional MRI and Diffusion Tensor Imaging����������������������������  77 Gaurang Shah

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7 Physics of Advanced Diffusion Imaging���������������������������������������� 103 Arun Venkataraman and Jianhui Zhong

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Part II Clinical Applications of DW Imaging

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8 Brain Edema������������������������������������������������������������������������������������ 113 Toshio Moritani

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9 Infarction������������������������������������������������������������������������������������������ 155 John Kim, Toshio Moritani, Sven Ekholm, and Yoshimitu Ohgiya

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10 Intracranial Hemorrhage���������������������������������������������������������������� 187 Toshio Moritani and Akio Hiwatashi

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11 Vasculopathy and Vasculitis������������������������������������������������������������ 217 Girish Bathla, Toshio Moritani, Patricia Kirby, and Aristides A. Capizzano

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12 Epilepsy�������������������������������������������������������������������������������������������� 275 Aristides A. Capizzano and Toshio Moritani

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13 Demyelinating Diseases ������������������������������������������������������������������ 313 Aristides A. Capizzano and Toshio Moritani

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14 Degenerative Diseases of the CNS�������������������������������������������������� 353 Aristides A. Capizzano and Toshio Moritani 15 Toxic and Metabolic Diseases���������������������������������������������������������� 391 Aristides A. Capizzano and Toshio Moritani 16 Infectious Diseases �������������������������������������������������������������������������� 429 Toshio Moritani, Yoshiaki Ota, and Patricia Kirby 17 Trauma���������������������������������������������������������������������������������������������� 487 Vikas Jain and Toshio Moritani 18 Brain Neoplasm�������������������������������������������������������������������������������� 521 Jayapalli Rajiv Bapuraj, Toshio Moritani, Shotaro Naganawa, and Akio Hiwatashi 19 Pediatrics������������������������������������������������������������������������������������������ 627 Lillian Lai and Toshio Moritani 20 Head and Neck �������������������������������������������������������������������������������� 715 Jerry Kovoor, Jack Kademian, and Toshio Moritani 21 Spine Infection���������������������������������������������������������������������������������� 777 John Kim, Eiyu Matsumoto, and Toshio Moritani 22 Primary and Metastatic Spine Tumors������������������������������������������ 803 Patrick W. Hitchon, Shotaro Naganawa, John Kim, Royce W. Woodroffe, Logan C. Helland, Mark C. Smith, and Toshio Moritani

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23 Spinal Cord Lesions������������������������������������������������������������������������ 839 John Kim, Duy Q. Bui, and Toshio Moritani

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Part III Futures of Diffusion Imaging

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24 Future Directions for Diffusion Imaging of the Brain and Spinal Cord�������������������������������������������������������������������������������������� 877 Takayuki Obata, Jeff Kershaw, Akifumi Hagiwara, and Shigeki Aoki

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25 Diffusion Imaging of the Head and Neck in the Future �������������� 891 Ashok Srinivasan

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Part IV How to use this book

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26 How to Use This Book���������������������������������������������������������������������� 903 Toshio Moritani and Per-Lennart Westesson

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Contents

Contributors

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Takashi Abe  Department of Radiology, Nagoya University, Nagoya, Japan

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Noriko  Aida Department of Radiology, Kanagawa Children’s Medical Center, Yokohama, Japan

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Aaron  Berg Department of Radiology, Sanford Health, Sioux Falls, SD, USA

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Felix  Boucher Department of Radiology, University of Michigan, Ann Arbor, MI, USA

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Adam  Bryant  Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

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Robert J. Casson  Ophthalmic Research Laboratories, Hanson Institute, and The University of Adelaide, Adelaide, SA, Australia

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Umar Shafique Chaudhary  Department of Radiology, University of Texas Medical Branch, Galveston, TX, USA

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Thomas Chenevert  Department of Radiology, University of Michigan, Ann Arbor, MI, USA

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Ramon  de Guzman Department of Imaging Sciences, University of Rochester, Rochester, NY, USA

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Joel Dennhardt  Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

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Nancy  A.  Dudek  Department of Radiology, University of Michigan, Ann Arbor, MI, USA

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Barbara  Germin Department of Pathology, University of Rochester, Rochester, NY, USA

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Justin  Guan Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

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Edip M. Gurol  Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

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Atsuhiko Handa  Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

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Myles Horton  Section of Neurology, Department of Medicine, University of Manitoba, Winnipeg, MB, Canada

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Masahiro  Ida Department of Radiology, Mito Medical Center, Ibaraki, Japan

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Karra A. Jones  Department of Pathology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA Jinsuh  Kim Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA

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Toshibumi  Kinoshita  Department of Radiology, Akita Cerebrospinal and Cardiovascular Center, Akita, Japan

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Takashi Kitanosono  Northfield International, INC., Honolulu, HI, USA

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Nobuo  Kobayashi Department of Radiology, St. Luke’s International Hospital, Tokyo, Japan

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Ryo  Kurokawa Department of Radiology, University of Tokyo, Tokyo, Japan Andrew Lee  Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA Ho Kyu Lee  Department of Radiology, Jeju National University Hospital, Jeju, Republic of Korea Michael Lee  Department of Radiology, University of Michigan, Ann Arbor, MI, USA Vincet A. Magnotta  Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA Joy  Matsui  Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA Mitsuru  Matsuki Department of Radiology, Kindai University, Osaka, Japan Harushi Mori  Department of Radiology, Jichi Medical University, Tochigi, Japan Minoru  Morikawa Department of Radiology, Nagasaki University, Nagasaki, Japan Kristine M. Mosier  Department of Radiology & Imaging Sciences, Indiana University, Indianapolis, IN, USA Hisao Nakamura  Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan Jared W. Nelson  Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

Contributors

Contributors

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Yuji  Numaguchi Department of Radiology, St. Luke’s International Hospital, Tokyo, Japan

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Hiroshi  Oba Department of Radiology, Teikyo University School of Medicine, Tokyo, Japan

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Masaki  Oka Department of Radiology, Kikuna Memorial Hospital, Yokohama, Kanagawa, Japan

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Bruno Policeni  Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

291

James  M.  Powers Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, Rochester, NY, USA

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Michael  Sacher Department of Radiology, Mount Sinai Medical Center, New York, NY, USA

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Sarabjit  K.  Saggu Ophthalmic Research Laboratories, Hanson Institute, and The University of Adelaide, Adelaide, SA, Australia

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Mio Sakai  Department of Radiology, Osaka International Cancer Institute, Osaka, Japan

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Tomoaki Sasaki  Faculty of Health Sciences, Okayama University Medical School, Okayama, Japan

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Takashi S. Sato  Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

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Yutaka  Sato Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

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R.  Nuri  Sener  Department of Radiology, Ege University Hospital, Izmir, Turkey

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Umber Shafique  Department of Radiology, Indiana University, Indianapolis, IN, USA

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Jay  L.  Starkey Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR, USA

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Sergei  Syrbu Department of Pathology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA

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Min  D.  Tang-Schomer Penn Center for Brain Injury and Repair and Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA

315

Keiko  Toyoda Department of Radiology, Jikei University School of Medicine, Tokyo, Japan

318

Ryutaro Ukisu  Department of Diagnostic Radiology, Kitasato University of School of Medicine, Tokyo, Japan

320

Hidetsuna  Utsunomiya Department of Radiology, Teikyo University School of Medicine, Tokyo, Japan

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Matthew White  Department of Radiology, University of Nebraska Medical Center, Omaha, NE, USA Kei  Yamada Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan Limin  Yang Department of Radiology, University of Iowa Hospitals & Clinics, Iowa City, IA, USA Yan D. Zhang  Department of Radiology, University of Nebraska Medical Center, Omaha, NE, USA

Contributors

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Part I

1

Principles of DW Imaging

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Basics of Diffusion Measurements by MRI

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Arun Venkataraman and Jianhui Zhong

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1.1

Diffusion: A Probabilistic Process

Diffusion in the context of diffusion-weighted imaging (DWI) refers to the probabilistic process of water movement over time. To better understand this process, we must first introduce the concept of a “random walk.” To understand this process, we can picture a one-dimensional system with discrete time points. At each time point, one can move either left or right along this line, with equal probability to do either. If we plot the course of multiple “walkers” over time, we get a probability distribution resembling a normal distribution centered at the origin (Fig.  1.1). This process is the basis for movement of water, termed “Brownian motion,” after the scientist who first described it [1, 2]. We can picture this movement by placing a drop of ink in a glass of water. The ink is initially concentrated where it initially hits the water, but over time it diffuses and becomes more diluted. If we discretized the time points (i.e., calculate how “diffuse” the ink is every second), we could get a measure of the A. Venkataraman · J. Zhong (*) Department of Physics and Astronomy, Center for Advanced Brain Imaging and Neurophysiology, University of Rochester, Rochester, NY, USA Department of Imaging Sciences, Center for Advanced Brain Imaging and Neurophysiology, University of Rochester, Rochester, NY, USA e-mail: [email protected]; [email protected]

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“diffusivity” of the ink in the water by how quickly it spreads. The quantification of this “diffusivity” of ink has interesting parallels in the realm of brain imaging. We can image that the diffusivity relies on the size of the container; the larger the container, the more distance can be traveled. Similarly, if we could inject “magic ink” into the brain tissue and follow its progress, we could infer details about the structure of brain tissue itself, as well as the changes occurring due to pathologic processes.

1.2

Diffusion Imaging in MR

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The aforementioned “magic ink” is created in diffusion imaging by the application of specific magnetic field gradients [3]. When a patient enters the bore (central cavity) of the scanner, the large, static magnetic field (measured in Tesla) induces magnetic spins of protons (small magnets inside hydrogen nuclei) to align with the direction of the magnetic field. In addition, magnetic field gradients add a smaller, nonstatic magnetic field for a certain duration to “tag” different locations in the tissue of interest. This equates to dropping “magic ink” in a location that can be chosen. The application of another gradient after some time informs us about how much the spins have diffused in this time. This equates to taking a snapshot of the “magic ink” at a later time and

© Springer Nature Switzerland AG 2021 T. Moritani, A. A. Capizzano (eds.), Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine, https://doi.org/10.1007/978-3-030-62120-9_1

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Fig. 1.1  Evolution of random walk over discretized time steps. (a) Shows a step size of one at each time point; the resulting trajectories (left panel) and histogram of position over time (right panel) are shown. (b) Same as (a), but with step size of five. Vertical bars on the left panel denote

when respective histogram is plotted. The distribution widens over time, and as a function of step size, reflecting processes that influence “diffusivity.” It is clear that a molecule represented by (b) would have a higher “diffusivity” that one represented by (a)

comparing to the initial snapshot to quantify how much the ink has diffused. However, the analogy of introducing “magic ink” to assess diffusivity ends here.

be identical in different directions, based on the local cellular neighborhood. As explained later in the chapter, the overall tendencies of diffusion can be measured through metrics such as diffusion rate, diffusion coefficient, or diffusivity based on the unit used, and the asymmetry of diffusion can be measured using diffusion anisotropy, with a variety of parameters defined [4–7].

1.3

Diffusion Imaging of the Brain

The brain is composed of a multitude of structures, each with unique physical properties. Different tissues may, therefore, restrict motion of water to different degrees. Since water particles are performing a random walk at baseline, they will run into constituents of cells in different cellular compartments and will therefore spread at different rates when labeled with the magnetic field gradients. In addition, this motion need not

1.4

Principles of Magnetic Resonance Diffusion Imaging

The MRI sequence of diffusion studies can be classified into two portions: (1) Diffusion labeling, and (2) Image acquisition. This means that we need to apply diffusion magnetic field

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g­ radients in addition to radiofrequency and gradient pulses used in conventional MRI imaging. A simplified version of the most commonly used pulse-­ gradient spin-echo (PGSE) pulse sequence for diffusion imaging is shown in Fig. 1.2. During the diffusion labeling portion, a pair of field gradients are applied to perform “diffusion-­encoding” (Fig.  1.2, shaded rectangles). Each gradient in this gradient pair lasts for a time, δ, with gradient magnitude, G (usually measured in mT/m). The gradient pairs are separated by time Δ. Note that the diffusion gradients can be applied in different spatial orientations, which is relevant to measurements of anisotropic diffusion as we will discuss later in the chapter. We can define the so-called diffusion-weighting factor, b, as

δ  b = γ 2 G 2δ 2  ∆ −  (1.1) 3   where γ is the gyromagnetic ratio of a proton (42.6 MHz/T). From Eq. (1.1), we can see that b, also called the b-factor, can be increased through increases in G, δ, Δ. A formal analysis

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(1.2) Here, ADC is the apparent diffusion coefficient and S0 is the signal intensity when there is no diffusion gradient used. The apparent diffusion coefficient is so-named because it is an average measure of more complicated processes in the tissues, as discussed in the next section. Equation (1.2) suggests that there is an exponential reduction in the measured signal, S, when diffusion-weighted gradients are applied; this can be understood with simple reasoning. The result of the diffusion gradients is a spatially varying magnetic field; as spins diffuse through this magnetic field, each spin is affected differently based on its location and trajectory. The net outcome is dephasing of the spins, when the spins are no longer aligned with the magnetic field. Since the signal is the summation of the small signals from individual spins, the dephasing caused by the gradient pulses leads to a drop in overall signal intensity. It is also evident that the longer the



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which can be achieved with a variety of techniques (three examples given). TE echo time, RF radiofrequency, SS slice selection, RO readout, PE phase encoding

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there is a reduction in measured signal resulting from any change in phase. Note that positive and negative phase changes equally attenuate signal. φ0 represents phase without any diffusion gradients applied

diffusion proceeds, the more dephasing takes place and the lower the signal (Fig. 1.3).

the signal when no DW gradients are used. Figure 1.2 suggests that this is actually the same as would be obtained from a simple spin-echo sequence. In most clinical scanners, a long TE time (tens of milliseconds) is needed to accommodate the diffusion pulses, leading to T2 weighting of the S0 image.

1.5

Apparent Diffusion Coefficient

From Eq. (1.2), we can see that when a fixed b-factor is used, tissues with a higher ADC produce a lower signal intensity. Since brain cerebrospinal fluid (CSF) contains water that can move around relatively freely, it has a much higher ADC value than that of other brain tissues (either gray or white matter), which have diffusion restriction due to cellular structures. Therefore, in a diffusion-weighted (DW) image, one typically sees dark CSF space (due to pronounced dephasing) and brighter signal in the brain tissue (less dephasing). Equation (1.2) also tells us that if we collect a series of DW images with different b values, we can calculate ADC value for each volumetric pixel (voxel) and obtain a parametric map, referred to as an ADC map. The calculated ADC map would have voxel intensities reflecting the strength of diffusion in the pixels. Regions of CSF will, therefore, have higher intensity than other brain tissues, opposite of what is seen in the DW images. There are reasons the ADC map is advantageous to the DW image. One is the so-called T2 shine-through effect, which will be discussed in Chap. 2. It should also be noted that in Eq. (1.2), S0 refers to

1.6

Diffusion Represents a Molecular Event

Despite resolution of MRI images on the order of a millimeter, the information provided by diffusion imaging reflects cellular or molecular events in much smaller scales. This is because the molecular diffusion process is highly influenced by these events. It can be shown that water spins diffuse about tens of micrometers during a typical MR imaging measurement time, which coincides with the dimension of typical cellular structures. If spins are relatively unrestricted by cellular structures during this time (as is the case in the CSF), the measured diffusion is “free” and “isotropic” (equal in all directions), and ADC is just the intrinsic molecular diffusion coefficient. On the other hand, when diffusing spins run into cellular constituents such as cell membranes, the value of ADC will be reduced when compared with the value in free space. Cellular diffusion events are represented schematically in Fig. 1.4.

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Fig. 1.4  Displacement profiles for different cell types. Green (left) panel depicts impermeable cell (upper) and corresponding mean displacement () graph (lower). The graph shows that water in an impermeable cell is hindered, seen by the plateau. Blue (right) panel depicts permeable and large cells, which have identical mean

displacement graphs (black line, lower panel), compared to impermeable cell graph (gray line, lower panel). Diffusion barriers inside a permeable cell or in a cell much larger than mean displacement during diffusion (blue panel) show minimal diffusion barrier effects

For patients with neurologic abnormalities, which change the water distribution in various cellular compartments, or change the ability for water to pass through cell membranes, measured ADC values will be altered [4–7]. Therefore, MR diffusion measurement offers a unique opportunity to obtain information about morphology otherwise inaccessible to conventional MR imaging methods. A wide range of pathological conditions can be explored with water diffusion measurements, as described in later chapters. The measured ADC may also vary depending on the duration of the diffusion process, the direction in which diffusion is measured, and other factors. For diffusion in an anisotropic environment (brain white matter consisting of axons with myelin layers wrapped around them, making diffusion along the bundle much easier than through the bundle), diffusion becomes more complicated, and a complete description of the process relies on what is called tensor analysis [8, 9]. Diffusion tensor imaging (DTI) is an MRI sequence that is available on clinical scanners with diffusion sequences. Instead of monitoring

the magnitude of water movement alone, DTI detects both the magnitude and directionality of water diffusion processes in three dimensions and allows the investigation of the microstructure of the CNS by measuring the diffusion properties of water protons in their microenvironment. It is expected that water molecules move less impeded along white matter bundles, than perpendicular to the bundles. The net effect is high directionality (high anisotropy) of movement in the white matter of the brain. In areas of high concentration of fluid (i.e., ventricles filled with CSF), there is little preferential directionality of movement (low anisotropy) and minimal hindrance to diffusion (high diffusivity); these scenarios are shown in Fig.  1.5. Here, we define fractional anisotropy (FA) and mean diffusivity (MD) in the context of DTI metrics used to measure microscopic motion of tissue water at each image voxel. In a DTI measurement, six or more DWI measurements are required; tensor eigenvalues (λ1, λ2, λ3) and eigenvectors are derived from these measurements [8]. Maps of MD and FA can then be generated from the eigenvalues with the following relations:

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8 Fig. 1.5  Illustration of diffusion and corresponding diffusion tensor and ellipsoid. Upper row illustrates the case with unrestricted diffusion, forming a spherical probability distribution. Lower row shows restricted diffusion, such as the case is in white matter. The diffusion ellipsoid is elongated along the direction of maximum diffusion. Note that the more elliptical the ellipsoid is, the higher the measured FA

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FA represents the anisotropy of the diffusion process, yielding values between 0 (perfectly isotropic diffusion) and 1 (completely anisotropic diffusion). MD represents overall strength of water mobility in different brain areas. A useful representation of DTI measurement results is given by a “color FA” map in which red, green, and blue colors are used to represent the orientation of the largest eigenvalue along left-right (LR), anterior-posterior (AP), and superior-­ inferior (SI) directions, respectively, while image intensity represents FA values in each voxel. By following the main direction of water movement (the largest tensor eigenvalue) and its connection in adjacent voxels in the three-­ dimensional space, fiber tractography in white matter can be formed. We will discuss more advanced applications, and potentials of going beyond the tensor model in the final chapter. The overall impact of DTI is to increase the diagnostic ability of diffusion imaging in degenerative neurological diseases where damage to axon bundles or demyelination can be directly detected and visualized.

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Requirements in Clinical Diffusion Imaging

In the clinical setting, certain requirements are imposed on diffusion studies. Imaging time is often limited to a few minutes for each scan (T1-­ W, T2-W, diffusion, and others). Multiple slices (15–20) are required to cover most of the brain. A good spatial resolution (~5 to 8  mm thick, 1 to 3 mm in plane) is required. A reasonably short TE ( > T1

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The signal intensity (SI) on DWI is influenced by T2, ADC, the b-factor, the spin density (SD), and the echo time (TE), and is calculated as follows: However,

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Subsequently, increased ADC causes decreased SI on DWI, and decreased ADC causes increased SI on DWI [3–5, 7, 10, 15, 16].

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tensity on exponential DWI means decreased ADC, and hypointensity means increased ADC.

SIeDWI = SI / SI b = 0 = e − bADC

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Fig. 3.1  T2 shine-through in a 35-year-old female patient with multiple sclerosis and weakness of the lower extremities. (a) T2-weighted image shows several hyperintense lesions, with the largest in the right frontal lobe (arrow). (b) On T1-weighted image the lesion was hypointense (arrow) and did not enhance with contrast (not shown). (c)

Clinical Conditions

3.3.1 T2 Shine-Through This is a well-known phenomenon that causes hyperintensity on DWI by means of T2 prolongation [3–5, 7, 8, 10, 11, 15, 16]. If ADC is decreased at the same time, this can result in an accentuation of the hyperintensity on DWI (Figs. 3.1, 3.2, and 3.3). c

On DWI the lesion is hyperintense (arrow). (d) ADC map also shows hyperintensity in the lesion (1.2 × 10−3 mm2/s; arrow). (e) Exponential image eliminates the T2 effect and shows the lesion to be hypointense (arrow). This confirms that the hyperintensity on DWI is due to a T2 shine-­ through effect

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Fig. 3.2  T2 shine-through in a 45-year-old female patient with seizures caused by an anaplastic astrocytoma. (a) T2-weighted image shows a hyperintense lesion in the left frontal lobe (arrow). (b) On T1-weighted image the lesion is hypointense with a peripheral hyperintense area (arrow). The lesion did not enhance with contrast (not

shown), (c) DWI shows hyperintensity (arrow). (d) ADC map also shows hyperintensity in the lesion (0.98– 1.35 × 10−3 mm2/s; arrow). (e) Exponential image eliminates the T2 effect and shows the lesion to be hypointense (arrow). This confirms that the hyperintensity on the DWI is due to a T2 shine-through effect

3.3.2 T2 Washout

isointensity on DWI, caused by a balance of hypointensity on T2-weighted images and decreased ADC.

This implies that isointensity on DWI is the result of a balance between hyperintensity on T2-weighted images and increased ADC [13, 14, 16]. This is often seen in vasogenic edema, where the combination of increased ADC and hyperintensity on T2-weighted images will result in isointensity on DWI (Fig. 3.4). To the best of our knowledge there have been no systematic reports on pathological conditions with

3.3.3 T2 Blackout This indicates hypointensity on DWI caused by hypointensity on T2-weighted images and is typically seen in some hematomas [9, 16]. Paramagnetic susceptibility artifacts may occur in this situation (Figs. 3.5 and 3.6).

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Fig. 3.3  T2 shine-through and restricted diffusion in a 56-year-old male patient with right-sided weakness due to acute infarction. MR imaging was obtained 24 h after the onset of symptoms. (a) FLAIR image shows a hyperintense lesion in the left middle cerebral artery territory. (b) On T1-weighted image the lesion is hypointense. (c) On T2-weighted image (b0) the lesion is hyperintense. (d)

DWI also shows hyperintensity in the lesion. (e) ADC map shows hypointensity in the lesion (0.27– 0.45 × 10−3 mm2/s). (f) On the exponential image, which eliminates the T2 effect, the lesion remains hyperintense. This confirms that the DW hyperintensity is due to both restricted diffusion and T2 prolongation

3.4

( 4) Chemical shift artifacts. (5) Motion artifacts.

Artifacts

Numerous artifacts can be generated during the acquisition of DWI. There are five main artifacts of single-shot DW echo-planar imaging: (1) Eddy current artifacts due to echo-planar imaging phase-encoding and readout gradients, and motion-probing gradient pulses for diffusion weighting. (2) Susceptibility artifacts. (3) N/2 ghosting artifacts.

We will discuss each artifact separately.

3.4.1 Eddy Current Artifacts Eddy currents are electrical currents induced in a conductor by a changing magnetic field. Eddy currents can occur in patients and in the MR scanner itself, including cables or wires,

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Fig. 3.4  T2 washout in a 45-year-old female patient with hypertension, seizures, and posterior reversible encephalopathy syndrome. (a) FLAIR image shows hyperintense lesions in the bilateral occipital lobe (arrows). (b) T2-weighted image (b0) also shows hyperintensity of the lesions (arrows). (c) DWI shows mild hyperintensity in the lesions. (d) ADC map shows hyperintensity of the

lesions (1.18–1.38 × 10−3 mm2/s; arrows). With the strong T2 prolongation one would expect more hyperintensity on the DWI, but the T2 shine-through effect is reduced by the hyperintensity on the ADC, resulting in a balance between increased diffusibility and hyperintensity on the T2-weighted image (T2 washout)

gradient coils, cryoshields, and radiofrequency shields [17]. Eddy currents are particularly severe when gradients are turned on and off quickly, as in echo-planar imaging pulse sequences. Gradient waveforms are distorted due to eddy currents, which results in image artifacts, including spatial blurring and misregistration. In single-shot DW echo-planar imaging, eddy currents are due to both echoplanar imaging gradients and motion-­probing

gradients, which lead to image distortions (Fig.  3.7). Correction of image distortion is essential to calculate ADC values and especially to quantify anisotropy with diffusion tensor imaging. Correction methods: (1) correction of distortion by using post-processing [18–21], (2) pre-­ emphasis or pre-compensation, purposely distorting the gradient driving currents [22, 23], (3) shielded gradients, redesigning the magnet to

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Fig. 3.5  T2 blackout in lung cancer metastasis in a 62-year-old male patient with adenocarcinoma of the lung. (a) T2-weighted image shows a hypointense mass (arrow) with surrounding edema in the left cerebellar hemisphere. (b) Gadolinium-enhanced T1-weighted image shows heterogeneous enhancement of the mass (arrow). (c) T2-weighted image (b0) also shows hypointensity in the lesion with surrounding hyperintense edema (arrow), (d) ADC map shows central hyperintensity (1.63–2.35  ×  10−3  mm2/s; arrowhead) and peripheral

hypointensity (1.13–1.38  ×  10−3  mm2/s; arrow) of the mass. There is also hyperintensity of the surrounding tissue, consistent with vasogenic edema. (e) DWI shows heterogeneous hypointensity of the mass (arrow) and isointensity of the surrounding edema. The DW hypointensity of the mass (arrow) is due to the increased diffusibility and the hypointensity on the T2-weighted image (T2 blackout). The isointensity in the surrounding edema is due to the balance between increased diffusibility and hyperintensity on the T2-weighted image (T2 washout)

incorporate shielding coils between the gradient coils and the main windings [24].

sinuses and mastoid (Fig.  3.8). Susceptibility artifacts are more severe along the phaseencoding direction and phase encoding should thus be along the anterior-posterior direction for axial DW images. Coronal and sagittal DW images are helpful in detecting lesions in certain locations, such as the hippocampus and the brain stem, and to identify susceptibility artifacts (Fig.  3.9). Increased matrix size leads to elongation of the readout time, which causes even larger image distortions.

3.4.2 Susceptibility Artifacts Single-shot echo-planar imaging is sensitive to susceptibility artifacts, especially frequency and phase errors due to paramagnetic susceptibility effects. These artifacts are seen near the skull base, especially near the air in paranasal

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Fig. 3.6  T2 blackout from susceptibility artifacts in acute hemorrhage (deoxyhemoglobin and intracellular met-­ hemoglobin) in a 74-year-old man with left-sided weakness. MR imaging was obtained 24  h after the onset of symptoms. (a) T2-weighted image shows hypointense lesions in the right frontoparietal lobes (arrows deoxyhemoglobin and intracellular met-hemoglobin) with areas of surrounding hyperintensity consistent with edema (arrowheads). (b) T1-weighted image shows the heterogeneous lesion with hypointensity (arrow deoxyhemoglobin) and hyperintensity (arrowheads intracellular met-­hemoglobin). Fig. 3.7 Misregistration due to eddy current artifact. a, b Misregistration artifact is noted in the occipital regions (arrows) on DWI (a) and the ADC map (b). Gradient waveforms are distorted due to eddy currents, which results in this misregistration

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Correction methods: (1) multishot echo-­ planar imaging (to reduce the readout time, to enable high-resolution scanning) [25, 26], (2) line scan [27, 28], (3) single-shot fast spin echo (SSFSE) [29, 30], (4) periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) [31, 32], (5) sensitive encoding (SENSE)/array spatial and sensitivity-­ encoding technique (ASSET), undersampling of k-space which enables effective band width and shortens readout time, providing thin section and high-resolution matrix [33].

3.4.3 N  /2 Ghosting Artifact (Nyquist Ghost) Fig. 3.8  Susceptibility artifact. Susceptibility artifacts are seen near the air content of the mastoids (arrows). This is generally prominent in echo-planar sequences

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Fig. 3.9  Susceptibility artifact in coronal and sagittal plane DWIs. Coronal DWI (a) and the ADC map (b) are used to evaluate the hippocampus, but susceptibility artifacts distort the image near the mastoids. Sagittal DWI (c)

and the ADC map (d) show a pontine infarct as hyperintense with decreased ADC (arrowhead). Susceptibility artifacts are caused by air in the ethmoid and sphenoid sinuses (arrows)

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Fig. 3.10  N/2 ghosting artifact. (a) DWI shows N/2 ghosting artifacts (arrows), which are always shifted by half of the field of view in the phase-encoding direction. (b) On the ADC map severe N/2 ghosting artifacts are also seen (arrows)

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tions (eddy currents, imperfect timing of even echo type imaging. Effective fat suppression and odd echo, imperfect gradients, and magnetic techniques, such as the chemical shift selective field inhomogeneity), which can be produced by (CHESS) method and the spectral selective radioon-off switching during readout gradients. The frequency excitation method are necessary. ghosts in this artifact are always shifted by half of Correction methods: appropriate fat suppresthe field of view in the phase-encoding direction sion techniques. (Fig.  3.10). This ghost can produce severe artifacts when ADC maps are calculated. Correction methods: (1) reduce eddy cur- 3.4.5 Motion Artifacts rents, (2) adjust gradients and magnetic field homogeneity, (3) high b-value, (4) fluid-­ The sources of motion artifacts include gross attenuated inverse recovery (FLAIR) DWI head motion, respiratory motions, cardiac-related (reduce cerebrospinal fluid signal) [33, 34]. pulsations, and patient bed vibration due to gradient pulses. Single-shot DW echo-planar imaging has relatively low sensitivity to patient motion, 3.4.4 Chemical Shift because each image is acquired in about 100– 300 ms and the total acquisition time is less than In echo-planar DWI, chemical shift artifacts due 40 s. If one of the x, y; z or b0 images is corrupted to the different resonance frequencies in water by motion artifacts during a scan, or if patient and fat are produced along the phase-encoding head motion occurs between scans, the isotopic direction, while they are along the frequency-­ DW images and the ADC maps will have these encoding direction in conventional spin-echo artifacts (Figs. 3.11 and 3.12). In those cases, unitype MR imaging. This artifact is more severe in directional and b0 images from the raw data of echo-planar imaging than in conventional spin-­ DWI can be free from the motion artifacts and

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Fig. 3.11  Motion artifacts due to head motion during the scan of a patient with status epilepticus. (a) It is difficult to evaluate the DW image because of severe motion artifacts. (b) In the raw data of the DWI, the x axis image is corrupted by head motion during the scan. (c) The y axis image is free from the artifacts. This image shows a

hyperintense lesion in the left hippocampus (arrow). (d) ADC map of y axis image also shows decreased ADC of the lesion (transferred to a workstation for image processing, using a home-made code, which is based on the numerical computation software)

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Fig. 3.12  Motion artifacts due to head motion between the scans. Chronic infarcts in the right basal ganglia. (a) DWI has motion artifacts due to head motion between the scans. This image is composed of overlapping b0, x, y, and

z axis images. (b) ADC map also shows severe motion artifacts. This map is composed of b0, x, y, and z axis images. (c) b0, (d) x axis, (e) y axis, (f) z axis. b0 and unidirectional images are all free from the artifacts

remain diagnostically useful. Long (tens of ms) gradient pulses to reach sufficient diffusion weighting often increase sensitivity to motion. Correction methods: (1) For a fixed b-factor, use high-gradient amplitude but reduce gradient pulse duration to minimize the sensitivity to motion, (2) post-processing to correct for phase error (navigator method) [35–37], (3) elimination of phaseencode step (line scan method, projection reconstruction), (4) minimize time for phase error accumulation (single-shot echo-planar imaging, hybrid method with multishot echo-­planar imaging), (5) SSFSE, (6) PROPELLER [38], (7) SENSE.

3.5

Conclusion

DWI is inherently T2 weighted, and the interpretation of signal intensity on DW images requires a correlation between b0 images, ADC maps, and exponential images to uncover the underlying pathophysiologic condition. It is also important to understand a variety of artifacts to avoid misinterpreting the DWIs. Understanding inherent artifacts and how to reduce the artifacts on DWI will improve the quality and accuracy of DW imaging.

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40 226

References

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1. Stejkal EO, Tanner J (1965) Spin diffusion measurements: spin echoes in the presence of a time-­dependent field gradient. J Chemical Phys 42:288–292 2. LeBihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161:401–407 3. Warach S, Gaa J, Siewert B, Wielopolski P, Edelman RR (1995) Acute human stroke studied by whole brain echo planar diffusion-weighted magnetic resonance imaging. Ann Neurol 37:231–241 4. Provenzale JM, Engelter ST, Petrella JR, Smith JS, MacFall JR (1999) Use of MR exponential diffusion-­ weighted images to eradicate T2 “shine-through” effect. AJR Am J Roentgenol 172:537–539 5. Burdette JH, Elster AD, Ricci PE (1999) Acute cerebral infarction: quantification of spin-density and T2 shine-through phenomena on diffusion-weighted MR images. Radiology 212:333–339 6. Coley SC, Porter DA, Calamante F, Chong WK, Connelly A (1999) Quantitative MR diffusion mapping and cyclosporine-induced neurotoxicity. AJNR Am J Neuroradiol 20:1507–1510 7. Schaefer PW, Grant PE, Gonzalez RG (2000) Diffusion-weighted MR imaging of the brain. Radiology 217:331–345 8. Field A (2001) Diffusion and perfusion imaging. In: Elster AD, Burdette JH (eds) Questions and answers in magnetic resonance imaging. Missouri, Mosby, St. Louis, pp 194–214 9. Maldjian JA, Listerud J, Moonis G, Siddiqi F (2001) Computing diffusion rates in T2-dark hematomas and areas of low T2 signal. AJNR Am J Neuroradiol 22:112–128 10. Engelter ST, Provenzale JM, Petrella JR, Alberts MJ, DeLong DM, MacFall JR (2001) Use of exponential diffusion imaging to determine the age of ischemic infarcts. J Neuroimaging 11:141–147 11. Chen S, Ikawa F, Kurisu K, Arita K, Takaba J, Kanou Y (2001) Quantitative MR evaluation of intracranial epidermoid tumors by fast fluid-attenuated inversion recovery imaging and echo-planar diffusion-weighted imaging. AJNR Am J Neuroradiol 22:1089–1096 12. Geijer B, Sundgren PC, Lindgren A, Brockstedt S, Stahlberg F, Holtas S (2001) The value of b required to avoid T2 shinethrough from old lacunar infarcts in diffusion-weighted imaging. Neuroradiology 43:511–517 13. Casey S (2001) “T2 washout”: an explanation for normal diffusion-weighted images despite abnormal apparent diffusion coefficient maps. AJNR Am J Neuroradiol 22:1450–1451 14. Provenzale JM, Petrella JR, Cruz LC Jr, Wong JC, Engelter S, Barboriak DP (2001) Quantitative assessment of diffusion abnormalities in posterior reversible

T. Moritani et al. encephalopathy syndrome. AJNR Am J Neuroradiol 22:1455–1461 15. Eastwood JD, Engelter ST, MacFall JF, Delong DM, Provenzale JM (2003) Quantitative assessment of the time course of infarct signal intensity on diffusion-­weighted images. AJNR Am J Neuroradiol 24:680–687 16. Hiwatashi A, Kinoshita T, Moritani T et  al (2003) Hypointensity on diffusion-weighted MRI related to T2 shortening and susceptibility effects. AJR Am J Roentgenol 181(6):1705–1709 17. Elster AD, Burdette JH (2001) Scanner hardware. In: Elster AD, Burdette JH (eds) Questions and answers in magnetic resonance imaging. Missouri, Mosby, St. Louis, pp 54–71 18. Haselgrove JC, Moore JR (1996) Correction for distortion of echo-planar images used to calculate the apparent diffusion coefficient. Magn Reson Med 36:960–964 19. Horsfield MA (1999) Mapping eddy current induced fields for the correction of diffusion-weighted echo planar images. Magn Reson Imaging 17:1335–1345 20. Jezzard P, Barnett AS, Pierpaoli C (1998) Characterization of and correction for eddy current artifacts in echo planar diffusion imaging. Magn Reson Med 39:801–812 21. Calamante F, Porter DA, Gadian DG, Connelly A (1999) Correction for eddy current induced Bo shifts in diffusion-weighted echo-planar imaging. Magn Reson Med 41:95–102 22. Schmithorst VJ, Dardzinski BJ (2002) Automatic gradient preemphasis adjustment: a 15-minute journey to improved diffusion-weighted echo-planar imaging. Magn Reson Med 47:208–212 23. Papadakis NG, Martin KM, Pickard JD, Hall LD, Carpenter TA, Huang CL (2000) Gradient preemphasis calibration in diffusion-weighted echo-planar imaging. Magn Reson Med 44:616–624 24. Chapman BL (1999) Shielded gradients. And the general solution to the near field problem of electromagnet design. MAGMA 9:146–151 25. Robson MD, Anderson AW, Gore JC (1997) Diffusion-weighted multiple shot echo planar imaging of humans without navigation. Magn Reson Med 38:82–88 26. Brockstedt S, Moore JR, Thomsen C, Holtas S, Stahlberg F (2000) High-resolution diffusion imaging using phase-corrected segmented echo-planar imaging. Magn Reson Imaging 18:649–657 27. Maier SE, Gudbjartsson H, Patz S et al (1998) Line scan diffusion imaging: characterization in healthy subjects and stroke patients. AJR Am J Roentgenol 171:85–93 28. Robertson RL, Maier SE, Mulkern RV, Vajapayam S, Robson CD, Barnes PD (2000) MR line-scan diffusion imaging of the spinal cord in children. AJNR Am J Neuroradiol 21:1344–1348 29. Lovblad KO, Jakob PM, Chen Q et al (1998) Turbo spinecho diffusion-weighted MR of ischemic stroke. Am J Neuroradiol 19:201–208

284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343

3  Pitfalls and Artifacts of Diffusion-Weighted Imaging 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362

30. Alsop DC (1997) Phase insensitive preparation of single-shot RARE: application to diffusion imaging in humans. Magn Reson Med 38:527–533 31. Forbes KP, Pipe JG, Karis JP, Farthing V, Heiserman JE (2003) Brain imaging in the unsedated pediatric patient: comparison of periodically rotated overlapping parallel lines with enhanced reconstruction and single-shot fast spin-echo sequences. AJNR Am J Neuroradiol 24:794–798 32. Bammer R, Keeling SL, Augustin M et  al (2001) Improved diffusion-weighted single-shot echo-planar imaging (EPI) in stroke using sensitivity encoding (SENSE). Magn Reson Med 46:548–554 33. Porter DA, Calamante F, Gadian DG, Connelly A (1999) The effect of residual Nyquist ghost in quantitative echoplanar diffusion imaging. Magn Reson Med 42:385–392 34. Bastin ME (2001) On the use of the FLAIR technique to improve the correction of eddy current induced

41 artefacts in MR diffusion tensor imaging. Magn Reson Imaging 19:937–950 35. Anderson AW, Gore JC (1994) Analysis and correction of motion artifacts in diffusion weighted imaging. Magn Reson Med 32:379–387 36. Ordidge RJ, Helpern JA, Zx Q, Knight RA, Nagesh V (1994) Correction of motional artifacts in diffusion-­ weighted MR images using navigator echoes. Magn Reson Imaging 12:455–460 37. Dietrich O, Heiland S, Benner T, Sartor K (2000) Reducing motion artefacts in diffusion-weighted MRI of the brain: efficacy of navigator echo correction and pulse triggering. Neuroradiology 42:85–91 38. Pipe JG (1999) Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med 42:963–969

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1

Diffusion Tensor and Kurtosis

2

Toshiaki Taoka

3AU1 4AU2 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

4.1

Preface

Diffusion of water is based on the principle that the water molecules show random motion. Water diffusion can provide unique information on the functional architecture of tissues, since during their random displacements water molecules probe tissue structure at a microscopic scale [1, 2]. Since the introduction of imaging methods to visualize the diffusion phenomenon of the water molecule within tissue, this method has become one of the most important imaging techniques in clinical practice. In diffusion study on magnetic resonance imaging using motion proving gradient, signal decrease which is caused by molecular diffusion in the tissue provides information about tissue structure and physiological status. In this chapter, theoretical basis of diffusion tensor method and non-Gaussian diffusion technique including diffusion kurtosis imaging is discussed, and clinical or basic researches mainly using diffusion kurtosis imaging are introduced. In addition, the recently introduced concept of “glymphatic system” and the trial to evaluate the system using diffusion tensor method are also introduced.

T. Taoka (*) Department of Radiology, Nagoya University, Nagoya, Aichi, Japan e-mail: [email protected], [email protected]

4

4.1.1 Theoretical Considerations for Diffusion Tensor and Diffusion Kurtosis Imaging

28 29 30 31

4.1.1.1 Diffusion-Weighted Imaging A method to observe in vivo diffusion phenomena using magnetic resonance techniques was first reported by Stajskal and Tanner in 1965 [3]. Subsequently, in 1986 LeBihan et al. applied diffusion techniques for the first time in MR imaging [1]. In this method, a pair of motion providing gradients (MPGs), which are gradient magnetic fields for detecting self-diffusion of water molecules, were applied with an inverted pulse of 180 degrees between them. The effects of the sequence cancel each other for stationary protons but cause additional dephasing in diffusing protons undergoing random motion which is proportional to the degree of movement. The commonly used index for the strength of the MPG is the b value, which is given by the formula b = γ2G2δ2(Δ − δ/3), where γ: gyromagnetic ratio, G: strength of the MPG, Δ: interval of the MPG, and δ: duration of the MPG.  The unit of the b value is s/mm2, and the reciprocal is thereby mm2/s, which is the diffusion coefficient. For diffusion-weighted imaging of the brain, a b value of 1000 s/mm2 is often used. On a diffusion-­ weighted image, tissues with high diffusion have low signal intensity and tissues with restricted diffusion have high signal intensity. In the method

© Springer Nature Switzerland AG 2021 T. Moritani, A. A. Capizzano (eds.), Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine, https://doi.org/10.1007/978-3-030-62120-9_4

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44 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105

proposed by Stajskal and Tanner, the diffusion coefficient can be calculated from the slope of a graph of the logarithmic value of the signal acquired at different b-values. The values obtained by this method are called the apparent diffusion coefficient (ADC) and are different from the true tissue diffusion coefficients because they are influenced by phenomena other than pure diffusion, such as perfusion when measured in vivo. Diffusion-weighted imaging plays a very important role in brain diagnoses particularly for acute infarctions and has become an essential tool for clinical practice. The most useful application of diffusion imaging in stroke has been supported by the finding that decreased diffusion can be used to detect brain ischemia at an early stage [4]. Although the underlying mechanisms for the decreased diffusion are not fully understood, cell swelling, changes in cell membrane permeability, decreased cytoplasmic streaming, and increased intracellular viscosity have been proposed [5–8]. Recently, a study of cerebral infarction using a very short diffusion time with oscillating gradient spin echo diffusion was reported. The findings indicated that a combination of neuronal beading and axonal swelling was the key structural changes leading to the reduced apparent diffusion coefficient after stroke [9]. This study indicates that alteration of the diffusion time is one method that may be applied to explore the microstructure of tissue; however there are alternative approaches that may also be used for this purpose.

4.1.1.2 Diffusion Tensor Imaging Based on a Gaussian Distribution In neuronal tissue, the diffusion of water molecules is not uniform in all directions. Due to cell membranes and other tissue structures, the speed of diffusion varies depending on the direction, resulting in a state called anisotropic diffusion. In white matter of the brain, the diffusion in a direction perpendicular to the nerve fibers is more limited than along the axial direction of the fibers. The diffusion coefficient differs depending on the direction of motion probing gradient. Mathematically, scalars and

T. Taoka

vectors are not sufficient to describe this phenomenon, and the application of a tensor is required. A tensor is a multidimensional array, where a rank 0 tensor is a scalar and a rank 1 tensor is a vector. When expressing diffusions in a three-dimensional space, a 3  ×  3 matrix is used, and the diffusion tensor image (DTI) is acquired for this purpose. The diagonal components λ1, λ2, and λ3 of the obtained diffusion tensor are called eigenvalues, and the eigenvectors of the eigenvalues coincide with the axes of the diffusion ellipsoid. In white matter, the largest value of λ1 is assumed to be the diffusion coefficient in the direction of fiber. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) value are often used as indices to evaluate the properties of this diffusion ellipsoid. For example, in tissue with dense fibers, the diffusivity in the direction perpendicular to the fiber is suppressed and thus the FA value is increased. In contrast, when the fiber structure is damaged as with degenerative diseases or other disorders, the diffusivity in the direction perpendicular to the fiber is increased and thus the FA value is reduced. So, by evaluating FA or ADC in the brain, the structural changes in tissue can be estimated [10]. Diffusion tensor tractography is a method to estimate white matter structural anatomy from diffusion tensor images [11–13]. In tractography, the direction of the principal axis of the eigenvector is assumed to coincide with the direction of the nerve fiber, and the direction of the principal axis for each voxel is traced to draw a virtual fiber. Although there are various algorithms, one type displays a virtual fiber path connecting a specified origin and endpoint, which is called the “deterministic model.” This model is sufficient for gross anatomical information such as the anatomy of the pyramidal tract. However, in situations where more detailed spatial resolution is desired, or where there are many nerve fibers crossing, such as in the corona radiata, the resultant tracks obtained by the deterministic model may not be good enough. In these instances, a probabilistic model can be applied to obtain a more accurate depiction of the fiber tracts. This algorithm creates multiple tracts by taking into

106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153

4  Diffusion Tensor and Kurtosis 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

account the probability of the vector orientation from each voxel in relation to the area of the predefined tract origin [14].

4.1.1.3 Non-Gaussian Diffusion and Diffusion Kurtosis Imaging A major assumption for conventional diffusion imaging is that “Diffusion has a Gaussian distribution.” Diffusion tensor imaging using a Gaussian distribution model has been successful in evaluating anisotropic water diffusion in white matter, in which the diffusion is restricted by macromolecules, membranes, and myelin. Diffusion tensor imaging is based on a mathematical ellipsoid model. Thus, the principal direction of diffusion and the magnitude of diffusion along this direction can be calculated. Diffusion tensor tractography is an innovative application of the diffusion tensor method, which can delineate the neuronal fiber pathways. However, neuronal tissue has a highly heterogeneous structure and a Gaussian distribution is only applicable to free diffusion in fluid that is sufficiently large and uniform. Complicated structures exist in neuronal tissue, and the Gaussian distribution model does not truly represent the existence of a large number of compartments and nerve tracts, particularly in the central nervous system (CNS). Therefore, instead of a simple Gaussian distribution model, the application of probability distribution for a more detailed depiction of water molecule diffusion is necessary. To acquire probability distributions, dedicated model-free methods such as diffusion spectrum imaging have been introduced, which has high demand on scanner hardware and a long acquisition time [15, 16]. Another method to acquire a probability distribution is q-space imaging (QSI). QSI is performed by acquiring a large number of diffusion encodings and can provide a probability density function (PDF) of water molecules. In QSI, the diffusion phenomenon is not assumed to have a normal distribution. The information from fast diffusion to slow diffusion can be collected at various q values, and diffusion in each voxel can be expressed in the form of the PDF.  The PDF in QSI is the distribution function of the distance traveled by the water

45

molecules and their probability. The q value represents the strength of the MPGs like a b value, and the definition is as follows. q  =  γ/2π Gδ (where γ: gyromagnetic ratio, G: strength of the MPG, and δ: duration of the MPG). The unit of q is m−1, that is, the spatial frequency, and the reciprocal is the distance of the water molecule movement. The shape of the PDF curvature is characterized by the mean displacement of the water molecule, which is calculated from the full width at half height and the probability for zero displacement, which is given by the height of the profile at zero displacement (Fig. 4.3). Although QSI is theoretically superior to conventional Gaussian distribution analysis, one of the limitations is a long acquisition time due to the large number of sampling process. To further evaluate non-Gaussianity, the concept of kurtosis also can be applied. Diffusion kurtosis is a method to analyze the non-Gaussian distribution of water molecules within tissues [17, 18]. Kurtosis is a descriptor of the “Peakedness” relative to a Gaussian distribution of the probability

201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223

4

distribution given by K =

1 n æ xi - x ö ÷ (where åç n i =1 è sn ø

x: observed value and Sn: standard deviation). The higher the kurtosis, that is, the larger the deviation from the Gaussian distribution, the more the diffusion is restricted by the structure of the tissue and the structure of the cell. Therefore, the diversity of the diffusion distribution in the tissue is increased. Positive kurtosis means that distribution is more sharply peaked than Gaussian. The higher the diffusion kurtosis, the more the water molecule diffusion deviates from a Gaussian distribution, indicative of a more restricted diffusion environment. In other words, higher kurtosis means the distribution has a distinct peak near the mean and heavy tails, which is closely associated with diffusional heterogeneity [17, 19, 20]. Diffusion kurtosis can be calculated within a clinically feasible imaging time using a combination of more than three different MPG strengths with cumulant expansion [17]. Jensen et al. developed the method of cumulant expansion to obtain diffusion kurtosis, which was approximated by including a quadratic function

224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246

T. Taoka

46 247 248 249 250 251 252 253 254 255 256 257 258 259 260

[20]. In this method, a relatively small number of relatively low b value MPGs can be used to evaluate the non-Gaussian distribution diffusion. Diffusion kurtosis is sensitive to slow diffusional movement in a restricted environment, which may be mainly determined by microstructure. A diffusion kurtosis image (DKI) is reported to provide better characterization of both normal and pathological tissue and is less susceptible to free fluid contamination compared to the mean diffusivity or fractional anisotropy parameters provided by DTI [21]. Typical parameters in DKI include radial kurtosis (Krad), which is the diffusion kurtosis perpendicular to the principal diffu-

sion tensor eigenvector, axial kurtosis (Kax), which is the diffusion kurtosis along the principal diffusion tensor eigenvector, and mean kurtosis (MK), which is a special mean of the diffusion kurtosis. These directional diffusion kurtosis parameters may provide detailed information regarding the tissue microstructure, thus improving the MR diffusion characterization of neural tissues. DKIs are reported to have a greater sensitivity to changes in myelination compared with conventional DTIs. In particular, both Krad and MK are reported to have stronger correlations with myelin content compared with diffusion tensor metrics [22] (Figs 4.1 and 4.2).

In

S(b) = –bD S(0)

Probability

Signal

Diffusion tensor imaging

Motion proving gradient

Displacement

In

S(b) 1 = –bD + b 2D 2(K ) 6 S(0)

Probability

Signal

Diffusion kurtosis imaging

Motion proving gradient

Displacement

Motion proving gradient AU3

Fig. 4.1  The theory of diffusion tensor imaging, diffusion kurtosis imaging, and multi-shell diffusion imaging. A diffusion tensor image can be obtained by a set of multi-­ axes motion probing gradients (MPGs) in addition to no MPG for covering the special distribution of water diffusion. Since diffusion tensor imaging is based on the assumption that water diffusion obeys a Gaussian distribution, only a single set of b-values (calculated by the strength and duration of motion probing gradient) is required. Because the shape of the distribution is fixed to Gaussian, the logarithm of the diffusion-weighted signal intensity as a function of the MPG is fit to a following S (b) = -bD. (S signal intensity, b b value, D difline: ln S (0) fusivity). Kurtosis is a dimensionless statistical metric for quantifying the non-Gaussianity of an arbitrary probability distribution. To acquire a diffusion kurtosis image, at

Fourier transform

Probability

Signal

q-Space Imaging

Displacement

least two sets of MPGs in addition to no MPG is needed, because the following approximation can estimate both the diffusivity and kurtosis when the b values of the MPGs S (b) 1 are sufficiently small: ln = -bD + b2 D 2 ( K ) . S (0) 6 (K kurtosis). With this approximation, one can estimate both D and K by fitting the diffusion-weighted signal intensity data with three or more MPGs while the logarithm of the signal intensity is fit to a parabola. Large kurtosis means a high peak and a fat tail in the probability distribution. Q-space imaging (QSI) is a method to measure the probability distribution of a detailed water molecule. The main principle in q-space analysis is that a Fourier transformation of the signal intensity with respect to the MPGs provides a probability density function (PDF). QSI is performed by the measurement of a large amount of diffusion encoding

261 262 263 264 265 266 267 268 269 270 271 272 273 274

4  Diffusion Tensor and Kurtosis

47

a

b

Fig. 4.2  Diffusion kurtosis images of the normal brain. (a) Radial kurtosis images, (b) axial kurtosis images, (c) mean kurtosis images. Radial kurtosis is the diffusion kurtosis perpendicular to the principal diffusion tensor eigenvector. On radial kurtosis images, white matter has a significantly higher value compared to gray matter. In

white matter, myelin acts as a multiple barrier structure in the direction that is perpendicular to the fiber bundles. In contrast, on axial kurtosis images white matter shows lower value showing that barrier structure in the direction of fiber bundle is not dense

48

T. Taoka

c

Fig. 4.2 (continued) 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293

4.1.2 S  tudies Using Diffusion Kurtosis Imaging for Central Nervous System Diseases 4.1.2.1 Cerebrovascular Diseases There are several reports on the application of DKI for ischemic stroke in human subjects [8, 20, 23, 24]. Jensen et al. reported that white matter infarction showed increased diffusion kurtosis with a strong fiber tract orientational dependence in their preliminary study. They showed a large increase in Kax in conjunction with a small increase in Krad. They speculated that it could be due to a large change in the intra-axonal diffusivity, which is possibly related to either axonal varicosities or alterations associated with the endoplasmic reticulum [8]. They also reported that the increased kurtosis in the ischemic lesion meant an increase in the complexity or heterogeneity of the water microenvironment in the tissue

[17, 20]. Diffusion kurtosis imaging was also 294 used to predict stroke outcome [24]. This study 295 showed that the volume of an acute lesion on a 296 kurtosis map was identical with the volume on 297 the 1-month follow-up T2-weighted image 298 (T2WI) for large (diameter ≥1 cm) lesions. For 299 small (diameter 3 cm, ≤6 cm, ENE (−)

t3.17 t3.18 t3.19 t3.20

N2b

t3.21 t3.22 t3.23 t3.24

N2c

t3.25 t3.26

N3a

t3.27 t3.28 t3.29 t3.30 t3.31

N3b

t4.1 t4.2 t4.3 t4.4 t4.5

N stage N0 N1

t4.8 t4.9 t4.10

N2

t4.11

N3

931 932 933 934 935 936 937 938

Single node ≤3 cm, ENE (−) Single node >3 cm, ≤6 cm, ENE (−) or Single ipsilateral or contralateral lymph node ≤3 cm in size, ENE (+) Two or more ipsilateral nodes, >3 cm, ≤6 cm, no ENE Bilateral or contralateral nodes, ≤6 cm, no ENE Node >6 cm, no ENE Metastasis in a lymph node larger than 3 cm with ENE (+) or multiple ipsilateral, bilateral or contralateral lymph nodes, ENE (+)

Table 20.4  p16-positive oropharynx nodal staging

t4.6 t4.7

930

Two or more ipsilateral nodes, >3 cm, ≤6 cm, ENE (−) Bilateral or contralateral nodes, ≤6 cm, no ENE Node >6 cm, ENE (−) Clinically positive ENE

Pathologic No regional (cervical) lymph nodes

Clinical (radiologic) No regional (cervical) lymph nodes One or more ipsilateral nodes, ≤6 cm Bilateral or contralateral nodes, ≤6 cm Node >6 cm

Pathologic No regional (cervical) lymph nodes Four or fewer nodes More than four nodes

n/a

may be as effective as surgery [113, 114]. Thus, NCCN guidelines suggest both surgical (total laryngectomy with bilateral neck dissections of levels II-IV) and chemoradiation treatments are acceptable options [107]. Often, the choice of treatment is dictated by the function of the larynx (e.g., in a nonfunctional larynx, organ conservation therapies have less utility), patient choice, and overall health.

20.14.3  Staging and Treatment of p16-Positive Oropharyngeal Squamous Cell Carcinoma As mentioned above, the etiology of oropharyngeal SCC generally is due to high-risk HPV infection, as opposed to tobacco and alcohol use as in other mucosal head and neck subsites. p16-positive SCCs carry an overall better prognosis compared to traditionally tobacco and alcohol-­ related HNSCCs. Notably, with the AJCC 8th edition staging system, T4a and T4b classification of p16-positive oropharyngeal SCCs are eliminated, in favor of a general T4 stage (Table 20.2). The updated nodal staging in the AJCC 8th edition is different for p16-positive oropharyngeal SCC than other mucosal SCC, with separate and unique clinical and pathologic staging (Table  20.4). Notably, for radiologic assessment, extranodal extension is not a criteria for upstaging, unlike oral cavity and laryngeal SCC nodal disease. Treatment options depend on the primary tumor site stage and anatomy, as well as cervical nodal status. For early stage tumors, surgical and XRT options are both acceptable. Transoral robotic surgery or transoral laser approaches may be employed in cases with resectable, accessible tumors. In surgical cases, ipsilateral neck dissection of levels II-IV is performed for lateralized tumors, while bilateral necks must be addressed in midline tumors. After primary surgery, pathology dictates whether adjuvant radiation or chemoradiation is necessary [115–117]. Radiation alone is an alternative treatment option in early stage tumors without aggressive features. In a similar fashion, the primary site and nodal levels II-IV must be addressed. For advanced stage tumors chemoradiation with cytotoxic chemotherapy is often utilized. Given the good prognosis and response to therapy of p16-positive oropharyngeal SCCs, many de-escalation trials are currently being investigated to maintain successful treatment rates while decreasing treatment-­ related toxicities; however, definitive data for such deescalation remains to be seen [118].

939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984

20  Head and Neck 985

986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 AU11 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041

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Author Queries Chapter No.: 20

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Queries

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AU2

Jerry, you have to write a caption for each Figure and select the best images from the PowerPoint.

AU3

Also here and in the remaining cases below, please write the caption and include the images. I have done that.

AU4

Please check if “osteocalastic” should be changed to “osteoclastic” here and elsewhere in the text.

AU5

Please provide caption for Part figure 20.38d

AU6

Please check the sentence “…cord (arrow), which showed…” for clarity in Fig. 20.53 caption.

AU7

Do you mean distinguish SCC from lymphoma?? Please rephrase and clarify. I have rephrased and clarified.

AU8

Please provide caption for Part figures 20.60b, c, d.

AU9

Please provide description for “*” in Table 20.2

AU10

Please check if the edit made in sentence “Very advanced local…” in Table 20.2 is appropriate.

AU11

Please provide chapter title for Ref. 11.

Author’s Response

21

1

Spine Infection

2

John Kim, Eiyu Matsumoto, and Toshio Moritani

3 4 5 6AU1 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

21.1 Introduction Spinal and paraspinal infections may result in significant morbidity and increased mortality, especially in the postoperative setting [1]. Spinal infections may also arise in patients without recent surgery in the increasing elderly population, immunocompromised and diabetic individuals, intravenous drug users, and those undergoing vascular access procedures [2–5]. Therefore, early diagnosis and proper treatment is essential. Despite advances in medical and imaging technology, the diagnosis of this entity is still challenging since the clinical features can sometimes be subtle and misleading. Although conventional MRI of the spine is considered the gold standard for diagnostic imaging with its relatively high sensitivity, specificity, and accuracy, there are still a significant number of challenging cases resulting in delays or misdiagnosis. In such cases, suboptimal diagnosis and management can result in irreversible paralysis, septic shock, and even death [6]. J. Kim (*) Division of Neuroradiology, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected] E. Matsumoto · T. Moritani Department of Internal Medicine, University of Iowa Hospitals & Clinics, Iowa City, IA, USA e-mail: [email protected]; [email protected]

Table 21.1  List of spinal and paraspinal infections 1 2 3 4 5 6

t1.1AU2

Vertebral osteomyelitis-discitis Infectious facet arthropathy Epidural and subdural infections/abscesses Meningitis Myelitis Infections of paraspinal soft tissue and musculature

Diffusion-weighted imaging (DWI) is a useful tool for the diagnosis of central nervous system infections, especially in the detection of abscesses and pus collections involving the brain and spine [7–12]. In this chapter, we demonstrate DWI findings of the common pyogenic and also atypical spinal infections involving the vertebral bodies, intervertebral discs, posterior elements, epidural/subdural spaces, leptomeninges, spinal cord, and paraspinal soft tissues/muscles as well as other disseminated infections (Table 21.1). We also illustrate and discuss the differential diagnosis and imaging pitfalls.

21.2 MRI and Diffusion-Weighted Imaging

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When it comes to diagnosis of spinal infections, conventional MRI has high diagnostic sensitivity, specificity, and accuracy of >90%, making it the diagnostic modality of choice [2, 4, 5, 13–17]. However, even with conventional MRI sequences including gadolinium-enhanced T1-weighted imaging, it is occasionally difficult to differentiate

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abscess/pus collection from other pathologies abscesses and pus collections within the retrosuch as postsurgical fluid collection, cystic/ pharyngeal, prevertebral, and epidural spaces necrotic tumor, hematoma, CSF leak and unusual (Fig. 21.1). patterns of degenerative disc and facet joint The most commonly used technique is spin-­ changes [6]. echo-­type echoplanar DWI, which allows for fast Diffusion-weighted imaging along with con- acquisition times with less motion artifacts. For ventional MR sequences can help identify the appropriate detection and evaluation of abscesses and pus collections with greater accu- abscesses and pus corrections, DWI is performed racy. Abscesses and pus collections are viscous with b values of 0 and 1000 s mm−2 followed by fluid containing cellular debris and necrosis with the calculated ADC map. Parallel imaging factor dead or viable neutrophils. The increased viscos- of 2 is used to reduce distortion in images. Newer ity and density of neutrophils in abscesses cause techniques by vendors include spin-echo type, restricted movement of water molecules which turbo spin echo (TSE) DWI which has signifiresults in hyperintense signal on DWI [11, 12]. If cantly less distortion or susceptibility artifacts gadolinium contrast-enhanced imaging cannot be but longer acquisition times. TSE DWI may be of performed owing to significant acute renal fail- most help in cases where metal hardware is presure, severe contrast allergy, or if other MR ent or where decreasing susceptibility artifacts is sequences are suboptimal in delineating abscesses critical. Small field-of-view (FOV) DWI is also because of motion artifacts, DWI can play a piv- useful for imaging the spinal cord or deep small otal role in diagnosis. For example, Fig.  21.1 organs. shows a patient who did not receive gadolinium Acquisition of the DWI can be done in both contrast due to acute renal failure with GFR of axial and sagittal planes. The axial plane DWI 20. With initial MRI lacking gadolinium-­ allows for better evaluation of paraspinal soft tisenhanced sequences, the preliminary read was sue infections due to its larger FOV and axial incorrectly interpreted as degenerative changes coverage. The sagittal plane DWI helps in evaluof the cervical spine. However, DWI clearly ation of the spinal cord. In the authors’ opinion, if delineates the extension and multiplicity of only one plane were to be acquired, it is best to

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Fig. 21.1 (a–c) 75-year-old previously healthy male presented with neck pain and acute renal failure. No gadolinium contrast given due to severe renal dysfunction. Staphylococcus aureus was proven by blood culture. (a) Sagittal T2-weighted image reveals high signal intensities

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in the disc and vertebral bodies from C2 through C6, with epidural, prevertebral, and retropharyngeal abscesses. (b) Diffusion-weighted imaging clearly demonstrates the extension of the abscesses (arrows) better than the same slice axial T2-weighted image (c)

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acquire in the axial plane with 5 mm thick slices and reformat the images into sagittal plane as necessary to best demonstrate the extension and multiplicity of abscesses.

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destroy the disc substance and then extend into the epidural or paraspinal spaces [5]. The valveless Batson’s venous plexus may contribute to early extension and seeding of spinal and paraspinal infections [5, 16, 24–27]. The terminal branches of the same artery may supply the spi21.3 Typical Pyogenic Infections nal and paraspinal area. Therefore, one site of spinal and paraspinal infections can extend The term pyogenic is defined as “involving or locally or can disseminate hematogenously to relating to production of pus.” Majority or about other spinal/paraspinal areas or remote areas of two-thirds of causative organisms in pyogenic the body. spinal infections are due to Staphylococcus The clinical features and laboratory findings aureus, including methicillin-resistant S. aureus of spinal infections can be subtle and misleading. (MRSA) and methicillin-susceptible S. aureus Local back pain and fever are common but not (MSSA) [2–5, 18]. MRSA is increasingly seen in always present. Elevated white blood cell count both healthcare-associated and community-­ is detected in two-thirds of the cases. Elevated acquired infections and is associated with higher C-reactive protein and erythrocyte segmentation complication rates and prolonged treatment rate are useful screening laboratory tests. courses [19, 20]. Other less common infectious Mortality rates have been reported in the range of agents and their associations include S. epidermis 2–20% for vertebral osteomyelitis and around (spine procedures), Streptococcus and 5% for epidural abscess, whereas in the pre-­ Enterococcus (coexisting infective endocarditis), antibiotic era, they were much greater at 25–70% Escherichia coli (urinary tract infection), [3–5, 18, 25]. Paraplegia or tetraplegia may occur Pseudomonas aeruginosa (intravenous drug in 1% of patients, especially with cervical and abuse), Salmonella (sickle cell disease), and thoracic spine infections secondary to cord comanaerobic bacteria (abdominal and pelvic infec- pression or injury. tions and gastrointestinal procedures). Mycobacterium tuberculosis, non-tuberculous mycobacteria, Nocardia, Actinomyces, and 21.4 Common Patterns of Spinal Brucella (endemic areas) are less common but Infection and Extension can also be pyogenic (please see Sect. 21.6). of Pyogenic Abscess Organisms infect the spinal and paraspinal structures via two routes: hematogenous and Pyogenic spinal and paraspinal infections can be non-hematogenous [2–5]. In approximately half categorized into five common patterns of extenof spinal infections, the source is hematogenous sion (Table 21.2) [6]. dissemination, one-third is contiguous spread Recognition of these patterns is helpful to (surgical intervention, trauma and direct inocula- identify abscess/pus collections and other spinal tion) and in the remaining cases, the source of infections on MRI and DWI. infection is unidentified. The dense region of arterial and venous channel networks is confined Table 21.2  Common patterns of spinal and paraspinal to bony end plates in adults, whereas in children, infections hyaline cartilage contains perforating vessels 1 Osteomyelitis/discitis until 7 years of age with concomitant increased 2 Epidural/paraspinal abscess with osteomyelitis/ discitis susceptibility to primary discitis [21–23]. 3 Epidural/paraspinal abscess with facet joint infection However, disc revascularization can occur with 4 Epidural/paraspinal abscess without concomitant degenerative disc changes and spondyloarthropaosteomyelitis/discitis or facet joint infection thy [5]. Organisms that produce proteolytic 5 Intradural abscess (subdural abscess, purulent meningitis and spinal cord abscess) enzymes quickly spread into the end plate and

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21.4.1 Osteomyelitis/Discitis The most common form of spinal infection is vertebral osteomyelitis, also referred to as osteomyelitis/discitis or spondylodiscitis, which involves the intervertebral disk and the adjacent vertebrae. The disease has a relatively insidious early clinical course, however if left untreated can have devastating consequences. Spondylodiscitis affects a bimodal age distribution, with peaks below 20 years of age and again between 50 and 70 years of age. For unknown reasons, males are twice as likely to be affected as females (2:1 male-tofemale ratio). The most common causative agent of pyogenic infection is Staphylococcus aureus; however other organisms may be encountered, especially with immunocompromised patients or IV drug abusers. The infection is typically spread hematogenously to the vertebral endplate in adults, and it can continue to spread through the intervertebral disc to involve the contiguous vertebral body endplate. Other routes of transmission may also be encountered, including direct inoculation from spinal procedures, direct extension from nearby infection, and hematogenous spread to the facet joints (facet septic arthritis). The lumbar spine is the most commonly affected region. The typical presentation is nonspecific back pain, with fever present in half of patients. Neurological symptoms are rare. Laboratory findings such as leukocytosis and elevated ESR/CRP are also nonspecific but may help rule out infection if normal. Unfortunately, early infection can often have a nonspecific appearance paralleling their nonspecific clinical symptoms. MR is the most sensitive and specific for diagnosis of spondylodiscitis; however due to the nonspecific clinical symptoms, there is generally a 4-month delay in obtaining MR imaging. MRI has also proven useful in follow-up imaging to evaluate treatment response in patients whose clinical symptoms have not improved. Plain radiographs are often the first imaging test obtained, yet are not adequate for diagnosis as imaging changes of infection often mimic degenerative changes early on and will not show the classic features of vertebral endplate destruction until late in the disease

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course. Gallium-67 SPECT and F-18 FDG PET studies have also provided useful information when the diagnosis is in doubt or when MRI is contraindicated. On MRI, marrow edema (T1 hypointense, T2 hyperintense signal) is often the earliest abnormality encountered along the anterior aspects of a vertebral body endplate. Following involvement of the endplate, the infection spreads throughout the vertebral marrow and into the adjacent intervertebral disc resulting in discitis, which is demonstrated as fluid-like disc signal (T1 variable, T2 hyperintense signal). The infection will continue to spread locally through the disc to the neighboring vertebral endplate. Gadolinium enhancement is typically diffuse throughout the affected marrow and disc, although peripheral disc enhancement may be seen (Fig. 21.2). Multilevel involvement (greater than 2 levels) of vertebral bodies and discs can occur in pyogenic spinal infections but less commonly in tuberculosis. While the most well recognized imaging pattern involves two contiguous vertebrae and the intervening intervertebral disc, other atypical patterns of disc/vertebral involvement may be encountered including only one vertebral body, one vertebral body and one disc, or two vertebral bodies with a normal appearing intervertebral disc (Fig. 21.3). When diagnosis is in doubt, follow-up imaging may be obtained which can show advanced changes of infection in as little as 8 days. As the infection progresses, erosions may occur in the vertebral endplates (Fig.  21.4). Vertebral body height loss and collapse may be seen later in the disease course. Surgical consult is often warranted at these later stages to determine if drainage, decompression, or stabilization is required.

21.4.2 Epidural/Paraspinal Abscess with Osteomyelitis/Discitis In more extensive cases, enhancing phlegmon may begin to develop in the adjacent epidural and paraspinal areas, including the psoas mus-

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Fig. 21.2 (a–c) Sagittal T1- (a), T2- (b), and fat-­saturated post-contrast T1 (c) images of the lumbar spine. There is T1 hypointense signal and patchy enhancement of the L4 vertebral body marrow (arrow), with early involvement of the superior endplate of L5 and anterior inferior endplate

of L3. T2 images show edema within the L4-L5 greater than L3-L4 intervertebral discs, with mild marginal disc enhancement at L4-L5. There was no significant surrounding soft tissue or epidural enhancement at this time point

cle. These phlegmonous changes can evolve further to abscesses, which classically demonstrate fluid-­ like signal and peripheral enhancement (Fig. 21.4). Spinal epidural abscess refers to a localized collection of pus between the dura mater and osseous spinal canal. Epidural/paraspinal abscess associated with osteomyelitis/discitis is the most common pattern of extension and is often seen in the anterior part of the epidural space. This pattern of extension more often occurs in the lumbar spine. Cervical spondylitis can progress anteriorly causing retropharyngeal abscess and inferiorly causing mediastinitis. Thoracic spondylitis can cause mediastinitis, empyema, and pericarditis, while lumbar spondylitis causes peritonitis, subdiaphragmatic abscess, and infection in the pelvis. Abscesses can also form within the vertebral body and disc [5]. Although epidural abscess usually extends over three to four vertebrae, it can involve the whole spine and is known as panspinal infection [3, 28, 29]. MRI findings of pyogenic osteomyelitis/discitis with large epidural abscesses on T2-weighted images may sometimes demonstrate spinal cord

compression and abnormal cord signal [2, 11, 13, 16, 30, 31]. Gadolinium-enhanced T1-weighted images with fat saturation can differentiate infection from postsurgical changes, and pus collection from granulation/phlegmon, based on their enhancement patterns [13, 32, 33]. However, in cases where the abscesses are small and/or gadolinium-­ enhanced T1-weighted images are not available, DWI may prove to be crucial in detection of abscesses in the epidural space and paraspinal soft tissues (Figs. 21.1 and 21.5).

21.4.3 Epidural/Paraspinal Abscess with Facet Joint Infection Facet joint infection (septic arthritis) is rare but has increased recently, owing to the increase in the elderly population, immunocompromised patients, and spine procedures [34–37]. This is another important route of infection resulting in epidural/ paraspinal abscesses. Facet joint infection is often hematogenous but also caused by epidural anesthesia and spinal procedures. It is usually unilateral, and presents more acutely than does

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Fig. 21.3  Evolution of spondylodiscitis: The top row of images show initial presentation of L4 and L5 vertebral osteomyelitis, with sparing of the intervertebral disc (red arrow). The infection appears to have spread longitudinally as a ventral epidural phlegmon, extending from L4 to L5 (yellow arrow). The bottom row of images was obtained 6 weeks later after the patient complained of increasing back pain. These show increasing marrow sig-

nal abnormality with worsening endplate erosions along upper endplate of L5 posteriorly (red arrowhead). The intervertebral disc now demonstrates signs of infection with hyperintense T2 signal and peripheral enhancement. The epidural phlegmon and soft tissue enhancement are relatively unchanged. Findings suggest persistent, evolving infection

spondylodiscitis. It frequently involves the L4-L5 joints. Epidural abscess associated with facet joint infection tends to occur in the posterior part of the epidural space. Degenerative facet joints may constitute an increased risk for infection [35]. Infection of the facet joint often spreads contiguously to the epidural space and to the paraspinal musculature. Intradural extension associated with facet joint infection has also been reported [38]. If there is no evidence of spondylodiscitis, the facet joints should be evaluated very carefully. The MRI findings of facet joint infection are sometimes very subtle and resemble degenerative joint disease. MRI shows swelling of the capsule

and periarticular soft tissue of the facet joint. DWI is useful in the detection of small abscess or pus collection in the posterior paraspinal muscle adjacent to the facet joint (Fig. 21.5).

21.4.4 Epidural/Paraspinal Abscess Without Concomitant Osteomyelitis/Discitis or Facet Joint Infection The route of this pattern of infection is variable: (1) isolated epidural or paraspinal abscess due to hematogenous spread, (2) direct extension from

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Fig. 21.4  The top row of images demonstrates abnormal marrow signal and enhancement at T8-T9 (red arrow) consistent with discitis/osteomyelitis. There is epidural enhancement with a small dorsal epidural abscess (yellow arrow). Follow-up MRI after 8 weeks of antibiotic therapy

shows persistent discitis/osteomyelitis at T8-T9 with progression of endplate destructive changes and new T8 vertebral body height loss (blue arrow). The epidural abscess has resolved, with minimal residual epidural enhancement

wound infection after surgery or trauma, and (3) direct inoculation from spinal intervention such as epidural steroid injection or nerve block. Isolated epidural abscess is more commonly seen in the cervical spine [5, 18]. Isolated epidural

abscess tends to occur posteriorly but can occur anteriorly. Diagnosis is often difficult clinically and radiologically. MRI with DWI is the key imaging technique in the diagnosis of isolated epidural abscess (Fig. 21.6).

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Fig. 21.5 (a–c) 43-year-old M with IV drug use. MSSA proven. Epidural and paraspinal abscesses with septic facet joint infection of the cervical spine. (a) Axial T1-weighted

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post-contrast with fat saturation, (b) DWI, and (c) ADC map demonstrate epidural abscesses (arrows) with left facet joint infection and paraspinal abscesses (arrowheads)

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Fig. 21.6 (a–d) Isolated small ventral epidural abscess mimicking a disc herniation. Streptococcus milleri was proven by surgical drainage. (a) Sagittal post-contrast T1-weighted image shows a lesion at L3/4 ventrally with peripheral enhancement. There is no abnormal enhancement in the adjacent disc and vertebral body. (b) Axial

T2-weighted image shows slightly hyperintense lesion on T2-weighted image. (c, d) Diffusion-weighted imaging (c) early reveals the lesion as hyperintense with decreased apparent diffusion coefficient (d) consistent with an epidural abscess (arrows). (Reprinted and adapted from Moritani and Kim et. al. BJR article)

21.4.5 Intradural Abscess (Subdural Abscess, Purulent Meningitis, and Spinal Cord Abscess)

21.5 Management of Patients with Spinal and Paraspinal Infections

Within the spinal canal, the epidural space is the most commonly affected but subdural involvement with meningitis and direct involvement of the spinal cord can occur [38–41]. Intradural abscess is a rare entity but it is a neurosurgical emergency associated with high morbidity and mortality. Progressive neurological deficits, severe pain, and fever suggest the diagnosis. Subdural abscess and purulent meningitis can be secondary to hematogenous spread, secondary infection after spine surgery and inoculation due to lumbar puncture, epidural steroid injection or nerve block (Fig. 21.7). Extension of bacterial meningitis with spinal dysraphism is also an important etiology in children with this condition (Fig.  21.8). MRI with DWI is very useful for the diagnosis of intradural abscesses [41].

MRI, in particular DWI, may play an important role in defining burden of infection, treatment planning, site selection for biopsy, or decision-­ making of surgical drainage [6]. Optimal management of patients with spinal and paraspinal infections is crucial. Diagnostic delays and suboptimal management may potentially result in irreversible paralysis, worsening sepsis, and death.

21.5.1 Treatment of Osteomyelitis/ Discitis sources of osteomyelitis/discitis include skin/soft tissue infection, genitourinary tract infection, infective endocarditis, intravenous drug abuse with contamination, respiratory tract infection, and intraabdominal infection. Although Staphylococcus

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Fig. 21.7 (a–c) Purulent meningitis after steroid epidural injection at an outside hospital. Methicillin-susceptible Staphylococcus aureus was proven in the cerebrospinal fluid (CSF). (a, b) Sagittal (a) and axial (b) post-contrast T1-weighted image show extensive meningeal enhance-

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ment in the thecal sac. (c) Diffusion-weighted imaging demonstrates hyperintensity in the thecal sac consistent with purulent meningitis (arrow). Methicillin-resistant Staphylococcus aureus was proven in the CSF. (Reprinted and adapted from Moritani and Kim et. al. BJR article)

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Fig. 21.8 (a–c) Spinal cord abscess with Chiari 1 malformation. (a) Sagittal T2-weighted image shows a syrinx as hyperintense lesions from C2 through C6. (b, c) Diffusion-weighted imaging demonstrates hypointensity

in the syrinx (b) and hyperintensity in the spinal cord abscess (arrow) (c). (Reprinted and adapted from Moritani and Kim et. al. BJR article)

aureus is the most common organism, other Grampositive, Gram-negative, and even anaerobic bacteria can cause this disease entity. In addition, Mycobacterium tuberculosis and Brucella spp. are common in endemic regions. Therefore, no empiric treatment is recommended unless the patient is hemodynamically unstable [42]. Obtaining blood cultures followed by imageguided needle biopsy or surgical open biopsy should be pursued. Sensitivity of each procedure is 53% and 91%, respectively [43]. Preprocedural antibiotic greatly reduces the yield for needle biopsy, but not for open biopsy [43]. After con-

firming etiologic agent, 6 weeks of parenteral or highly bioavailable oral antimicrobial therapy are often curative for usual pyogenic infections [42]. Patient’s symptoms and inflammatory markers (e.g., ESR, CRP) should be monitored. Repeat MRI for follow-up purpose has very limited value and may give an impression of clinical progression, even though there is otherwise clinical improvement. Once treatment with IV antibiotics is begun, the adjacent epidural enhancement is often the first feature to improve on MR imaging. However, T2 hyperintensity and enhancement within the

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Fig. 21.9 (a–g) Sagittal CT (a) in a patient with recent L2-L3 laminectomy and discectomy and remote L4-L5 fusion demonstrates erosions involving the inferior endplate of L2 and superior endplate of L3. Sagittal T1 (b), STIR (c), and post-contrast fat-sat T1 (d) show the extent of infection involving the vertebral bodies, intervertebral disc, epidural space, and overlying soft tissues. A rim-­

enhancing abscess can be seen adjacent to the spinal hardware. Follow-up MRI after 6 weeks of antibiotic therapy with sagittal T1 (e), STIR (f), and post-contrast T1 (g) show persistent but improved marrow signal abnormalities and resolution of epidural enhancement. The patient was monitored clinically without further antibiotic therapy needed

marrow and intervertebral disc often persists after several weeks of antibiotics, and may even appear more extensive despite improving symptoms. This is postulated to represent granulation tissue and the effects of healing, and should not be taken as signs of treatment failure. The recommended approach is to limit the use of follow-up MRI to patients with persistent elevation of inflammatory markers, in patients with persistent pain, or in those that develop new neurologic signs or symptoms. Routine repeat MRI images are no longer recommended [42]. If the patient is not improving clinically, follow-up

MRI may be obtained after 4 weeks of treatment. As the endplates heal, fibrosis and sclerosis take the place of prior enhancing granulation tissue, leading to marrow hypointensity on all sequences and eventual lack of enhancement (Fig. 21.9).

21.5.2 Treatment of Spinal Epidural Abscess The incidence of spinal epidural abscess has recently increased along with increased prevalence of intravenous drug use and healthcare-­associated

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bacteremia. The infecting microorganism in most cases (50% to 90%) is Staphylococcus aureus with hematogenous dissemination. Concomitant osteomyelitis/discitis with or without psoas abscess is exceedingly common. Bacteremia is detected in 60% of cases [3]. Although the pathogenesis and microorganism are very similar to osteomyelitis/discitis, more urgent intervention is needed for the management of spinal epidural abscess as delay could lead to the future possibility of spinal cord injury. After blood cultures are obtained, empiric antibiotic with prompt surgical consultation should be obtained. Traditionally, surgical decompression along with drainage of the abscess followed by long-term antimicrobial therapy (4–8 weeks) has been the mandatory therapeutic principle [3–5, 18]. Since paralysis lasting more than 24–36 h is unlikely to reverse, urgent decompressive laminectomy is indicated for epidural abscess. However, there are a number of reports of success with a nonsurgical approach. Caution should be made in interpreting such data [3]. To date, there is no randomized trials performed for medical versus surgical management of spinal epidural abscess. In regard to repeat MRI imaging, spinal epidural abscess differs from osteomyelitis/discitis. It is often recommended to obtain routine repeat MRI, especially in the case where the abscess was never surgically drained. Antibiotic duration is determined by a clinician with consideration of (1) patient symptoms, (2) inflammatory markers (ESR, CRP), and (3) MRI findings. The typical treatment duration is 6–8 weeks [3].

21.6 Tuberculosis and Atypical Infections Tuberculosis is one of the more common infections in endemic regions of the world [4, 18, 44, 45]. Reactivation of tuberculosis has increased with HIV infection, recent chemotherapy and concurrent steroid use and the use of antitumor necrosis factor agents (infliximab). Among the 30 million people currently affected with tuberculosis worldwide, 1–3% have skeletal involvement.

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Tuberculous spinal infections are more indolent than the typical bacterial pyogenic spinal infection with a more gradual onset of symptoms over months to years. Tuberculous spondylitis, also known as Pott’s disease, follows a slightly different course from the more typical bacterial pyogenic infection. Tuberculosis is more common in the thoracic spine than in the lumbar spine [4, 18]. Infection typically begins in the anterior vertebral body and spreads along the undersurface of the longitudinal ligaments (subligamentous spread) to involve multiple adjacent vertebrae and often form large paravertebral abscesses. Vertebral body collapse is much more common with tuberculosis than with typical bacterial pyogenic infections, creating the classic “gibbus deformity” of short segment kyphosis. Intervertebral discs are relatively preserved since M. tuberculosis does not have proteolytic enzymes that allow it to spread into the disc. Abscesses can grow to be quite large, without causing significant pain or pus formation, lending to the term “cold abscesses.” Involvement of posterior elements of the spine and psoas abscess is another characteristic of tuberculosis. Paravertebral calcifications are a late finding, but help distinguish granulomatous from pyogenic infection. Evidence of pulmonary involvement is only seen in 50% of cases. MRI typically demonstrates destruction of more than two consecutive vertebrae with relative sparing of the intervening disc and associated large paraspinal abscesses [4, 18, 44]. Epidural infection can be seen. DWI usually shows restricted diffusion in the abscess (Fig. 21.10). DWI can show partially hyper- and partially hypointense signal associated with high and low ADC values. The high ADC component may represent more serous fluid in a tuberculous cold abscess than in viscous pus formation. Routine surgery for spinal tuberculosis is not advocated. When necessary, the combined anterior and posterior approaches have been reported to be superior in lumbar and multilevel thoracic disease [46, 47]. Brucellosis is an important endemic infection commonly seen in Mediterranean countries or

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Fig. 21.10 (a–d) Tuberculosis. Epidural abscess and paraspinal abscesses with spondylodiscitis. Tuberculosis was proven by surgical drainage. (a, b) Sagittal T2-weighted image (a) and axial (B) post-contrast T1-weighted images with fat saturation show osteomyelitis from T4 through T9 with subligamentous extension, epidural abscess, and paraspinal abscesses. The discs are

relatively spared. (c, d) Diffusion-weighted imaging (c) demonstrates partially hyper- and partially hypointensity with high and low apparent diffusion coefficient (ADC) values (arrows) (d). The high ADC component represents serous fluid in a cold abscess. (Reprinted and adapted from Moritani and Kim et. al. BJR article)

developing nations. Brucella spp. is a Gram-­ negative coccobacilli which is encountered most commonly from ingestion of unpasteurized milk or milk products. The organism has a predilection for the musculoskeletal system, particularly the spine where both focal and diffuse forms exist. Brucellar spondylitis is often difficult to distinguish from its tuberculous counterpart but is more likely to involve the lumbar spine [48]. Gibbus deformities and multilevel involvement are less commonly seen in brucellosis; however serological tests should be performed for both organisms for definitive diagnosis (Fig.  21.11). Paravertebral and epidural abscesses are also common in Brucellosis [48, 49]. Fungal spinal infections include candidiasis (Fig.  21.12), aspergillosis, blastomycosis, cryptococcosis, Sporothrix, histoplasmosis, and coccidioidomycosis [2, 3, 18, 40, 45]. Positive blood and tissue cultures are helpful in establishing the diagnosis but causative organisms are not proven in a significant number of cases. Therapy is often delayed because of the difficulty in making the diagnosis. The imaging findings of most fungal spinal infections are nonspecific. Vertebral body involvement with destruction can occur mimick-

ing tuberculosis. Absence of T2 high signal in adjacent discs is suggestive of fungal spondylitis [11, 50–52]. Candida spondylitis may be a simultaneous occurrence or late manifestation of hematogenously disseminated candidiasis. The most common location of Candida spondylitis is the lumbar spine. The formation of epidural abscess is uncommon [51]. Echinococcosis, onchocerciasis, toxoplasmosis, and toxocariasis can also involve the spine [2, 53].

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21.7 Mimics of Spinal Infection

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Early changes often mimic degenerative disease, specifically Modic Type I degenerative endplate changes. Type I Modic changes represent edema within the vertebral endplates. The intervertebral disc may also demonstrate T2 hyperintensity and enhancement, although this is more common with infectious spondylitis. Vacuum phenomenon and lack of adjacent phlegmon/abscess are important findings that suggest degenerative changes as opposed to infection (Fig. 21.13).

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Fig. 21.11 (a–e) Patient is a 62-year-old recent immigrant from Mexico. Sagittal T1 (a), T2 (b), and fat-sat post-contrast T1 (c) show abnormal vertebral body marrow signal at L1-L2 and L4-L5 (red arrow). There is a small epidural phlegmon and pre-spinal soft tissue

enhancement at L1-L2. Axial post-contrast (d) and DWI (e) show large, rim-enhancing fluid collections with diffusion restriction in both psoas muscles consistent with large abscesses (yellow arrow). Sampling of this fluid revealed Brucella as the causative organism

21.7.2 Schmorl’s Nodes

21.7.3 Ankylosing Spondylitis

Acute invagination of the vertebral body endplate by a portion of the intervertebral disc can also produce marrow edema and enhancement. This is usually confined to a concentric ring in the marrow around the abnormality, and will not involve the entire endplate or adjacent vertebral body. Additionally, surrounding soft tissues will be normal (Fig. 21.14).

Early in its disease course, active inflammation and erosions at the corners of the endplates (Romanus lesions) may be indistinguishable from infection, although multiple levels of involvement provide a clue. A late manifestation of ankylosing spondylitis is pseudoarthrosis. This occurs after a traumatic event on the fused spine, creating a 3-column stress fracture that extends to

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Fig. 21.12 (a–c) Candidiasis. Epidural and psoas infections with spondylodiscitis. Candida was proven by biopsy. (a, b) Sagittal (a) and axial (b) post-contrast T1-weighted image with fat saturation show spondylodis-

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Fig. 21.13 (a–c) Sagittal T1 (a), STIR (b), and post-­contrast fat-sat T1 (c) show Grade 1 spondylolisthesis at L5-S1 with Modic type I endplate changes (T1 hypointense, STIR hyper-

citis at L2/3 and L4/5 with enhancing psoas and epidural lesions. (c) Diffusion-weighted imaging demonstrates hyperintensity in the epidural and psoas lesions suggestive of small pus collections (arrows)

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intense) and mild associated enhancement. There is loss of intervertebral disc height with decreased signal intensity on STIR, suggesting desiccation and degenerative disc disease

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Fig. 21.14  The top row of images demonstrates irregularity of the inferior endplate of L4 with a small surrounding rim of edema and enhancement (red arrow). Axial post-contrast image shows a small abscess in the right psoas muscle confirming infectious spondylitis (short arrow). The bottom row shows an irregularity of the supe-

rior endplate of L1, also with marrow edema and enhancement (yellow arrow). There were no surrounding soft tissue abnormalities, and patient inflammatory markers were normal, making acute Schmorl’s node the likely diagnosis

the posterior elements. The fracture line provides a point of mobility in an otherwise fuse spine, and surrounding inflammation results (Andersson lesion). This should be distinguishable from infection given the clinical history, lack of widespread edema/enhancement, and involvement of the posterior processes (Fig. 21.15).

can be similar to extruded or sequestrated disc herniation on MRI. DWI demonstrates facilitated diffusion in disc herniation (Fig.  21.16) while diffusion is restricted in an abscess. Epidural/subdural hematoma (Fig. 21.17) and subarachnoid hemorrhage can show hyperintensity on DWI in the late subacute to early chronic phase and in the hyperacute phase. T1-weighted images usually show hyperintensity in the late subacute hematoma due to methemoglobin and can thus differentiate hematoma from abscess. However, the chronic phase of hematoma can show T1 iso- to hypointensity with restricted diffusion due to the high viscosity containing hemichrome.

21.8 Mimics of Epidural/ Paraspinal Abscesses Disc herniation is extremely common and the diagnosis on MRI is usually straightforward. However, isolated epidural abscess (Fig.  21.6)

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Fig. 21.15 (a–c) Sagittal T1 (a), STIR (b), and T1 post-­ contrast (c) images in a young patient with back pain demonstrate edema of the anterior superior endplates of T12, L2, L4, L5, and S1 without appreciable enhancement

(best seen on STIR). Additionally, the patient was found to have bilateral sacroiliitis (not shown). Findings represent early inflammation of ankylosing spondylitis

CSF leaks can dissect through the epidural space associated with venous plexus engorgement and may appear similar to epidural abscess. However, DWI shows facilitated diffusion in the CSF collection in the epidural space and the venous plexus (Fig. 21.18). Intraspinal epidermoid can have restricted diffusion that can mimic infection (Fig. 21.19). Postsurgical wound infection (pus collection) has restricted diffusion while postsurgical fluid collection has facilitated diffusion (Fig.  21.20). However, postsurgical hematoma can have restricted diffusion with well-defined round shape at the postsurgical site (Fig. 21.21) (Table 21.3).

21.9 Limitation and Pitfalls on Diffusion-Weighted Imaging and Apparent Diffusion Coefficient Maps Many kinds of echoplanar imaging (EPI) and non-EPI diffusion sequences with various b-­values have been reported in the diagnosis of spine and spinal cord disease [54–56]. We routinely use spin-echo-type single-shot EPI sequence with parallel imaging techniques in our spine protocols because of the general availability and short acquisition time. For the detection and differentiation of abscesses or pus collections, we use a b-value of 0 and 1000 s mm−2 with

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Fig. 21.16 (a–c) Extruded disc herniation. Sagittal (a) and axial (b) T2-weighted images show a left lateral recess extruded disc which demonstrated facilitated diffusion on DWI (c)

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Fig. 21.17 (a–c) Epidural hematoma after trauma. (a, b) Sagittal T2- (a) and T1-weighted images (b) show hyperintense epidural hematoma from T2 through T6 posteri-

orly. (c) Diffusion-weighted imaging demonstrates hyperintensity in the epidural hematoma (arrow) similar to an epidural abscess

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Fig. 21.18 (a–c) Cerebrospinal fluid leak after lumbar puncture in an infant. (a) Sagittal T2-weighted image shows hyperintensity in the posterior epidural space extending through the whole spine (arrows). (b) Axial

post-contrast T1-weighted image with fat saturation shows epidural enhancement due to the prominent venous plexus. (c) Diffusion-weighted imaging demonstrates hypointensity in the posterior epidural space (arrowhead)

the ADC map. DWI with lower b-value can show the CSF as partially high signal intensity due to the intravoxel incoherent motion and T2 effect (Fig.  21.22) [57]. To acquire less distortion diffusion-­weighted images with the appropriate diffusion weighting, we use a parallel imaging factor of two.

Since DWI is typically implemented with EPI with fat saturation, there are many artifacts (susceptibility artifact, incomplete fat suppression, N/2 ghosting artifact, eddy current artifact, and motion artifact) and imaging pitfalls on DWI and the ADC map. Paramagnetic susceptibility artifacts from surgical metallic devices cause signifi-

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Fig. 21.19 (a–c) Ruptured epidermoid with chemical meningitis. (a, b) Sagittal MRI shows a large T2 hypointense (a) and T1 hyperintense (b) mass with posterior scalloping of the vertebral bodies. (c) Diffusion-weighted

imaging demonstrates slight hyperintensity (not shown) in the mass with decreased apparent diffusion coefficient (arrow)

cant image distortion on DWI (Fig.  21.23). Incomplete fat saturation also occurs related to paramagnetic and diamagnetic susceptibility artifacts.

DWI usually shows hyperintensity in epidural and paraspinal abscesses associated with significantly lower ADC values than those in CSF collections, postoperative fluid collections, and

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Fig. 21.20 (a–c) Postoperative fluid collection. (a) Axial post-contrast T1-weighted image with fat saturation shows fluid collections at the postoperative site with peripheral enhancement. (b, c) Diffusion-weighted imag-

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ing shows the fluid collections as hypointense (b) associated with increased apparent diffusion coefficient (c) consistent with seroma or pseudomeningocele (arrows)

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Fig. 21.21 (a–c) Postoperative hematoma. (a, b) Sagittal (arrow) with decreased apparent diffusion coefficient (not T2- (a) and T1-weighted images (b) show a hyperintense shown) similar to an abscess. A few white blood cells with round lesion at the postoperative site. (c) Diffusion-­ blood products were proven by drainage weighted imaging shows hyperintense signal in the lesion

21  Spine Infection Table 21.3  Important differential diagnoses include epidural, subdural, or subarachnoid hemorrhage, cerebrospinal fluid (CSF) leak, disc herniation, synovial cyst, granulation tissue, intra- or extradural tumor, and postsurgical fluid collections Differential diagnoses: Mimics of epidural and paraspinal abscesses Epidural, subdural, or subarachnoid hemorrhage Cerebrospinal leak Disc herniation Synovial cyst Granulation tissue Tumor Postsurgical fluid collections

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Fig. 21.22 (a, b) Diffusion-weighted imaging (DWI) with b-values of 500 vs. 1000 s mm−2. (a) We routinely use DWI in spine protocols with a b-value of 1000 s mm−2 for the evaluation of abscess and pus collection. (b) DWI with a b-value of 500 s mm−2 shows partially hyperintense normal cerebrospinal fluid in the spinal canal, which is difficult to differentiate from pus collection. DWI use with low or intermediate b-value is not adequate for this purpose since the signal is more influenced by T2 effect and intravoxel incoherent motion 678 679 680 681 682

muscles. However, ADC maps sometimes show mixed intensity and slightly higher values than those reported in brain abscesses. Mixed intensity and slightly higher ADC values have been reported in subdural, epidural abscess, purulent

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meningitis, and abdominopelvic abscesses than those in brain abscesses. The higher ADC values may be affected by prior antibiotic treatment, or may be diluted by exudates, CSF, or other fluid [8–10, 58]. If the abscess or pus collection is small, the ADC values can result in errors due to volume-averaging. DWI has limited usefulness for the evaluation of vertebral body and disc. DWI usually shows slightly high signal intensity with increased ADC in vertebral osteomyelitis (Fig.  21.24). Pathologically, the signal abnormality corresponds to bone marrow necrosis and edema with inflammatory cell infiltration. ADC values of the bone marrow in spinal infection have been reported as significantly higher than those in malignancy. However, an overlap of ADC values is noted [11, 59–61]. DWI signals and the ADC values of the vertebral body are variable depending on the proportion between red and yellow bone marrow associated with aging, degenerative changes, and other conditions [56, 62]. The bone marrow fat (yellow bone marrow) causes signal void on DWI because DWI typically uses chemical fat saturation techniques. Moreover, bony trabeculae cause signal loss related to diamagnetic susceptibility artifacts. Thus, the calculated ADC values are affected by the chemical fat saturation and susceptibility artifacts. DWI signals and ADC values of the disc can also be different owing to the variable presence of water contents with aging and degenerative changes.

21.10 Conclusions DWI with ADC maps is useful in early and accurate diagnosis of spinal and paraspinal infections, treatment planning and preoperative assessment for surgical drainage and biopsy. Important differential diagnoses include epidural, subdural, or subarachnoid hemorrhage, CSF leak, disc herniation, synovial cyst, granulation tissue, intra- or extradural tumor, and postsurgical fluid collections. Recognition of artifacts on DWI and ADC maps is essential when interpreting DWI of spinal and paraspinal infections.

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Fig. 21.23 (a–d) Susceptibility artifacts. (a, b) Sagittal (a) and axial (b) T2-weighted images show a hyperintense lesion in the epidural space posterolaterally (arrows) with relatively mild susceptibility artifacts from a fusion device. (c) Axial post-contrast T1-weighted image with

fat saturation is difficult to detect the epidural abscess owing to motion artifacts. (d) Diffusion-weighted imaging shows no apparent hyperintensity owing to susceptibility artifacts from the fusion device, erroneously not identifying the epidural abscess

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Fig. 21.24 (a–d) Vertebral osteomyelitis. (a) Sagittal post-contrast T1-weighted image with fat saturation shows enhancement in the disc and vertebral bodies at L4 and L5 consistent with discitis/osteomyelitis. (b, c) There is mild hyperintensity on diffusion-weighted imaging (b)

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with increased apparent diffusion coefficient (c) in the adjacent bone marrow. (d) Pathology demonstrates extensive bone necrosis and edema with patchy inflammatory cell infiltrations

3. Darouiche RO (2006) Spinal epidural abscess. N Engl J Med 355(19):2012–2020 4. Kourbeti IS, Tsiodras S, Boumpas DT (2008) Spinal infections: evolving concepts. Curr Opin Rheumatol 20(4):471–479 5. Tins BJ, Cassar-Pullicino VN (2004) MR imaging of spinal infection. Semin Musculoskelet Radiol 8(3):215–229

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6. Moritani T et  al (2014) Pyogenic and non-pyogenic spinal infections: emphasis on diffusion-weighted imaging for the detection of abscesses and pus collections. Br J Radiol 87(1041):20140011 7. Fertikh D et  al (2007) Discrimination of capsular stage brain abscesses from necrotic or cystic neoplasms using diffusion-weighted magnetic resonance imaging. J Neurosurg 106(1):76–81 8. Ferreira NP et al (2005) Imaging aspects of pyogenic infections of the central nervous system. Top Magn Reson Imaging 16(2):145–154 9. Abe M et al (2002) Purulent meningitis with unusual diffusion-weighted MRI findings. Eur J Radiol 44(1):1–4 10. Tsuchiya K et al (2003) Diffusion-weighted MRI of subdural and epidural empyemas. Neuroradiology 45(4):220–223 11. Thurnher MM, Bammer R (2006) Diffusion-weighted magnetic resonance imaging of the spine and spinal cord. Semin Roentgenol 41(4):294–311 12. Eastwood JD, Vollmer RT, Provenzale JM (2002) Diffusion-weighted imaging in a patient with vertebral and epidural abscesses. AJNR Am J Neuroradiol 23(3):496–498 13. Trombly R, Guest JD (2007) Acute central cord syndrome arising from a cervical epidural abscess: case report. Neurosurgery 61(2):E424–E425; discussion E425. 14. Palestro CJ, Love C, Miller TT (2006) Infection and musculoskeletal conditions: Imaging of musculoskeletal infections. Best Pract Res Clin Rheumatol 20(6):1197–1218 15. Butler JS et al (2006) Nontuberculous pyogenic spinal infection in adults: a 12-year experience from a tertiary referral center. Spine (Phila Pa 1976) 31(23):2695–2700 16. Boden SD et  al (1992) Postoperative diskitis: distinguishing early MR imaging findings from normal postoperative disk space changes. Radiology 184(3):765–771 17. Modic MT et  al (1985) Vertebral osteomyelitis: assessment using MR. Radiology 157(1):157–166 18. Lury K, Smith JK, Castillo M (2006) Imaging of spinal infections. Semin Roentgenol 41(4):363–379 19. Conaughty JM et al (2006) Efficacy of linezolid versus vancomycin in the treatment of methicillin-resistant Staphylococcus aureus discitis: a controlled animal model. Spine (Phila Pa 1976) 31(22):E830–E832 20. Stevens QE et al (2007) Reactivation of dormant lumbar methicillin-resistant Staphylococcus aureus osteomyelitis after 12 years. J Clin Neurosci 14(6):585–589 21. Mahboubi S, Morris MC (2001) Imaging of spi nal infections in children. Radiol Clin N Am 39(2):215–222 22. Bair-Merritt MH, Chung C, Collier A (2000) Spinal epidural abscess in a young child. Pediatrics 106(3):E39 23. Glazer PA, Hu SS (1996) Pediatric spinal infections. Orthop Clin North Am 27(1):111–123

J. Kim et al. 24. Wiley AM, Trueta J (1959) The vascular anatomy of the spine and its relationship to pyogenic vertebral osteomyelitis. J Bone Joint Surg (Br) 41-B:796–809 25. Ozuna RM, Delamarter RB (1996) Pyogenic vertebral osteomyelitis and postsurgical disc space infections. Orthop Clin North Am 27(1):87–94 26. Calderone RR, Larsen JM (1996) Overview and classification of spinal infections. Orthop Clin North Am 27(1):1–8 27. Uchida K et  al (2010) Epidural abscess associated with pyogenic spondylodiscitis of the lumbar spine; evaluation of a new MRI staging classification and imaging findings as indicators of surgical management: a retrospective study of 37 patients. Arch Orthop Trauma Surg 130(1):111–118 28. Solomou E et  al (2004) Multiple spinal epidural abscesses extending to the whole spinal canal. Magn Reson Imaging 22(5):747–750 29. Ghosh PS et  al (2009) Holocord spinal epidural abscess. J Child Neurol 24(6):768–771 30. Ledermann HP et  al (2003) MR imaging findings in spinal infections: rules or myths? Radiology 228(2):506–514 31. Wagner SC et  al (2000) Can imaging findings help differentiate spinal neuropathic arthropathy from disk space infection? Initial experience. Radiology 214(3):693–699 32. Numaguchi Y et  al (1993) Spinal epidural abscess: evaluation with gadolinium-enhanced MR imaging. Radiographics 13(3):545–559; discussion 559–60. 33. Longo M et al (2003) Contrast-enhanced MR imaging with fat suppression in adult-onset septic spondylodiscitis. Eur Radiol 13(3):626–637 34. Doita M et al (2007) Septic arthritis of lumbar facet joints without predisposing infection. J Spinal Disord Tech 20(4):290–295 35. Smida M et al (2004) Septic arthritis of a lumbar facet joint case report and review of the literature. Acta Orthop Belg 70(3):290–294 36. Mellado JM et al (2004) MR imaging of spinal infection: atypical features, interpretive pitfalls and potential mimickers. Eur Radiol 14(11):1980–1989 37. Muffoletto AJ et  al (2001) Hematogenous pyo genic facet joint infection. Spine (Phila Pa 1976) 26(14):1570–1576 38. Coscia MF, Trammell TR (2002) Pyogenic lumbar facet joint arthritis with intradural extension: a case report. J Spinal Disord Tech 15(6):526–528 39. Velissaris D et al (2009) Spinal subdural staphylococcus aureus abscess: case report and review of the literature. World J Emerg Surg 4:31 40. Reihsaus E, Waldbaur H, Seeling W (2000) Spinal epidural abscess: a meta-analysis of 915 patients. Neurosurg Rev 23(4):175–204; discussion 205. 41. Dorflinger-Hejlek E et al (2010) Diffusion-weighted MR imaging of intramedullary spinal cord abscess. AJNR Am J Neuroradiol 31(9):1651–1652 42. Berbari EF et  al (2015) 2015 Infectious Diseases Society of America (IDSA) clinical practice guide-

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lines for the diagnosis and treatment of native vertebral osteomyelitis in adults. Clin Infect Dis 61(6):e26–e46 43. Marschall J et al (2011) The impact of prebiopsy antibiotics on pathogen recovery in hematogenous vertebral osteomyelitis. Clin Infect Dis 52(7):867–872 44. Harada Y, Tokuda O, Matsunaga N (2008) Magnetic resonance imaging characteristics of tuberculous spondylitis vs. pyogenic spondylitis. Clin Imaging 32(4):303–309 45. Skaf GS et al (2010) Non-pyogenic infections of the spine. Int J Antimicrob Agents 36(2):99–105 46. Karaeminogullari O et  al (2007) Tuberculosis of the lumbar spine: outcomes after combined treatment of two-drug therapy and surgery. Orthopedics 30(1):55–59 47. Zhang HQ et  al (2007) One-stage surgical management for multilevel tuberculous spondylitis of the upper thoracic region by anterior decompression, strut autografting, posterior instrumentation, and fusion. J Spinal Disord Tech 20(4):263–267 48. Oztekin O et  al (2010) Brucellar spondylodiscitis: magnetic resonance imaging features with conventional sequences and diffusion-weighted imaging. Radiol Med 115(5):794–803 49. Al-Nakshabandi NA (2012) The spectrum of imaging findings of brucellosis: a pictorial essay. Can Assoc Radiol J 63(1):5–11 50. Chia SL et  al (2005) Candida spondylodiscitis and epidural abscess: management with shorter courses of anti-fungal therapy in combination with surgical debridement. J Infect 51(1):17–23 51. Aganovic L, Hoda RS, Rumboldt Z (2004) Hyperintensity of spinal Cryptococcus infection on diffusion-weighted MR images. AJR Am J Roentgenol 183(4):1176–1177

801 52. Williams RL et al (1999) Fungal spinal osteomyelitis in the immunocompromised patient: MR findings in three cases. AJNR Am J Neuroradiol 20(3):381–385 53. Akhan O et al (1991) Spinal intradural hydatid cyst in a child. Br J Radiol 64(761):465–466 54. Andre JB, Bammer R (2010) Advanced diffusion-­ weighted magnetic resonance imaging techniques of the human spinal cord. Top Magn Reson Imaging 21(6):367–378 55. Hori M et al (2006) Line scan diffusion tensor MRI at low magnetic field strength: feasibility study of cervical spondylotic myelopathy in an early clinical stage. J Magn Reson Imaging 23(2):183–188 56. Raya JG et  al (2006) Methods and applications of diffusion imaging of vertebral bone marrow. J Magn Reson Imaging 24(6):1207–1220 57. Le Bihan D et  al (1986) MR imaging of intra voxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161(2):401–407 58. Oto A et  al (2011) Diffusion-weighted MR imaging of abdominopelvic abscesses. Emerg Radiol 18(6):515–524 59. Castillo M et al (2000) Diffusion-weighted MR imaging offers no advantage over routine noncontrast MR imaging in the detection of vertebral metastases. AJNR Am J Neuroradiol 21(5):948–953 60. Pui MH et  al (2005) Diffusion-weighted magnetic resonance imaging of spinal infection and malignancy. J Neuroimaging 15(2):164–170 61. Herneth AM et al (2005) Diffusion weighted imaging of bone marrow pathologies. Eur J Radiol 55(1):74–83 62. Padhani AR et  al (2013) Assessing the relation between bone marrow signal intensity and apparent diffusion coefficient in diffusion-weighted MRI. AJR Am J Roentgenol 200(1):163–170

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Primary and Metastatic Spine Tumors

22

Patrick W. Hitchon, Shotaro Naganawa, John Kim, Royce W. Woodroffe, Logan C. Helland, Mark C. Smith, and Toshio Moritani

22.1 Introduction The presenting symptoms of night pain, pain at rest, or progressive neurologic deficit should prompt the clinician to entertain the diagnosis of primary or metastatic tumors of the spine. The primary complaint of most patients with spine tumors is pain, without an apparent difference between benign or malignant tumors. With the increased availability of MRI, the number of patients presenting with neurologic deficits will P. W. Hitchon · R. W. Woodroffe Department of Neurosurgery, University of Iowa Carver College of Medicine, Iowa City, IA, USA e-mail: [email protected]; royce-­[email protected] S. Naganawa Department of Radiology, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected] J. Kim (*) · T. Moritani Division of Neuroradiology, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected]; [email protected] L. C. Helland Department of Neurosurgery, University of Iowa Hospitals & Clinics, Iowa City, IA, USA e-mail: [email protected] M. C. Smith Department of Radiation Oncology, University of Iowa Carver College of Medicine, Iowa City, IA, USA e-mail: [email protected]

most likely decrease secondary to earlier diagnosis. Often, plain radiographs demonstrate the site of the lesion and in many cases can be diagnostic. However, a significant amount of destruction is necessary before plain radiographs show evidence of disease [1]. Hence, computed tomography (CT) and MRI are valuable for defining the extent and precise location of the lesion. The need for myelography to diagnose and define benign tumors of the spine has been nearly eliminated by these two imaging advances. In the case of metastatic tumors, a CT of the chest, abdomen, and pelvis or positron emission tomography scan can help to identify any additional distant pathology. Ultimately, however, a tissue diagnosis by image-guided biopsy is required to aid in preoperative planning. Angiography can be an ancillary test in diagnosis, but mostly for preoperative embolization to decrease intraoperative blood loss and therefore the postoperative morbidity. Aneurysmal bone cysts, giant cell tumors, hemangiomas, and renal cell metastases should be considered for preoperative embolization.

22.2 DWI of Spinal Tumors

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In this chapter, we address the utility of diffusion-­ weighted imaging (DWI) along with corresponding apparent diffusion coefficient (ADC) maps in addition to conventional MR sequences for

© Springer Nature Switzerland AG 2021 T. Moritani, A. A. Capizzano (eds.), Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine, https://doi.org/10.1007/978-3-030-62120-9_22

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e­ valuation of spinal lesions by assessing individual cases across a wide range of pathologies.

percent of patients who die from cancer have evidence of vertebral metastases visible on postmortem examination [1]. Even though only 10% of metastatic tumors will prove unstable requiring 22.2.1 Technique stabilization, accuracy in diagnosis and appropriate management are paramount. We routinely use DWI in spine protocols with The frequency of metastatic disease to the b-value of 1000  sec/mm2, spin-echo type echo-­ spine does vary with the type of the primary canplanar imaging, and a parallel imaging factor of 2 cer. Prostate cancer is the most common cancer or 3. The ADC maps are calculated from b values associated with spinal metastases. This is folof 0 and 1000  sec/mm2. Lower b-values (e.g., lowed in frequency by breast, melanoma, lung, 0–500) are not adequate for diffusion-weighted thyroid, and renal, lymphoma, myeloma, and imaging due to influence by both slow perfusion pancreatic cancers [1, 4, 5]. However, some cen(intravoxel incoherent motion) and T2 effect. A ters have developed areas of specialization dealDWI image with b = 500 shows partially hyperin- ing with one type of cancer over others. At the tense normal CSF in the spinal canal, whereas University of Iowa (UIHC), specialized care in this effect is absent in the b = 1000 image. neuroendocrine tumors has resulted in a significantly larger proportion of such tumors in our practice.

22.2.2 DWI Signal and ADC of the Spine

DW signal of the vertebral body is variable depending on the proportion between red and yellow bone marrow. The fat content of bone marrow causes a DWI signal void because DWI innately uses chemical fat saturation. Moreover, the bony trabeculae also cause signal loss due to diamagnetic susceptibility effects. The resulting calculated ADC values are thus affected by both chemical shift and susceptibility artifacts. DW signal and ADC values of the disc also vary secondary to degree of degenerative changes present.

22.3 Metastatic Tumors to the Spine By far and away, the most common adult spine tumors are metastatic, with more than 90% of spinal tumors being metastatic [2, 3]. Most common cancers affecting the spine include breast, lung, prostate, and kidney [4]. The American Cancer Society predicts that in 2019 there will be an estimated 1,762,450 new cases of cancer in the USA, with 606,900 deaths. Thirty to seventy

22.3.1 Imaging As stated above, plain films are often inadequate except in advanced cases. On CT scan, the majority of metastatic tumors to the spine are lytic (Figs. 22.1, 22.2, 22.3, and 22.4). Bone scans can reveal the multiplicity of lesions, favoring metastatic disease rather than primary bone tumors. On MRI, expansion and compression of the spinal canal is better appreciated, with enhancement of the cancerous tissue. The enhancement reflects tumor extension into the canal as well as the paraspinal space (Fig. 22.5). The diagnosis can sometimes be surmised in the presence of other lesions noted in the kidney or lung. Prostate metastases can be mixed with lytic and sclerotic lesions (Fig. 22.6). The sclerotic or blastic appearance on CT associated with increased activity on bone scan is diagnostic of prostate metastases. Metastatic tumors generally show DWI hyperintensity with reduced ADC values (Figs.  22.7, 22.8, 22.9, and 22.10). The ADC values depend on a combination of factors including the hypercellularity of the primary tumor, the degree of necrosis, and the makeup of the extracellular tumor matrix. Since DWI uses

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Fig. 22.1 (a–c) CT sagittal and axial (a and b) reveals a lytic lesion involving T6 vertebral body and pedicles bilaterally. Abdominal CT (c, arrow) also shows a mass involving the right kidney compatible with renal cell carcinoma

(Figs.  22.1, 22.2, 22.3, and 22.4 pertain to the same patient. A middle-aged gentleman presents with a 2-month history of right interscapular pain without a history of weight loss or malignancy)

fat saturation and free water saturation techniques with a larger FOV than conventional MRI, the detection of ­primary tumors and other metastatic foci are often enhanced. Recently, whole-body DWI technique is used to detect primary and metastatic lesions [6]. A few studies show DWI can be of high diagnostic accuracy in detecting metastases, with comparable results to PET/CT [7, 8].

motherapy is obviously the treatment of choice [5]. Tumors that are moderately sensitive include breast, prostate, and thyroid. However, in the presence of neurological deficit interfering with mobility or pain unresponsive to radiation and chemotherapy, and in the absence of medical contraindications, surgery is indicated. Surgery is also recommended in cases of instability with kyphosis or subluxation. Less often, surgery can be appropriate where the diagnosis has been elusive in spite of multiple attempts at biopsy. There are extant guidelines for surgery in metastatic spinal disease, such as the Spinal Instability Neoplastic Score (SINS) [5, 9, 10]. As is often the case, the indications for surgery are often selected after multidis-

22.3.2 Management In cases of pain from lymphoma, myeloma, or small cell lung cancer, radiation therapy with che-

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22.4 Primary Spine Tumors The American Cancer Society estimates approximately 3,450 new cases of bone cancer will be diagnosed in 2018 [11]. The incidence of primary bony spinal neoplasms has been estimated at 2.5 to 8.5 per 100,000 people per year [12]. As a general rule, two important clinical features need to be considered in evaluating the potential malignancy of a lesion in the spine: age and location. More than 75% of lesions located in the vertebral body are malignant, compared to one-third of lesions in the posterior elements. Second, more than two-thirds of all lesions seen in children younger than age 18 are benign, whereas this figure is reversed in adults. In a recent review at the Mayo Clinic, of nearly 10,000 skeletal tumors distributed throughout the skeleton, 2,900 were benign, and 7,100 were malignant [13]. A further analysis of this group revealed that 1,100 tumors involving the spine were malignant and only 274 were benign.

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Fig. 22.2 (a, b) Contrast-enhanced T1-weighted sagittal (a) and axial (b) MRI shows a mass involving T6 with loss in height and extension of tumor into the spinal canal and paraspinal space 154 155 156 157

ciplinary discussions between the patient and the treating team. There are exceptions to every guideline, and it is the patient who bears the consequence of an ill-­advised operation.

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22.5 Benign Tumors

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22.5.1 Giant Cell Tumor

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Giant cell tumors of the spine are locally aggressive, benign primary bone tumors that constitute about a third of benign spinal tumors [13]. The mean age of involvement is approximately 30 years, with a range of 13 to 62 years [14]. The incidence in women appears to outnumber that in men by a ratio of 2:1 [15]. Most patients present with pain localized to the site of the lesion, and occasionally with a neurologic deficit, depending on the location. Giant cell tumors primarily occur in the vertebral body, but may extend into the posterior elements [2]. Malignant transformation occurs in approximately 10% of cases. Some giant cell tumors are biologically aggressive lesions. In patients with these tumors, local recurrence is common after incomplete resection.

22.5.1.1 Imaging The diagnosis of giant cell tumors may be made from MRI and CT scans (Figs. 22.11 and 22.12).

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Fig. 22.3 (a, b) For the reduction of intraoperative blood loss in vascular tumors such as renal cell carcinoma, embolization with Onyx glue (ethylene-vinyl alcohol

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Fig. 22.4 (a, b) On account of the symptoms and unresponsive nature of renal cell cancer, the patient underwent corpectomy of T6, decompression of the spinal canal, and stabilization. Postoperative plain films in the anteroposte-

copolymer) was undertaken through T7 and T8 segmental arteries. Images before (a) and after (b) embolization are shown

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rior (a) and lateral projection (b) are presented. Postoperative sagittal T2-weighted MRI (c) shows adequate decompression when compared to the preoperative images in Fig. 22.2

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Fig. 22.5 (a–d) A middle-aged gentleman presents with 2-month history of worsening back pain and 20-pound weight loss. Endorses knees giving out while walking and numbness in legs for the past 2 weeks. Pathology was that of neuroendocrine tumor. CT scan sagittal (a) and axial (b) shows a large mass involving T11 with erosion of the left pedicle and bilateral lamina as well as the spinous pro-

cess. There is some erosion of the posterior elements of T12 as well. Sagittal (c) and axial (d) enhanced MRI shows a large extradural lesion within the left pedicle and posterior elements of T11 resulting in severe cord compression. Multiplicity of spinal lesions seen on other images suggests metastatic disease

The lesion is characterized as a destructive, expansile enhancing mass that may contain blood degradation products. Pathologically, giant cell tumors consist of multinucleated osteoclastic

giant cells and spindle cell stroma, occasionally with secondary “aneurysmal bone cystlike” change [16]. This pathologic feature of hypercellularity corresponds to lower ADC values [17].

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Fig. 22.6 (a–f) A gentleman with a 2-year history of prostate cancer presents with leg weakness, mild back pain, and the need to use a cane on one side and a crutch on the other for the past couple of months. Technetium-99 m methylene diphosphonate (Tc-99  m MDP) bone scan shows multiple foci of abnormal uptake consistent with multiple osseous metastases. (a, b) Sagittal T2 (c), sagittal T1 contrast-enhanced (d) and axial T1 contrast-enhanced

(e) MRI shows interval increase in marrow replacement, most prominently at L3, with significant enhancement consistent with spread of metastatic disease. Interval reduction of vertebral body height at this level is suggestive of new pathologic fracture. Sagittal (f) CT scan shows pathologic compression fractures in the lumbar spine with lytic and sclerotic features characteristic of metastatic prostate cancer

22.5.1.2 Management Giant cell tumors are large expansile masses that frequently result in the destruction of the vertebral body. The most acceptable treatment approach seems to be an attempted wide resection [18–20]. However, due to the vascular nature of these tumors, preoperative embolization is frequently performed [2]. Radiation therapy may be considered with subtotal surgical excision [21]. Giant cell tumors can recur relatively early after even the most radical surgical excision. In such cases, it may be reasonable to consider radiation

therapy if it is believed that further excisional attempts will also be unsuccessful. The 10-year success rate of radiation alone was 69%, compared to 83% for postoperative radiation therapy [22]. Unfortunately, progression to a high-grade sarcoma occurs in 5–15% of cases following radiation treatment [22]. Therefore, radiation therapy should be reserved for patients with recurrent or residual disease after attempted resection [20]. In the spine, wide resection may cause destabilization and may necessitate instrumentation of the spine and fusion (Fig. 22.13).

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Fig. 22.7 (a–d) 64-year-old male with non-small cell lung carcinoma. Axial T2 (a), axial T1 contrast-enhanced (b), DWI (c), and ADC map (d) clearly shows vertebral, right rib, and mediastinal lymph node metastases

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Fig. 22.8 (a–f) 57-year-old male with renal cell carcinoma. Bull’s eye or halo pattern seen on T2 (a) contrast-­ enhanced T1 (b and c), DWI (d), and ADC (e) is due to necrotic tumor center, which is reported as characteristic

for metastasis [98]. ADC value in the necrotic center is 1.52  ×  10−3  mm2/s, significantly higher than solid outer ring, 0.65  ×  10−3  mm2/s. In addition, DWI reveals right renal tumor and renal vein tumor thrombus (f)

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Fig. 22.9 (a–h) 25-year-old male with Primary Ewing Sarcoma in the radius (a). T1 (b) and contrast-enhanced T1 (c) shows multiple enhancing lesions. DWI (d) demonstrates homogeneous hyperintensity due to hypercellularity, indicating bone metastases. ADC map (e) shows

low ADC values in the vertebral body (0.48– 0.61  ×  10−3  mm2/s) as markedly hypercellular tumor replaces the normal fatty bone marrow as seen on histology (f–h)

22.5.2 Hemangioma

local pain, but spinal surgeons typically become involved with the treatment of hemangioma when the lesion causes spinal cord or nerve root compression, or when a pathologic fracture occurs.

Hemangioma is one of the common benign lesions involving the spinal axis. It is often discovered incidentally during evaluation of patients with back or neck pain. Of the 275 benign spinal tumors described by Unni et  al. [13], the incidence of hemangioma was 15%, with less than 5% of patients with hemangiomas developing symptoms [12]. Vertebral hemangiomas most commonly occur in the thoracic and lumbar spine and can occur in the body, pedicle, or lamina [2]. These lesions can cause

22.5.2.1 Imaging Plain radiography and CT can clearly demonstrate the typical coarsened trabeculae within the involved vertebrae, with a characteristic honeycombed appearance. Gadolinium enhancement and MRI evidence of a soft tissue component are often observed (Fig. 22.14).

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Fig. 22.10 (a–g) 52-year-old male with Glioblastoma as a primary tumor in the left frontoparietal lobes. Sagittal (a) and axial (b) contrast-enhanced T1 shows osseous and mediastinal metastases. DWI (c) demonstrates hyperinten-

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sity in the bone metastases with reduced ADC (d) due to hypercellularity of glioblastoma (ADC: 0.64 × 10−3 mm2/s). Pathology of the primary GBM (e) and bone metastasis shows hypercellularity (f) and necrosis (g)

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Fig. 22.11 (a–f) T2-weighted (a), T1 (b), T1  +  Gd (c), DWI (d), and ACD map (e) magnetic resonance imaging scans reveal the bony tumor arising from L3 with extension anteriorly and posteriorly. DWI demonstrates mild hyperintensity in giant cell tumor (GCT) with reduced ADC due to hypercellularity (ADC: 0.84 × 10−3 mm2/s). Differentiation

f

from malignant tumor is difficult based on the ADC value. A needle biopsy revealed a giant cell rich hypercellular tumor (f) (Figs. 22.11, 22.12, and 22.13 pertain to the same patient: a young lady who has suffered with back, left hip, and left leg pain for the past 6 months. The intensity of the pain has increased with the passage of time)

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Fig. 22.12 (a, b) Axial (a) and sagittal (b) CT scans demonstrate the expansile intramedullary tumor with extension into the spinal canal

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Fig. 22.13 (a, b) Three years following lateral corpectomy and posterior instrumentation, AP (a) and lateral (b) radiographs show satisfactory alignment with hardware in place

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Fig. 22.14 (a–e) An elderly woman with paraparesis. MRI shows a thoracic hemangioma. T2 (a), T1 (b), and T1 + Gd (c) sequences show tumor involving body, pedicle, transverse process, and spinous process, with anterior

displacement of the cord. Sagittal CT scan (d) demonstrates the trabeculated appearance of the hemangioma. The tumor mass was resected with the implantation of instrumentation (e)

Spinal hemangiomas often have varying fatty component, and MR signal intensity may be suppressed on fat-suppressed images such as ­ DWI, depending on the amount of fat tissue. This signal loss should not be mistaken as low ADC

values. Typical hemangiomas often show hyperintense appearance on T1- and T2-weighted images (Fig. 22.15), but some (atypical) hemangiomas show hypointense signal on T1-weighted image (Fig. 22.16), which can mimic malignancy

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Fig. 22.15  61-year-old male. There is a well-defined round mass in the L1 vertebral body. Hyperintense appearance on T1- (a) and T2- (b) weighted images indicates

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Fig. 22.16  83-year-old female. In this case, tumor in the vertebral body shows hyperintensity on T2-weighted image (a), iso- to hypointensity on T1-weighted image (b) with inhomogeneous enhancement on contrastenhanced T1-weighted

typical hemangioma. DWI (c) demonstrates very low signal in fat-contained hemangioma with blackout on the ADC map (d) due to chemical fat saturation

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image (c), often seen with atypical hemangioma. DWI (d) demonstrates mild hyperintensity in atypical hemangiomas with increased ADC (e) (1.35 × 10 mm /s), which is significantly higher than in metastasis and lymphoma

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Fig. 22.17 (a–f) 62-year-old female with a history of breast cancer metastasis to the liver. She was found to have a growing lytic lesion in the T9 vertebral body on chest CT (a). The tumor demonstrated hyperintense signal on sagittal T2-weighted image (b) and hypointense signal

on T1-weighted images (c) with homogeneous enhancement on T1-weighted contrast-enhanced image (d), mimicking a new bone metastasis. DWI (e) also shows hyperintensity and low ADC value (0.7 × 10−3 mm2/s) (f) favoring metastatic disease

[23]. To differentiate atypical hemangiomas from malignancy, it has been reported that ADC values in metastatic lesions are lower than those in hemangiomas (Fig. 22.17) [24, 25].

At the UIHC, since 2005 we have evaluated 56 patients with vertebral hemangioma. Of these, only 7 have undergone surgery for decompression. As most of these lesions are chronic and extradural, complications have been nil. Vertebroplasty and kyphoplasty have also been advocated for the treatment of symptomatic hemangiomas. Studies have recently reported excellent pain relief with no evidence of post-treatment instability [27, 28]. This may be performed alone in patients who are not good surgical candidates, or as an adjunct to decompressive surgery [27, 28]. While radiotherapy is typically reserved for patients who undergo a subtotal resection, some advocate for its use as a standalone treatment [29, 30].

22.5.2.2 Management Surgical decompression is recommended if there is progressive neurologic decline. Because of the potential morbidity and relative lack of efficacy associated with radiation therapy, total lesion removal often should be attempted. Preoperative embolization may also be performed to reduce intraoperative blood loss [26]. When postoperative instability is suspected, instrumentation is implanted following excision (Fig. 22.14e).

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22.5.3 Eosinophilic Granuloma (Langerhans Cell Histiocytosis)

cent disc spaces are well preserved (Fig.  22.19 MRI). These findings differentiate eosinophilic granulomas from other lesions in the differential diagnosis, including infection, benign tumor, or malignancy.

Most commonly, eosinophilic granulomas present in children in the first two decades of life and are identified in the cervical spine, with the vertebral body typically being involved [31, 32]. While these lesions can occur in any bone, spinal lesions comprise 6.5% to 25% of cases [33]. Eosinophilic granulomas involving the adult spine are less frequent, but do occur [31, 34]. These lesions may be identified incidentally, but patients often present with localized neck or back pain with restricted motion [22, 31, 32]. These self-limiting, benign lesions cause osteolytic damage secondary to the local proliferation of histiocytes [35].

22.5.3.2 Management In many cases, symptoms will resolve over time, and a conservative approach should always be considered first. Bracing and non-steroidal anti-­ inflammatory drugs may provide symptomatic relief. In patients where the diagnosis is not clear, biopsy is recommended [36]. The vertebral body height can be restored spontaneously if the areas of endochondral ossification are preserved. Therefore, treatment is generally conservative with activity limitations and bracing. For symptomatic relief, vertebroplasty may occasionally be indicated [37]. In cases where bony destruc22.5.3.1 Imaging tion leads to instability that persists despite a Radiographically, eosinophilic granulomas are course of conservative management, arthrodesis identified as destructive bony lesions with well-­ is required [35]. For patients deemed to be poor demarcated borders and no evidence of a soft tis- surgical candidates with progressive, intractable sue mass (Fig. 22.18). Extensive bony destruction lesions, radiation therapy may be considered occasionally results in vertebra plana. The adja- [38].

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Fig. 22.18 (a–f) A young man complains of neck pain following a motor vehicle accident. The pain radiates into his left shoulder and sometimes into his head. His past medical history is unremarkable, and he continues to work as a cook. Sagittal (a), axial (b), and coronal (c) CT scans show a lytic process involving the C4 body, left pedicle,

d

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and lateral mass. The clear margins and sclerosis suggest a benign process such as eosinophilic granuloma. On follow-­up 4 years later, sagittal (d), axial (e), and coronal (f) CT show resolution of the lytic process without treatment, confirming the diagnosis of eosinophilic granuloma

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Fig. 22.19 (a–i) 24-year-old male with Langerhans cell histiocytosis (LCH). Sagittal T2 (a), T1 (b), and T1 contrast-­enhanced (c and d) images demonstrate a C7 vertebral body lesion. DWI (e) demonstrates hyperintensity in LCH with intermediate ADC (f) due to the hypercellu-

larity (ADC: 0.99  ×  10−3  mm2/s). Differentiation from malignant tumor is difficult based on the ADC value. Axial (g) and sagittal (h) CT demonstrate the lytic process. Pathology demonstrates intermediate cellularity of LCH (i)

22.5.4 Osteoid Osteoma and Osteoblastoma

by Boriani et al. [42], 16 of 30 lesions occurred in the lumbar spine, 8 in the thoracic spine, and 6 in the cervical spine. Two-thirds of these lesions are confined to the dorsal elements. These tumors are also associated with scoliosis, and patients may present with neurologic deficit. It is important to remember that both osteoid osteoma and ­osteoblastoma should be considered in any young patient with back or neck pain, painful scoliosis, or radicular pain.

Osteoid osteomas originate from cancellous bone and are a histologic continuum of osteoblastomas. Osteoid osteomas by definition are less than 2.0  cm in diameter, while osteoblastomas are lesions that are greater than 2.0  cm in diameter [39]. The most common presenting symptom is localized pain that is worse at night and relieved by salicylates or other non-steroidal anti-­ inflammatory drugs [40]. Osteoblastomas consti- 22.5.4.1 Imaging tute about 16% of benign primary spinal tumors These lesions often become symptomatic [13]. Patients are usually in their second or third before they are visible on plain radiographs. decade at presentation [41]. Osteoblastomas are MRI shows a heterogeneously enhancing mass distributed throughout the spine, and in the series arising from the posterior elements (Fig. 22.21).

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Fig. 22.20 (a–c) Sagittal T2 (a), T1 (b), and T1 + Gd (c) MRI show a 48 × 37 × 21 mm mass posterior to the cord at T10-T11. The mass contains fluid–fluid level cystic components and shows heterogeneous intense enhancement with pedicle and vertebral body involvement. The mass compresses and displaces the spinal cord anteriorly.

Based on these findings, primary bone tumor, such as aneurysmal bone cyst, or osteoblastoma was entertained (Figs. 22.20 and 22.21 pertain to the same patient: a young man who presents with numbness and weakness in his legs. He has been using a cane in the last couple of weeks. He also endorses problems with bladder emptying)

Osteoblastomas can progressively enlarge, and on CT show a well-defined, lobulated, lytic, expansile mass that usually involves the neural arch structures (Fig. 22.22).

of sarcomatous transformation. Radiofrequency ablation of both osteoid osteoma and osteoblastoma has been shown to be effective for initial procedural intervention as well as in cases of recurrence [48].

22.5.4.2 Management The initial management of osteoid osteoma should be conservative with observation and treatment with salicylates or other non-steroidal anti-inflammatory drugs [40]. Patients with intractable pain or scoliotic deformity are indicated for excision [43]. Wide en bloc excision appears to be curative, while intralesional resection has been shown to have a 7% recurrence rate [44]. Osteoblastomas should initially be treated surgically with total resection [45, 46]. Again, wide en bloc excision is preferred, as there is a high recurrence rate with intralesional resection, 24% in one series [47]. With larger, aggressive lesions, radiation therapy and embolization may be considered as adjuncts to surgical resection, but the potential risks of radiation therapy must be considered, including the risk

22.5.5 Osteochondroma Osteochondromas constitute about 9% of benign spinal tumors [13]. They consist of cartilage-­ covered cortical bone with underlying medullary bone. Histologically, the cartilaginous cap and underlying bone are identical to normal bone. Osteochondromas most commonly occur in the third decade, with a male/female predominance of 2:1. They tend to occur more commonly in the cervical spine and in the posterior elements [49, 50]. Patients usually have localized pain, although neurologic deficit, including cord or nerve root compression, can also occur [51]. Rarely, osteochondromas may also be a manifestation of hereditary multiple osteochondromas [52].

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Fig. 22.21 (a–c) Sagittal (a), axial (b), and coronal (c) CT scan reveals a well-circumscribed expansile lesion containing calcifications and a sclerotic rim arising from the right posterior elements. Findings are most consistent

with osteoblastoma or aneurysmal bone cyst. Patient underwent surgery with total excision of the mass, which was identified as osteoblastoma

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Fig. 22.22 (a–c) T1 sagittal MRI (a), T1 + Gd axial (b), and T2 sagittal (c) show a lobulated osteo-cartilaginous mass arising from the facet joint extending into the canal (Figs. 22.22, 22.23, and 22.24 pertain to the same patient.

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Additionally, radiation-induced osteochondroma has been reported in patients who have received radiation treatment [53].

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A middle-aged lady was referred to our clinic for evaluation of a spinal thoracic mass that was discovered incidentally during workup for hoarseness)

not a tumor per se, its classical appearance and presentation should be familiar to clinicians dealing with spine lesions. ABCs of the spine typically affect the pediatric population, with a small 22.5.5.1 Imaging female predominance [59, 60]. Approximately MRI of these lesions shows their lobulated, well-­ 40–45% involve the lumbar spine, with 30% circumscribed enhancing masses involving the involving the thoracic spine and 25–30% involvspinous and transverse processes (Fig.  22.22). ing the cervical spine [61]. In one series, 75% of CT scan confirms their well-circumscribed oste- lesions arose in the neural arch and 40% extended oid appearance (Fig. 22.23). into the body [59]. Lesions are often not confined to a single vertebra; instead, they bridge two or 22.5.5.2 Management more levels in approximately 40% of cases [61]. For symptomatic lesions, surgical excision is the Although primary aneurysmal bone cysts are of treatment of choice (Fig. 22.24) [54–56]. Wide sur- unknown cause, a secondary form of aneurysmal gical excision should be performed and should bone cyst has been described that arises within include the cartilaginous cap in order to prevent eosinophilic granulomas, simple bone cysts, recurrence [52]. Radiation therapy is not recom- osteosarcoma, chondroblastoma, or giant cell mended due to concern for transformation into sec- tumors [62]. ondary chondrosarcoma [57]. Given proper surgical Pain occurring especially at night and that is resection, recurrence is generally rare [58]. localized to the site of the mass is the most common presenting complaint [63]. The presence or absence of a neurologic deficit depends on the 22.5.6 Aneurysmal Bone Cysts site of the tumor and on the degree of compression of adjacent neural elements. The clinical Aneurysmal bone cysts (ABCs) are rare, benign, course is commonly progressive over several proliferative non-neoplastic lesions that may months because of the slow growth of these occur in any part of the skeleton. Although this is lesions, although rapid growth can also occur.

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Fig. 22.23 (a, b) Coronal (a) and axial (b) CT scans show the calcified mass emanating from the facet joint extending into the canal

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22.5.6.1 Imaging Diagnosis is often best achieved with MRI in demonstrating the epidural extent of the mass. The classical appearance of the involved vertebra is that of a multiloculated, expansile, highly vascular mass with eggshell-like cortical bone and blood product fluid–fluid levels (Fig.  22.25a–c). Computed tomography demonstrates the bony scalloping and thinning of adjacent cortices without invasion favoring the benign nature of the tumor (Fig. 22.25d, e). The heterogeneity of cystic component is reflected by variety of ADC values (Fig. 22.26). Selective spinal angiography has both diagnostic and, potentially, therapeutic value. Preoperative embolization is a useful adjunct that may decrease the intraoperative blood loss. 22.5.6.2 Management Because of the destructive nature of ABCs and the risk of progressive instability coupled with the frequent presence of neurologic deficits secondary to compression of neural elements, complete surgical resection is often considered the treatment of choice, either with or without preoperative embolization [61, 64–67]. However, other options such as embolization alone, radiation therapy, sclerotherapy, or treatment with denosumab have also been reported [68–71]. In patients without instability or neural compression, bisphosphonate therapy may also serve as a definitive treatment [72]. The approach (ventral, dorsal, or dorsolateral) depends on the exact location and extent of the lesion. An ­eggshell-­thin

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Fig. 22.24 (a, b) Intraoperative photographs show the mass removed from the canal (a) and the canal restored to its normal configuration (b) after removal of the mass. Pathology confirmed the diagnosis of osteochondroma

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will ultimately develop multiple myeloma, most commonly within 2 years [74]. Plasma cell tumors of the spine usually present with pain, and in advanced cases with myelopathy. The duration of symptoms before diagnosis may vary; however, in general, symptoms worsen considerably in the 6 to 12  months before presentation. The diagnosis of solitary plasmacytoma is established histologically by either a needle biopsy or an open procedure. In plasmacytoma, the bone marrow is negative for plasma cell infiltrates, and serum-protein electrophoresis results are normal. Multiple myeloma is usually diagnosed definitively by a bone marrow biopsy, the presence of multiple bony lesions on bone survey, and an abnormal monoclonal ­immunoglobulin spike on serum or urine electrophoresis [74]. Most patients with multiple myeloma present with Bence-Jones proteinuria, reflecting the spillover of monoclonal immunoglobulin fragments into the urine.

22.6.1 Plasma Cell Tumors

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f

cyst of subperiosteal new bone that is continuous with adjacent cortex is observed at surgery. With complete excision, the prognosis is excellent and recurrence rates are rare in most series [61, 73]. En bloc resection should be the goal of surgical intervention. As with all resections of spinal neoplasms, postoperative spinal instability should always be considered and instrumented fusion performed when identified (Fig. 22.25f).

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image. Sagittal (d) and axial (e) CT scan demonstrates the lytic lesion of the body, pedicle, and transverse process. Owing to symptoms and cord compression, the patient underwent resection with instrumentation as shown on the plain radiographs (f). Pathology was that of aneurysmal bone cyst

22.6 Malignant Spinal Tumors

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Fig. 22.25 (a–f) A young woman presents with severe back and leg pain on her left side. Sagittal T2 (a), T2 axial (b), and T1 + Gd (c) MRI scans show an enhancing lesion centered in the posterior elements of L1 vertebra with extension anteriorly involving the vertebral body, paraspinal soft tissue, and spinal canal with compression of the cord. A fluid/fluid level can be appreciated on the axial

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Plasma cell tumors of the spine (multiple myeloma and solitary plasmacytoma) are the most common type of malignant primary spinal tumors, accounting for nearly one-third [13]. Half of patients diagnosed with plasmacytoma

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Fig. 22.26 (a–f) The patient is a 24-year-old male with aneurysmal bone cyst (ABC). Sagittal T1 (a), sagittal T2 (b), axial T2 (c), axial T1 postcontrast (d), axial CT (e),

axial DWI (f), ADC (g). DWI demonstrates iso- to hypointensity with some hyperintense foci in ABC associated with increased ADC due to the cysts and hemorrhagic foci

22.6.1.1 Imaging MRI and CT scan show the ill-defined enhancing lesions often associated with vertebral body collapse and destruction (Figs.  22.27 and 22.28). Bone involvement of these tumors is demonstrated by hyperintense DWI and range of mildly reduced to slightly elevated ADC due to hypercellularity (Fig. 22.29).

Chemotherapy is generally withheld until progression to multiple myeloma is documented. Vertebroplasty can help relieve pain and improve quality of life in patients without neurological deficit [77]. Because plasmacytomas are highly radiosensitive, surgical decompression and fusion is generally reserved for patients with progressive neurologic deficit and deformity.

22.6.1.2 Management The treatment of choice for solitary plasmacytoma in the absence of instability or rapid paralysis is radiation therapy [74–76]. It is recommended that patients with a diagnosis of solitary plasmacytoma be followed closely for the development of indexes characteristic of multiple myeloma.

22.6.2 Chordoma Chordoma constitutes approximately a quarter of malignant tumors involving the spine [13]. The tumor originates from primitive remnants of the notochord. The vast majority of chordomas arise

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22  Primary and Metastatic Spine Tumors

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Fig. 22.27 (a–c) Sagittal T2 (a), T1 (b), and T1 + Gd (c) MRI now shows severe compression deformities of T5, T6, and L1. There is marrow replacement and displacement of the cord. Also noted is marrow replacement in the posterior elements of T8 (Figs. 22.27 and 22.28 pertain to

the same patient. An elderly lady with multiple myeloma and bone marrow transplant 20  years ago presents with new onset right leg weakness and numbness in both legs since yesterday. She is known to have compression fractures of L1 and T5

in either the skull base or sacrococcygeal areas; those involving the mobile intermediate regions of the spine are rare [78]. These tumors, considered to be of low-grade malignancy, are extremely difficult to resect because of their proximity to the spinal cord and cauda equina. In addition, in 5% to 10% of cases, they tend to metastasize within 1 to 10 years of the diagnosis. Fifty percent of all chordomas occur in the fifth to seventh decades of life, with a mean age of onset of approximately 50 years. Men are twice as likely to be afflicted with this tumor as women [78, 79]. Chordomas arise from the vertebral body with ensuing ventral and dorsal extension. Pain is the presenting symptom in 75% of cases and a radicular component in 10% of patients. More than two-thirds of patients present with weakness or other neurologic deficits. Mean duration of symptoms at presentation is 14 months, with a range of 4–24  months. In chordomas arising from the sacrum, 40% complain of rectal dysfunction including constipation, tenesmus, or bleeding hemorrhoids (Figs.  22.30, 22.31, and 22.32). A palpable tumor on rectal exam can be identified in most patients [80]. These tumors can also

affect two adjacent vertebral bodies while sparing the intervertebral disc. Though a pseudocapsule separates the tumor from adjacent soft tissue, it is diffusely invasive within adjacent bone without clear margins [80].

22.6.2.1 Management Percutaneous CT-guided biopsy is generally recommended to establish a definitive working diagnosis. The optimal treatment of chordomas is wide en bloc resection [81, 82]. The location of these lesions (close to neural structures) renders cure extremely unlikely. Some authors recommend a staged approach, where first a posterior approach is performed to mobilize the posterior elements, free the dura from the tumor pseudocapsule, and place posterior instrumentation, followed by an anterior approach to perform an en bloc vertebrectomy [80]. With involvement of the sacrococcygeal spine, resection can be accomplished by either ventral, dorsal, or combined approaches [83, 84]. These approaches are geared for the widest possible resection of tumor with sparing of as many nerves as possible [81]. Generally, sparing the S3 nerve root on either

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Fig. 22.28 (a, b) Sagittal (a) and axial (b) CT scan shows pathologic compression deformity at T5 with osteolysis extending into the posterior elements. Canal narrowing at this level is better appreciated on recent MRI.  There is also osteolysis within the posterior elements of T8. Patient was treated with radiation therapy and steroids with recovery of function without surgery

side may be sufficient for bladder and fecal continence. Loss of sexual function also needs to be discussed with the patient preoperatively. Approximately 30% of spinal chordomas will develop metastases [79]. Chordomas are not generally radiosensitive. However, because of the high risk of recurrence—47% of patients will have either recurrence or progression [85]—postoperative radiation therapy is often recommended. The 40-year experience at MD Anderson from 1954 until 1994 yielded 27 patients [86]. The median Kaplan-Meier survival for the entire group was 7.4  years. The disease-free interval for patients undergoing radical resection was 2.3 years, compared to 8  months in those undergoing subtotal excision (p 3 VB • MRI brain nondiagnostic for MS .

•  AQP4 antibodies

NMOd Sarcoidosis • Optic neuropathy • Multiple organs affected. plus transverse myelitis

• Acute phase: restricted diffusion in an inverted V pattern. • Hyperintense T2 in an inverted V pattern.

• Posterolateral and dorsal aspects. •  Inverted V. •  B12 deficiency

SCDe • Decreased vibration sense, pyramidal signs and peripheral neuropathy.

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therapy is considered first-line therapy followed by intravenous immunoglobulin and plasma exchange. The cord lesion of ADEM is characterized by abnormal high T2 signal that is long, spanning three to four vertebral body levels. They have been classically described as vertical or flame-­ shaped lesions and may occur at any level of the spinal cord with no specific preference. On transverse views, lesions are typically centrally located overlapping with transverse myelitis and cord infarct. ADEM usually shows increased diffusion with hyperintense signal on DWI and increased ADC values (average of 1.35 × 10−3 mm2/s) [34] in the subacute to chronic phases. In the acute setting, restricted diffusion may be seen with reported ADC values ranging from 0.37– 0.68  ×  10−3  mm2/s [35]. Often these lesions are accompanied by vasogenic edema (increased ADC) representative of acute inflammation [36].

23.4.3 Multiple Sclerosis c

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Multiple sclerosis is a chronic demyelinating autoimmune disease that results from immune attack to myelin components. Under the microscope, an active plaque shows defined collections of foamy macrophages with preservation of the neurons. Conversely, an inactive plaque shows changes of gliosis. Clinicians look for a history of at least two separate episodes in which the patient suffered central nervous system deficits Fig. 23.4 (a–d) Recurrent idiopathic transverse myelitis that have at least partially resolved. In the acute in a 62-year-old female with bilateral lower extremity weakness. Sagittal T2-weighted (a) and gadolinium-­ phase, intravenous glucocorticoids are recomenhanced T1-weighted (b) images show increased cord mended for treatment. signal spanning four vertebral bodies with enhancement at Typically, cord lesions in multiple sclerosis T4 and T5). The increased ADC (1.36 × 10−3 mm2/s) (c) present in two varieties: (1) focal lesions which suggests a subacute to chronic timeline. Follow-up sagittal gadolinium-enhanced T1-weighted image (d) shows are well delineated with high T2 signal intensity interval decreased enhancement at T4 and T5 and new or (2) poorly demarcated areas with slightly enhancement at T6/7 (). I do not see the arrow increased T2 signal intensity when compared to CSF.  These lesions are usually peripherally symptoms are usually preceded by a prodrome located (most commonly posterolateral), pre(71–74%) of febrile illness for up to 4 weeks. dominantly in the cervical spinal cord, and span Patients may present with a prodrome of fever, less than two vertebral body segments on sagittal headache, and vomiting that progresses to focal views [37, 38] (Fig. 23.5). DWI is typically hyperintense in subacute to or multifocal neurologic deficits and encephachronic lesions, similar to other inflammatory lopathy [31–33]. Intravenous glucocorticoid

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Fig. 23.5 (a–c) Multiple sclerosis in a 30-year-old male with resolving right-sided lower and upper extremity weakness but new generalized numbness. T2-weighted sagittal image (a) of the cervicothoracic spine shows multiple high signal plaques spanning approximately one vertebral body which enhance on the post-gadolinium

sequence (not shown). Axial T2-weighted (b) image shows the corresponding lesions are located in the posterior and lateral white matter columns. These lesions have a round/ovoid morphology. There is high diffusion signal (c) and ADC (not shown) is slightly low to isointense (0.85–0.93 × 10−3 mm2/s)

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etiologies, and may likely be bright due to T2 shine through. Increased ADC values have been reported in a range from 1.06 to 1.82 × 10−3 mm2/s with a mean of 1.36  ×  10−3  mm2/s, while the mean for healthy controls was 0.93 × 10−3 mm2/s [34, 37, 39]. These higher ADC values are to be expected in subacute to chronic lesions given that components such as myelin sheaths, axons, and cellular membranes are damaged in MS [40]. Acute plaques may show restricted diffusion owing to cytotoxic edema [41].

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23.4.4 Neuromyelitis Optica (NMO)

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values) in patients suffering from NMO versus those with MS, with average diffusion values of 1.40 × 10−3 mm2/s for NMO and 1.24 × 10−3 mm2/s for MS. They hypothesized that this may be due to decreased ability of NMO patients to recover from the severe demyelination and gray/white matter necrosis that develops leading to increased water diffusion [44].

23.4.5 Sarcoidosis

The pathogenesis of sarcoidosis is not well understood but may be pathologically described as a non-caseating granulomatous disorder likely initiNMO involves both optic neuropathy (unilateral ated by CD4  +  T cells, which affects multiple or bilateral) and transverse myelitis. Serum IgG organs with no clearly implicated etiologic agent autoantibodies against the water channel or genetic locus. Neurosarcoidosis is most comaquaporin-­4(AQP4-Ab), are primarily found in monly associated with granulomatous infiltrates astrocytes and mediate inflammatory attack tar- involving the leptomeninges, dura, hypothalamus, gets. Positive AQP4-Ab antibody is thought to be pituitary gland, and cranial nerves and occurs in pathogenic [40, 41]. However, some patients are 5% to 16% of sarcoidosis patients [45]. Spinal seronegative for AQP4-Abs, which makes diag- cord neurosarcoidosis is rare with an incidence at nosis more difficult. Some AQP4-Ab seronega- 0.43% to 1% of all sarcoidosis patients. Spinal tive patients have antibodies against cord neurosarcoidosis is thought to be at high risk myelin-oligodendrocyte glycoprotein (MOG-­ for severe neurological sequelae without prompt Abs), which may have different pathogenic diagnosis and management. Spinal cord neurosarmechanisms [42]. Limited forms of the disease coidosis is relatively refractory to corticosteroids are known as NMO spectrum disorder (NMOSD). and frequently requires c­ orticosteroids plus addiPathologic findings typically show demyelin- tional immunosuppressant therapy including ation with progression to necrosis. Optic neurop- infliximab, cyclophosphamide, methotrexate, athy and transverse myelitis may occur azathioprine, cyclosporine, leflunomide, mycosimultaneously but may also present at different phenolate mofetil, or adalimumab. times. NMO lesions commonly spare the brain in As many as 81% of spinal sarcoidosis the early stages. Oral prednisone may be insti- patients present with intramedullary fusiform tuted for optic neuritis. cord enlargement and almost always shows Winterchuk et  al. used a number of varying enhancement in a nodular and peripheral fashdiagnostic criteria for NMO. They found the fol- ion (96%) [45] (Fig.  23.7). In addition to the lowing diagnostic criteria to have the highest peripheral enhancement, the radiologist should combined sensitivity and specificity (99% and also look for leptomeningeal enhancement, 90%, respectively): (1) a contiguous spinal cord which is seen in approximately half of the cases. lesion spanning three or more vertebral body These enhancing lesions may disappear with lengths (Fig.  23.6) and (2) an initial brain MRI steroid treatment, but do not correlate well with nondiagnostic for MS along with (3) positive clinical response. Sarcoidosis is most comAQP4 antibody titers [43]. Benedetti et al. found monly found in the thoracic spine, followed by statistically significant increased diffusion (ADC the cervical spine. The majority of cases span

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Fig. 23.6 (a–c) Neuromyelitis optica. Sagittal (a) and axial (b) T2-weighted images show multiple longitudinal heterogeneous high signals spanning more than 3 segments with central spinal cord distribution. There is brain

stem involvement. These lesions show minimal enhancement. There is high diffusion signal with slightly increased ADC (c) (1.10 × 10−3 mm2/s)

more than three vertebral body segments, which helps differentiate sarcoidosis from MS.  There has been limited evaluation of intramedullary sarcoidosis using diffusion. Of the limited data available, the ADC values appear to vary widely ranging from 0.77  ×  10−3  mm2/s to 2.32 × 10−3 mm2/s [30].

23.4.6 Subacute Combined Degeneration (SCD) Although most accurately described as a demyelinating disease [46], SCD is placed under inflammatory etiology due to its many similarities. SCD can be caused by deficiencies in cobalamin (vita-

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Fig. 23.7 (a–d) Sarcoidosis in a 70-year-old female with double vision, left 6th nerve palsy, and bladder and bowel incontinence. Sagittal (a) gadolinium-enhanced T1-weighted image shows moderate cauda equina

enhancement. T2-weighted axial image (b) shows high signal within the cord associated with increased ADC (c) (1.29  ×  10−3  mm2/s). Chest CT (d) demonstrates hilar granulomatous calcifications

min B12), nitrous oxide susceptibility, and copper deficiency. In the acute phase, SCD manifests pathologically as myelin sheath swelling. In the late stage, there is typically a combination of myelin sheath and axonal degeneration leading to Wallerian degeneration. Given that SCD is easily treated by vitamin B12 administration, it is imperative that radiologists are able to diagnose it. SCD is a well-known clinical syndrome characterized by symmetric diminished vibration sense, pyramidal signs, and peripheral neuropathy caused by “combined degeneration” of the posterior columns and/or lateral corticospinal tracts. The classic MR finding of SCD is hyperintense T2 signal in the posterolateral and dorsal columns of the spinal cord, which has been described as an inverted V.  Most cases do not show any enhancement [47–49]. Those cases that do enhance have been hypothesized to be an end

stage lesion with breakdown of the blood-cord barrier. Three case reports showed restricted diffusion in the acute phase, which suggests cytotoxic edema of the myelin sheath [46, 50, 51]. DWI and ADC are useful in diagnosing and evaluating SCD (Fig. 23.8).

23.5 Infectious Etiologies (Table 23.3) 23.5.1 Infectious Myelitis Infectious myelitis can be further divided into bacterial, fungal, parasitic, and viral causes, with the latter being the most common. Examples of viral myelitis include polio, herpes, varicella zoster, influenza, and West Nile. Pathologies for each will vary depending on the organism. West Nile virus (WNV) typically targets gray matter of

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Fig. 23.8 (a–d) Symmetric T2 signals seen in the lateral and posterior columns are characteristic findings in subacute combined degeneration secondary to vitamin B12 deficiency (a, b). The demyelinating process selectively

involves the large diameter fibers with thickened myelin sheath causing focal swelling of the myelin sheath without inflammatory reaction. DWI (c) can show restricted diffusion with low ADC (d) (0.81 × 10−3 mm2/s)

the spinal cord producing lower motor neuron disease. On the milder end of the spectrum, patients may suffer from fever, headache, and/or generalized body aches. On the more severe end of the spectrum, patients will progress to meningoencephalitis (severe headache, fever, stupor, and muscle weakness), with risk of severe infection and death. There are several spinal pathologies that may span multiple vertebral body segments. WNV myelitis is one of them and can easily span more than four vertebral body segments on sagittal T2-weighted images (Fig. 23.9). WNV may present with discrete foci of hyperintense T2 signal and therefore mimic transverse myelitis (clinical

history and lab values can be key in making the differentiation). On contrast-enhanced images, leptomeningeal enhancement may be seen (possibly long segment). There is often thickening and enhancement of the cauda equina as well. Enterovirus 71 (EV-71) is a major cause of hand-foot-mouth disease (HFMD) and has been associated with rhombencephalitis, acute flaccid paralysis, and myelitis. Characteristic MR imaging findings from EV-71 include non-enhancing dorsal brainstem and long segment anterior horn cell predominant spinal cord T2 hyperintensities. Enhancement of the ventral cauda equine nerve roots has been described [52]. Recent research suggests that there is retrograde axonal transport

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Clinical picture AU7

Intramedullary distribution Etiology

Key MRI findings

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Compressive myelopathy • Progressive myelopathy Infectious variable

Spinal cord contusion • Spinal cord bruise • Cervical spine • Young: High •  Mass effect velocity leading -> trauma. decreased perfusion-> infarct •  Old: Fall.

• Variable contrast enhancement. •  ADC: Increased

• Acute: Restricted. • Chronic: Hyperintense T2/STIR

of the EV-71 via the pharyngeal branch of the vagus nerve, hence the brainstem and upper cord involvement [53]. There is little information in the current literature regarding diffusion-weighted imaging of the spinal cord in EV-71 infections. The authors recently encountered a case of EV-71-positive 6-month-old child who presented with fever, vomiting, oropharyngeal/tracheal secretions, acute on chronic respiratory failure with hypoxemia. The imaging findings included anteromedial thalamic, dorsal brainstem and anterior horn cell predominant spinal cord T2 hyperintensities and restricted diffusion spanning multiple vertebral body levels (Fig. 23.10).

23.5.2 Intramedullary Abscess The mechanism of infection is different between children and adults. In children, spinal dysraphism or neural tube defect such as a dermal sinus tract (tract from the skin surface extending towards the spinal canal) is often present allowing for direct spread of infection. In adults, it is commonly idiopathic. However identifiable

t3.1

West Nile virus •  Meningoencephalitis • Can be focal but can also span >4 vertebral bodies. •  Infectious myelitis

• Focal or long segment hyperintense T2 +/− leptomeningeal enhancement

Intramedullary abscess •  Pain

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• Young: Spinal dysraphism or neural tube defect. • Adults: hematogenous or direct spread. • Well formed: Diffusion restriction. • Follows evolution of intracerebral abscess • Variable restriction.

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causes include hematogenous spread or direct spread from spinal infection. Intramedullary abscesses of the spinal cord are very rare. Iwasaki et al. found only 54 cases reported in the world literature between 1977 and 2009. Clinical symptoms often resemble those of a structural cord lesion rather than infection. An intramedullary abscess arising from within the spinal cord (Fig.  23.11) should be distinguished from epidural or subdural abscesses. The evolution of an intramedullary abscess seems to roughly correspond to that of an intracerebral abscess [54, 55]. T1-weighted images may show an ill-defined low signal expansile lesion within the cord with associated high T2 signal representing local edema. With evolution of the abscess, well-defined ring enhancement should be seen. Well-formed abscesses show restriction of diffusion centrally. However, the degree of diffusion restriction is variable depending on the amount of internal inflammatory cells and viscosity of abscess fluid [56]. DWI aids in differentiating abscess from a necrotic or cystic tumor, or cystic demyelinating disease as the latter will show increased rather than restricted diffusion.

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Fig. 23.9 (a–c) West Nile meningomyelitis in a 38-year-­ old male with 3-day history of worsening lower extremity paralysis. T2-weighted sagittal image shows (a) extensive longitudinal high T2 cord signal. Sagittal T1-weighted

postcontrast image (b) showing dural and leptomeningeal enhancement. Diffusion is restricted in the thoracic spine (c) with decreased ADC (0.67 × 10−3 mm2/s) (not shown) which represents acute demyelination process

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23.6 M  echanical Cord Injury (Table 23.3)

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23.6.1 Compressive Myelopathy

Beyond any obvious mass effect on the cord such as from a recent trauma or severe degenerative disease, subtle diagnosis of compressive myelopathy has typically relied on high T2 cord signal and atrophy (Fig.  23.2). Unfortunately, studies have shown that high T2 signal was only observed in 15–65% of symptomatic patients [57, 58]. Various contrast enhancement patterns have been described including peripheral, central, scattered, diffuse, and “snake eyes.” Compressive myelopathy will typically be slightly hyperintense on DWI and have increased ADC values, representing facilitated diffusion due to chronic hypoperfusion and changes in the arrangement of axons of the spinal cord [57, 58]. Some studies have suggested that ADC maps

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Although not completely understood, the pathophysiology of compressive myelopathy is secondary to spinal stenosis from bulging or herniated disks, osseous spurs, or other form of mass effect on the cord leading to decreased perfusion, infarct, and necrosis [57, 58]. Clinical symptoms include neck or back pain, neurogenic claudication, and radiculopathy and may progress in severity to myelopathy with a combination of upper and lower motor neuron symptoms and signs plus sensory and autonomic deficits.

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have sensitivity as high as 80% compared to T2 sensitivity of 61% and were able to show statistically significant changes in ADC values between normal patients and those suffering from cervical myelopathy before cord compression is even appreciated on conventional MR [59, 60].

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23.6.2 Spinal Cord Contusion

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The most common etiology of spinal cord contusion is high velocity trauma, which tends to happen most commonly in young adults during motor vehicle accidents. Cord contusion in middle aged and older adults is typically the result of a fall. Cord contusion is most easily described as a “bruise” of the spinal cord with intramedullary hemorrhage and a variable amount of adjacent edema depending on the acuity of the injury. Diffusion can be especially useful for spinal cord contusion (Fig.  23.12). In the acute stage, cytotoxic edema due to severe cord injury will restrict water diffusion, while adjacent vasogenic edema shows up slightly hyperintense on DWI and increased ADC helping to delineate the different types of edema [61].

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23.6.3 Syrinx and Pre-syrinx State

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Fig. 23.10 (a–c) EV-71 infection. 6-month-old male who presented with fever, vomiting, and respiratory failure. Sagittal diffusion-weighted imaging demonstrated restricted diffusion involving the midbrain, dorsal pons, dorsal medulla, and anterior spinal cord (a). The areas of restricted diffusion within the brainstem and anterior horn cells also demonstrated T2 hyperintensities (b and c)

Syringomyelia consists of a cystic cavity within the spinal cord that is not lined with ependyma. It occurs in many conditions, including post-­ traumatic states, Chiari malformations, arachnoiditis, spine and spinal cord tumors, and spondylosis. Although the pathophysiology is not certain, it is hypothesized that mechanical impairment of normal CSF circulation drives CSF into the cord via perivascular spaces, partially due to CSF pulsation, resulting in a syrinx cavity. Pre-­ syrinx states can be thought of as intramedullary interstitial edema and enlargement due to CSF flow into the cord at presumed areas of an underdeveloped central canal, which pathologically represents interstitial edema, gliosis, and slight loss of gray matter [62]. Pre-syrinx states are of special importance because they are reversible after surgical treatment of the offending cause.

23  Spinal Cord Lesions Fig. 23.11 (a, b) Intramedullary spinal cord abscess in a pediatric patient with Chiari I malformation and associated syrinx. Sagittal T2-weighted (a) image shows a syrinx in the cervical cord and enhancing abscess in the lower portion of the syrinx. Diffusion images (b) show high signal in the abscess with reduced ADC (0.54 × 10−3 mm2/s) (not shown)

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Clinical symptoms are varied and depend on size and location of the syrinx. Typically, patients present with some degree of pain and thermal sensory loss. Syringomyelia will appear as a central tubular intramedullary cystic lesion that is high on T2-, but low on T1-weighted images along the trajectory of the central canal [62–65]. Presyrinx states typically present with isointense T1 and high intramedullary T2 signal (Fig. 23.13). Diffusion is typically low in signal intensity with high ADC values, reflecting interstitial edema.

b

23.7 Neoplasm Ependymoma is the most common in adults and astrocytoma is the most common spinal cord tumor in children. DWI signal and ADC value of neoplasms depend on the cellularity and extracellular matrix. Low-grade astrocytoma usually shows high ADC values due to the abundant extracellular matrix which contains more free water. Ependymoma shows intermediate ADC values and often contains hemorrhage. Hypercellular tumors such as high-grade glioma, intramedullary metastasis, and lymphoma can

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Fig. 23.12 (a–c) Acute cord contusion in a 39-year-old male status post motor vehicle accident. Sagittal T2-weighted images (a) show extensive high signal associated with a fracture dislocation. The upper region of edema (b) shows hyperintense diffusion signal with

increased ADC (1.20 × 10−3 mm2/s) that represents vasogenic edema. At the contusion site (c), there is restricted diffusion with decreased ADC (0.57 × 10−3 mm2/s) at the fracture suggestive of cytotoxic edema

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Fig. 23.13 (a–c) Pre-syrinx state. Sagittal T2-weighted image (a) of the cervical spine shows a hyperintense lesion involving a large portion of the cord secondary to Chiari I malformation. b,c BLADE DWIs at the level of syrinx (b) and just inferior to the syrinx (c) clearly demon-

strate syrinx and pre-syrinx state without distortion. The ADC values are 3.61  ×  10−3  mm2/s in a syrinx and 1.76 × 10−3 mm2/s in a pre-syrinx state representing interstitial edema

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Fig. 23.14 (a–d) Astrocytoma of the cervical spinal cord. Sagittal MRI images show T2 high (a) and T1 low (b) signal mass lesion without enhancement. (c, d) DWI

hyperintensity with high ADC (1.57 × 10−3 mm2/s) which likely represents more abundant extracellular matrix seen in pilocytic astrocytoma

show restricted diffusion with low ADC associated with less extracellular matrix.

Similarly, Rumboldt et al. found that with a cutoff value of 1.4 × 10−3 mm2/s, they were able to differentiate infratentorial juvenile pilocytic astrocytoma (WHO grade I) from medulloblastoma (WHO grade IV) with 100% specificity in posterior fossa tumors [68]. This relationship seemed to hold true in a meta-analysis for intra-­ axial astrocytomas, which showed that lower ADC values correlated with poor survival independent of tumor grade [69].

23.7.1 Astrocytoma A retrospective study by Yamasaki et  al. found ADC values for posterior fossa pilocytic astrocytoma ranging between 1.3–1.9 × 10−3 mm2/s with a mean of 1.66 × 10−3 mm2/s (Fig. 23.14). ADC values for anaplastic astrocytomas ranged between 1.04–1.57 × 10−3 mm2/s with a mean of 1.24 × 10−3 mm2/s. To provide perspective, ependymomas had ADC values that ranged from 1.05–1.33  ×  10−3  mm2/s with an average of 1.23 × 10−3 mm2/s. Overall the authors hypothesized that higher-grade tumors roughly correlated with lower ADC values and vice versa which were corroborated in other studies [66, 67].

23.7.2 Ependymoma A retrospective study found ADC values for intracranial ependymomas to range between 1.05–1.33  ×  10−3  mm2/s with a mean of 1.2 × 10−3 mm2/s [70] (Fig. 23.15), which is typically lower than those seen for pilocytic astrocy-

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Fig. 23.15 (a–d) Cervical cord ependymoma with hemorrhage. (a) Sagittal T2-weighted image shows fluid-fluid levels showing hemorrhage and cystic component. (b) Gadolinium-enhanced T1-weighted image shows a small

enhancing portion and peripheral enhancement. DWI (c) and ADC (d) of the enhancing solid portion shows intermediate ADC value (0.94 × 10−3 mm2/s)

tomas [71]. Other studies seem to confirm this trend of lower ADC values correlating with higher-grade tumors [72, 73].

Patients, even those with multiple intramedullary metastases, may be asymptomatic. With intramedullary metastasis, the cord is focally enlarged. On T2-weighted images, there is typically diffuse edema surrounding the lesion. Most metastatic lesions will show ring or homogenous enhancement (Fig.  23.16). However, if hemorrhage is present, enhancement will become heterogeneous [75]. A retrospective review of 70 intramedullary metastasis found that peri, intra-­ tumoral cysts or hemorrhage were only seen in 4% of the sample size [74]. Metastatic lesions tend to be most common in the cervical spine, followed by the thoracic and then lumbar spine. Diffusion and ADC characteristics likely depend on the cellularity of primary tumor. ADC values for intra-axial metastasis ranged between 0.89–1.58  ×  10−3  mm2/s with a mean of 1.15 × 10−3 mm2/s [66].

23.7.3 Intramedullary Metastasis Intramedullary metastasis is a poor prognostic indicator with a median survival of only 4 months after the time of diagnosis. Lung cancer is the most common cause, with breast cancer being the second most common. However, any primary tumor has the potential to spread to the cord either via hematogenous dissemination or through the central canal via CSF pathways (primary brain tumors). In a recent retrospective study primary malignancy was not always previously diagnosed at the time of intramedullary metastasis symptom onset or MRI imaging [74].

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Fig. 23.16 (a–e) Intramedullary metastatic lesion from breast cancer. Sagittal T2 (a) and gadolinium-enhanced T1-weighted (b) images show an enhancing intramedullary lesion near the conus medullaris (arrow). (c) Axial

T2-weigted image at the liver shows high signal cord lesion (arrow) and multiple liver metastases. (d, e) Sagittal DWI shows hyperintensity and corresponding low ADC (arrow)

23.7.4 Lymphoma

and may range from patchy to diffuse to discrete. Lymphoma will typically show diffusion restriction indicative of its hypercellularity [75]. ADC values for intracranial lymphomas ranged between 0.60–1.07 × 10−3 mm2/s with a mean of 0.73 × 10−3 mm2/s [66] (Fig. 23.17).

Most intramedullary lymphomas are highly aggressive with poor prognosis. The most common known risk factor is immunodeficiency. Immunocompetent patients tend to present older, roughly at 60 years, while immunosuppressed patients tend to present in their 30–40 years [75, 76]. The most typical presenting symptoms include back pain (30%), progressive myelopathy, and radiculopathy. More than 50% of spinal lymphomas are multifocal, which includes intracranial lesions [75, 76]. Not uncommonly, the lesion is isointense on T1-weighted images and hyperintense on T2-weighted images. Enhancement is variable

23.7.5 Waldenstrom’s Macroglobulinemia and Bing-­ Neel Syndrome Waldenstrom’s macroglobulinemia is a rare lymphoproliferative disorder characterized by bone marrow and lymphoid tissue B cell infiltration as well as presence of IgM monoclonal gammopa-

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Fig. 23.17 (a–e) Lymphoma with invasion of the cord. (a) Sagittal T1 postcontrast image demonstrates enhancing intradural lesion (arrow) with invasion of the cord better visualized on the axial T1 postcontrast image (b).

Extensive leptomeningeal enhancement and cord edema are also present. DWI and ADC of the enhancing solid portion show high and low ADC (0.63 × 10−3 mm2/s)

thy in the serum. Bing-Neel syndrome is a rare neurological complication of the already rare Waldenstrom’s macroglobulinemia with central nervous system involvement by malignant cells that was first described in 1936 by Jens Bing and Axel Valdemar von Neel [77]. Diagnosis of Bing-­ Neel syndrome is based on cytological or histological confirmation most often via cerebrospinal fluid analysis in the setting of systemic Waldenstrom’s macroglobulinemia. Imaging plays a helpful role in diagnosis. Routine MR sequences such as FLAIR, DWI, and contrast-enhanced T1-weighted images are important in identifying Bing-Neel syndrome. The most common radiological finding is contrast enhancement of the subarachnoid space due to leptomeningeal infiltration within the brain or spine. The cranial nerves and cauda equina nerve roots may become thickened with contrast enhancement [78]. Dural thickening with enhancement is another common finding. Brain parenchymal involvement may often present with periventricular and brainstem enhancing lesions. Similar to lymphoma with hypercellular cells,

lesions in Bing-Neel syndrome may sometimes show restricted diffusion (Fig. 23.18).

23.8 Conclusion Diffusion-weighted imaging is useful in narrowing the differential diagnoses of intramedullary spinal lesions. Etiologies that cause cytotoxic edema or decreased free diffusion of water will lead to diffusion restriction, which includes spinal cord infarct, acute demyelination, intramedullary abscess, and highly cellular tumors. Conversely, lesions that have increased diffusion of water molecules will have elevated ADC values, which include subacute to chronic inflammatory and demyelinating lesions, chronic compressive myelopathy, pre-syrinx state, and low-grade primary tumors. On the other hand, technical difficulties for DWI implementation in the spine are being addressed by development of different MRI sequences that minimize magnetic homogeneity issues. Although the early data is promising in regard to the utility of DWI and

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Fig. 23.18 (a–c) Bing-Neel syndrome. 67-year-old male with sensation of disequilibrium for 2 months, trouble with fine motor movements in both hands, with progressive bowel/bladder changes and radicular pain into legs. MRI of the cervical spine demonstrates T2 hyperintense

(a), contrast-enhancing (b), and restricted diffusion (c) lesion in the cervical-medullary junction (arrows) and cauda equine enhancement (not shown) compatible with Bing-Neel syndrome. CSF cytology and flow cytometry was positive for malignant cells with elevated IgM

ADC with intramedullary lesions, more dedicated research studies will be needed in the future.

medullary, whereas those arising from the dura or elsewhere are extramedullary. Intramedullary spinal cord tumors (IMSCT) are rare lesions and constitute only 4–10% of all primary central nervous system tumors [70, 79, 80]. IMSCTs are less common in adults than in children. The most frequently involved localization was in the thoracic region (36%), followed by the cervical region (33%), the cervicothoracic region (19%), and the thoracolumbar (with conus) region (13%) [79]. The majority of IMSCTs are comprised of gliomas (80–90%), of which 60–70% are ependymomas and 30–40% are astrocytomas [80–82]. Imaging is paramount in identifying intradural from extradural spinal tumors. Diagnosis of the lesion can often be suspected or made

23.9 Treatment of Intramedullary Spinal Cord Tumors Patrick W. Hitchon, Royce W. Woodroffe, Jennifer Noeller, and Kirill V. Nourski

23.9.1 Introduction Spinal cord tumors are classified based on their anatomic location. Those originating from the spinal cord parenchyma are referred to as intra-

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from imaging, but confirmation of diagnosis demands surgery. In an attempt to identify radiographic features of spinal cord tumors, we reviewed our records of intramedullary spinal cord tumors for the past 10 years (IRB 201710760), including radiology reports, treatment, and outcomes. In our experience, we encountered 30 meningiomas, 30 intramedullary gliomas, 28 nerve sheath tumors, 4 hemangioblastomas, 3 cavernomas, and 3 intramedullary metastases. Hereafter, we describe our experience and that of others in the diagnosis and management of ependymomas and astrocytomas.

23.9.2 Ependymoma The most common intramedullary spinal cord tumor, regardless of age group, is ependymoma and accounts for 50–60% of lesions [83]. The vast majority of these lesions are WHO grade II, with WHO grade III anaplastic ependymoma

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Fig. 23.19 (a–c) A 47-year-old male with neck pain since a fall 1 month ago endorses instability of gait. No frank weakness on exam. MRI shows a 5.6 × 1.7 × 1.2 cm intramedullary lesion in the cervical cord extending from

being exceedingly rare. Up to 88% of patients with intramedullary spinal ependymomas present with neurologic symptoms, including motor weakness, sensory loss, or bladder dysfunction [84]. Of those who are neurologically intact, pain is the most common presenting symptom [84]. Intramedullary spinal ependymomas most commonly originate in the cervical spine, followed by the thoracic and then lumbar areas [85].

23.9.2.1 Imaging An intramedullary tumor is radiographically recognized by focal expansion of the cord and occasionally an associated cyst. MRI shows T2-weighted (T2W) and fluid attenuated inversion recovery (FLAIR) hyperintensity, T1-weighted (T1W) hypo- or isointensity, with variable contrast enhancement [86]. Ependymomas show contrast enhancement associated with cystic changes, and hemosiderin suggestive of previous hemorrhage [87, 88] (Figs.  23.19, 23.20, and 23.21). A syrinx was present in 80% of the ependymomas [80].

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C1 to C3 with high signal on T2 (a), low signal on T1 (b), and enhancing nodule (c). This is associated with fluid/ fluid level (a) compatible with hemorrhage

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general, most patients (69%) will have no changes in the neurologic status, and improvement is unlikely (20%) [91]. There is some increased risk with thoracic lesions and advanced age [90]. Depending on the plane of dissection and the infiltrative nature of the tumor, attempts should be made to achieve a gross total resection (GTR) [85]. For unresectable tumor or recurrences, radiation is recommended [85] (Figs. 23.22 and 23.23). Contrary to intramedullary astrocytomas, ependymomas yield themselves more likely to a successful GTR, which translates to a cure in the majority of patients [89]. Recurrence rates are lower as well, with one study demonstrating recurrence in only 7.3% of patients [91]. Chemotherapy is still considered investigational when other modalities have been exhausted [79].

23.9.3 Malignant Astrocytoma

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Fig. 23.20 (a–c) Intraoperative myelotomy (a), followed by total exposure of the entire ependymoma grade II (b). The tumor bed following excision of the tumor is seen in figure (c) 808 809 810 811 812 813 814 815 816

23.9.2.2 Treatment The vast majority of patients with intramedullary ependymomas present with a neurologic deficit, and in that case, early excision is recommended whenever possible [89, 90]. Patients who present without neurologic deficit should be appropriately counseled on the risks of surgery. One study of such patients demonstrated worsening of ­preoperative complaints in 10% [84]. In

Astrocytomas are the most common primary spinal cord neoplasm in the pediatric population, comprising up to 90% of all IMSCTs [92], while in the adult population they account for approximately 60% of IMSCTs [93]. Most of these lesions are low grade; however, 10–15% classify as malignant astrocytomas, including anaplastic astrocytomas (WHO grade III) and glioblastomas (WHO grade IV) [94]. Intramedullary astrocytomas most commonly originate in the cervical spine, followed by the thoracic and lumbar areas [95]. They commonly present in the third decade of life, with a range of 2–61 years found in one study [85].

23.9.3.1 Imaging Astrocytomas appear on MRI as fusiform expansion of the cord and are less likely to be associated with cyst or syrinx. The tumor is hypo- to isointense on T1W images, hyperintense on T2W and FLAIR images, with variable contrast enhancement. Astrocytoma is more likely than ependymoma to be non-enhancing [96] (Figs. 23.24, 23.25, and 23.26). In general, the distinction between astrocytomas and ependymomas by magnetic resonance

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Fig. 23.21 (a–c) Five years following resection, there is low signal intensity on T2 (a), T1 (b), and no enhancement with Gd administration (c). No evidence of recurrence

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Fig. 23.22 (a–c) A 43-year-old male presents with neck stiffness, numbness, and weakness in his arms. MRI shows a solid isointense mass on T2 (a) and T1 (b),

enhancing (c) from C3–7 capped with a syrinx extending to the medulla and caudally to the thoracic cord

(MR) is not possible [81], although ependymomas are generally better defined and less infiltrative than astrocytomas. A syrinx was present in 75% of the astrocytomas [80].

23.9.3.2 Treatment Astrocytomas often lack a clear dissection plane due to their infiltrative nature, making a GTR difficult to achieve [95]. In one study that

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Fig. 23.23 (a–c) One year from surgery for excision of the grade III ependymoma in Fig. 23.23, a postoperative MRI was obtained. The study shows small residual syrinx hyperintense on T2 (a) and isointense on T1 (b). There is

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Fig. 23.24 (a–c) A 37-year-old male presents with shaking in his legs and stiffness in walking. He has a sensory level and clonus. MRI shows abnormal high T2 signal (a)

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a small residual enhancing mass at the caudal margin of the resection cavity at C7 (c). As a result, the patient underwent radiation therapy and is being followed closely

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recurrence rate (47.6%) [88], and long-term survival is associated with a more aggressive resection [85, 91, 97]. In patients with anaplastic astrocytoma, the median over survival is 72 months, whereas for patients with glioblastoma it is only 9 months [85]. Chemotherapy is still considered investigational when other modalities have been exhausted [79].

23.9.4 Surgical Technique b

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Fig. 23.25 (a–c) Intraoperative exposure (a), circumferential dissection (b), and following excision of infiltrating grade II astrocytoma (c) 861 862 863 864 865 866 867 868 869 870 871 872 873

included 18 patients with WHO grade III or IV astrocytomas, none achieved a GTR [87]. In fact, this is generally not advisable due to risk of damage to normal cord parenchyma [95]. This risk is evident, as 61% of those in the aforementioned study had worsened functional status postoperatively [87]. Neurologic improvement for patients postoperatively is rare, with rates as low as 5% [88]. For significant residual, recurrences, or unresectable tumor, adjuvant radiation is recommended, and the majority of patients undergo treatment and receive some benefit [95]. Subtotal resection lends to a high

Surgery accomplishes three main goals: (1) tissue diagnosis; (2) cytoreduction; and (3) prevention of worsening of neurologic function. Timing of surgical intervention for IMSCTs is poorly studied. However, in patients with progressive neurologic decline, earlier intervention is preferred as preoperative neurologic function is strongly predictive of postoperative function [86]. Extent of tumor resection is a significant predictor of survival, as these lesions respond poorly to chemotherapy or radiation due to their low proliferation index [86]. Patient preparation is nowhere more necessary than in IMSC. The surgeon should always apprise the patient of the potential diagnosis and the risks involved. Within reason, the patient should be educated about the ease and complexity of these operations. Removal of these tumors can at times be easy, and at others quite difficult. Examples of similar cases should be presented to the patient to comprehend this potentially life-changing procedure. If the patient comprehends and the surgeon has the experience, then it is appropriate to proceed, earlier rather than later [98, 99]. If there is reluctance on the part of the patient to accept neurologic deficit in spite of malignancy, a delay in decision-making by the patient is wise. All biopsies or resections of intramedullary tumors were accomplished with intraoperative somatosensory evoked potentials (SSEP) and motor evoked potentials (MEP). For localization, intraoperative fluoroscopy was always used. Once the levels were confirmed, laminoplasty of the levels of interest was undertaken up to one level above and below the level of the

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Fig. 23.26 (a–c) Two years postoperative, there is a stable small residual hyperintensity on T2 signal (a), none on T1 (b), and no evidence of residual tumor enhancement (c)

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tumor, as it is unwise to undertake bony exposure once the dura is opened. Laminoplasty is commonly employed [83] and generally preferred as l­aminectomy can portend to the development of spinal deformity, especially in the cervical region [84]. However, in the pediatric population, laminoplasty or laminectomy of greater than four levels is still associated with the development of spinal deformity necessitating fusion [89]. Intraoperative ultrasonography is routinely used prior to dural opening. Myelotomy and dissection is always undertaken under the microscope with a suction regulator. A minimal amount of suction with a 5 French suction is necessary. The use of bipolar coagulation is minimized. Oftentimes small bleeding points can be easily controlled with Gelfoam, obviating the need for coagulation. Where a cleavage plane is identified between the tumor and spinal cord, total excision is always attempted, and oftentimes possible. The ultrasonic aspirator has provided a significant adjunct to resection of IMSCTs and has become integral to the armamentarium for resection of these lesions. SSEPs and MEPs have to be

heeded, as they are extremely sensitive and specific for anticipating neurologic injury [90], and at every stage the surgeon should always reconsider the risk vs. benefit to the patient where the cleavage plane is elusive. Intraoperative histopathologic analysis provides important information that will influence how aggressive the surgeon may be in attempting a gross total resection. Among the intramedullary tumors, ependymomas can often be entirely resected, while astrocytomas are more likely to infiltrate the spinal cord [79, 81, 98, 99]. However, it is important to keep in mind that intraoperative frozen pathology has lower diagnostic accuracy in spinal neoplasms (~70%) than with cranial neoplasms [100]. Resection of intradural but extramedullary spinal cord tumors such as meningiomas (Fig.  23.27) or nerve sheath tumors (Fig.  23.28) are by far simpler operations with far less risk of morbidity to the patient. Though hemangioblastoma tumors are also intramedullary, they are generally far simpler to remove as their borders are well delineated with significantly less trauma imparted to the cord (Fig. 23.29).

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Fig. 23.27 (a–c) A 71-year-old female with a several-­ month history of progressive weakness and numbness in her legs. She failed to improve with therapy, with persistence of paraparesis, loss of sensation in her legs, and fecal incontinence. Thoracic MRI shows mild hyperintense T2 signal (a, arrow), low attenuation on T1 (b), and

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Fig. 23.28 (a–c) A 64-year-old lady complains of 1-year history of bilateral leg pain. MRI shows a well-defined intradural extramedullary lesion at the level of T11-T12.

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a homogeneously enhancing extradural extramedullary mass at T2-T3 with a dural tail effacing the right posterolateral aspect of the thecal sac and causing significant mass effect on the spinal cord (c). Uneventful surgery resulted in excision of the meningioma, with slow but significant improvement

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The lesion appears isointense on T2 (a) and T1 (b) and shows homogeneous intense contrast enhancement (c). Pathology revealed a benign schwannoma

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Fig. 23.29 (a–c) A 39-year-old lady has had problems with coordination and function on the right side of her body for the last 2–3 years. Her medical history is positive for retinoblastoma of her right eye as a child. MRI shows cord enlargement from the 4th ventricle to T3. There is

hyperintensity on T2 (a) and decreased signal on T1 (b). There is intense enhancement (c), with a non-enhancing cyst rostral to the enhancing mass. The differential includes hemangioblastoma and ependymoma. The pathology following surgery confirmed the former

Table 23.4  Demographic and radiographic features of spinal cord tumors Parameter Number M/F Age Distribution cervical/thoracic Presence of syrinx Syrinx rostral/caudal MRI accuracy

969 970 971 972 973 974 975 976 977 978 979 980

Astrocytoma 15 9/6 34+/−22 5/10 7 6/3 13/15

23.9.5 Our Experience Our review yielded 54 intramedullary tumors, with 15 astrocytomas and 15 ependymomas (Table 23.4). The results suggest no gender or age predilection to one pathology over the other. The results show a prevalence of astrocytomas to the thoracic spine, whereas ependymomas were more prevalent in the cervical spine. MRI was accurate in identifying gliomas from other pathologies such as hemangioblastoma, nerve sheath tumors, or infections. However, distinguishing astrocytoma from ependymoma was not

Ependymoma 15 8/7 42+/−11 10/5 12 11/10 15/15

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consistent, and they were listed as one or the other. A syrinx cavity was more often associated with ependymoma (12/15) compared to glioma (7/15, p = 0.0253).

23.9.6 Conclusion Based on our single institution review, the differences between ependymomas and astrocytomas have meaningful clinical significance in terms of need for ancillary treatment as well as prognosis. On MRI, ependymomas were more likely to be

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associated with a syrinx. Distinguishing astrocytoma from ependymoma from MRI images is suspected but not always accurate.

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14. Weidauer S et  al (2002) Spinal cord infarction: MR imaging and clinical features in 16 cases. Neuroradiology 44(10):851–857 15. Bammer MMTaR (2006) Diffusion-weighted magnetic resonance imaging of the spine and spinal cord. Acknowledgement  The authors acknowledge the Semin Roentgenol 41:294–311 invaluable assistance of Faith Vaughn in the editing and 16. Loher TJ, Bassetti CL, Lovblad KO et  al (2003) submission of this manuscript. Diffusion-weighted MRI in acute spinal cord ischaemia. Neuroradiology 45:557–561 17. Wilhelm Kuker MW, Klose U et al (2004) Diffusion-­ weighted MRI of spinal cord infarction. High References resolution imaging and time course of diffusion abnormality. J Neurol 251:818–824 1. Beslow LA et  al (2008) Role of diffusion MRI in 18. Criscuolo GR, Oldfield EH, Doppman JL (1989) diagnosis of spinal cord infarction7  in children. Reversible acute and subacute myelopathy in Neuropediatrics 39(3):188–191 patients with dural arteriovenous fistulas. Foix-­ 2. Facon D et al (2005) MR diffusion tensor imaging Alajouanine syndrome reconsidered. J Neurosurg and fiber tracking in spinal cord compression. AJNR 70(3):354–359 Am J Neuroradiol 26(6):1587–1594 19. Heros RC (2009) Foix-Alajouanine syndrome: what 3. Kuker W et  al (2004) Diffusion-weighted MRI of is it? J Neurosurg 111(5):900–901 spinal cord infarction—high resolution imaging 20. Inoue T et al (2006) Congestive myelopathy due to and time course of diffusion abnormality. J Neurol cervical perimedullary arteriovenous fistula evalu251(7):818–824 ated by apparent diffusion coefficient values—case 4. Loher TJ et  al (2003) Diffusion-weighted MRI report. Neurol Med Chir (Tokyo) 46(11):559–562 in acute spinal cord ischaemia. Neuroradiology 21. Kataoka H et al (2001) Venous congestion is a major 45(8):557–561 cause of neurological deterioration in spinal arterio 5. Sagiuchi T et  al (2002) Diffusion-weighted MRI venous malformations. Neurosurgery 48(6):1224– of the cervical cord in acute spinal cord injury with 1229; discussion 1229–30 type II odontoid fracture. J Comput Assist Tomogr 22. Spetzler RF et  al (2002) Modified classification 26(4):654–656 of spinal cord vascular lesions. J Neurosurg 96(2 6. Thurnher MM, Bammer R (2006) Diffusion-­ Suppl):145–156 weighted MR imaging (DWI) in spinal cord isch- 23. Sibon I et  al (2006) Diffusion MRI in spinal dural emia. Neuroradiology 48(11):795–801 arterio-venous fistula: a case report. Spinal Cord 7. Porter DA, Heidemann RM (2009) High resolu44(5):315–317 tion diffusion-weighted imaging using readout-­ 24. Borchers AT, Gershwin ME (2012) Transverse segmented echo-planar imaging, parallel imaging myelitis. Autoimmun Rev 11(3):231–248 and a two-dimensional navigator-based reacquisi- 25. Frohman EM, Wingerchuk DM (2010) Clinical tion. Magn Reson Med 62(2):468–475 practice. Transverse myelitis. N Engl J Med 8. Kumral E et  al (2011) Spinal ischaemic stroke: 363(6):564–572 clinical and radiological findings and short-term out- 26. Seifert T et  al (2005) Relapsing acute transcome. Eur J Neurol 18(2):232–239 verse myelitis: a specific entity. Eur J Neurol 9. Manara R et al (2010) Spinal cord infarction due to 12(9):681–684 fibrocartilaginous embolization: the role of diffusion 27. Alper G et  al (2011) Idiopathic acute transverse weighted imaging and short-tau inversion recovery myelitis in children: an analysis and discussion of sequences. J Child Neurol 25(8):1024–1028 MRI findings. Mult Scler 17(1):74–80 10. Millichap JJ, Sy BT, Leacock RO (2007) Spinal 28. Choi KH et  al (1996) Idiopathic transverse myelicord infarction with multiple etiologic factors. J Gen tis: MR characteristics. AJNR Am J Neuroradiol Intern Med 22(1):151–154 17(6):1151–1160 11. Hickey R et al (1986) Autoregulation of spinal cord 29. Goh C, Phal PM, Desmond PM (2011) Neuroimaging blood flow: is the cord a microcosm of the brain? in acute transverse myelitis. Neuroimaging Clin N Stroke 17(6):1183–1189 Am 21(4):951–973 12. Marcus ML et  al (1977) Regulation of total 30. Renoux J et al (2006) MR diffusion tensor imaging and regional spinal cord blood flow. Circ Res and fiber tracking in inflammatory diseases of the spi41(1):128–134 nal cord. AJNR Am J Neuroradiol 27(9):1947–1951 13. Sandler AN, Tator CH (1976) Effect of acute 31. Dale RC et al (2000) Acute disseminated encephaspinal cord compression injury on regional spilomyelitis, multiphasic disseminated encephalonal cord blood flow in primates. J Neurosurg myelitis and multiple sclerosis in children. Brain 45(6):660–676 123(12):2407–2422

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32. Hynson JL et al (2001) Clinical and neuroradiologic features of acute disseminated encephalomyelitis in children. Neurology 56(10):1308–1312 33. Tenembaum S, Chamoles N, Fejerman N (2002) Acute disseminated encephalomyelitis: a long-term follow-up study of 84 pediatric patients. Neurology 59(8):1224–1231 34. Marcel C et al (2010) Diffusion-weighted imaging in noncompressive myelopathies: a 33-patient prospective study. J Neurol 257(9):1438–1445 35. Balasubramanya KS et al (2007) Diffusion-weighted imaging and proton MR spectroscopy in the characterization of acute disseminated encephalomyelitis. Neuroradiology 49(2):177–183 36. Zuccoli G et al (2014) Vasogenic edema characterizes pediatric acute disseminated encephalomyelitis. Neuroradiology 56:679–684 37. Andre JB, Bammer R (2010) Advanced diffusion-­ weighted magnetic resonance imaging techniques of the human spinal cord. Top Magn Reson Imaging 21(6):367–378 38. Lycklama G et  al (2003) Spinal-cord MRI in multiple sclerosis. Lancet Neurol 2(9):555–562 39. Clark CA, Werring DJ, Miller DH (2000) Diffusion imaging of the spinal cord in vivo: estimation of the principal diffusivities and application to multiple sclerosis. Magn Reson Med 43(1):133–138 40. McKeon A et  al (2008) CNS aquaporin-4 autoimmunity in children. Neurology 71(2):93–100 41. Wingerchuk DM et al (2007) The spectrum of neuromyelitis optica. Lancet Neurol 6(9):805–815 42. Kitley J et  al (2014) Neuromyelitis optica spectrum disorders with aquaporin-4 and myelin-­ oligodendrocyte glycoprotein antibodies: a comparative study. JAMA Neurol 71(3):276–283 43. Wingerchuk DM et  al (2006) Revised diagnostic criteria for neuromyelitis optica. Neurology 66(10):1485–1489 44. Benedetti B et al (2006) Grading cervical cord damage in neuromyelitis optica and MS by diffusion tensor MRI. Neurology 67(1):161–163 45. Sohn M et  al (2013) Spinal cord neurosarcoidosis. Am J Med Sci 347:195–198 46. Lee WJ, Hsu HY, Wang PY (2008) Reversible myelopathy on magnetic resonance imaging due to cobalamin deficiency. J Chin Med Assoc 71(7):368–372 47. Hirata A et al (2006) Subacute combined degeneration of the spinal cord concomitant with gastric cancer. Intern Med 45(14):875–877 48. Okada S et  al (2006) Two cases of subacute combined degeneration: magnetic resonance findings. J Nippon Med Sch 73(6):328–331 49. Ravina B, Loevner LA, Bank W (2000) MR findings in subacute combined degeneration of the spinal cord: a case of reversible cervical myelopathy. AJR Am J Roentgenol 174(3):863–865 50. Tian C (2011) Hyperintense signal on spinal cord diffusion-weighted imaging in a patient with subacute combined degeneration. Neurol India 59(3):429–431

J. Kim et al. 51. Kim EY et al (2013) Subacute combined degeneration revealed by diffusion-weighted imaging: a case study. Clin Neuroradiol 23(2):157–159 52. Jang S et al (2012) Enterovirus 71-related encephalomyelitis: usual and unusual magnetic resonance imaging findings. Neuroradiology 54(3):239–245 53. Li H et al (2019) MRI reveals segmental distribution of enterovirus lesions in the central nervous system: a probable clinical evidence of retrograde axonal transport of EV-A71. J Neuro-Oncol 25(3):354–362 54. Iwasaki M et  al (2011) Acute onset intramedullary spinal cord abscess with spinal artery occlusion: a case report and review. Eur Spine J 20(Suppl 2):S294–S301 55. Murphy KJ et al (1998) Spinal cord infection: myelitis and abscess formation. AJNR Am J Neuroradiol 19(2):341–348 56. Dorflinger-Hejlek E et al (2010) Diffusion-weighted MR imaging of intramedullary spinal cord abscess. AJNR Am J Neuroradiol 31(9):1651–1652 57. Thurnher MM, Bammer R (2006) Diffusion-­ weighted magnetic resonance imaging of the spine and spinal cord. Semin Roentgenol 41(4):294–311 58. Hori M et al (2012) New diffusion metrics for spondylotic myelopathy at an early clinical stage. Eur Radiol 22(8):1797–1802 59. Demir A et al (2003) Diffusion-weighted MR imaging with apparent diffusion coefficient and apparent diffusion tensor maps in cervical spondylotic myelopathy. Radiology 229(1):37–43 60. Banaszek A et  al (2014) Usefulness of diffusion tensor MR imaging in the assessment of intramedullary changes of the cervical spinal cord in different stages of degenerative spine disease. Eur Spine J 23(7):1523–1530 61. Zhang JS, Huan Y (2014) Multishot diffusion-­ weighted MR imaging features in acute trauma of spinal cord. Eur Radiol 24(3):685–692 62. Yurube T et  al (2009) The vanishment of an intramedullary high-signal intensity lesion at the craniocervical junction after surgical treatment: a case report of the presyrinx state. Spine (Phila Pa 1976) 34(6):E235–E239 63. Eser O et  al (2007) Idiopathic recurrent transverse myelitis with syringomyelia: a case report. Turk Neurosurg 17(3):228–231 64. Fischbein NJ et  al (1999) The “presyrinx” state: a reversible myelopathic condition that may precede syringomyelia. AJNR Am J Neuroradiol 20(1):7–20 65. Fischbein NJ et  al (2000) The “presyrinx” state: is there a reversible myelopathic condition that may precede syringomyelia? Neurosurg Focus 8(3):E4 66. Yamasaki F et  al (2005) Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology 235(3):985–991 67. de Fatima Vasco Aragao M et al (2014) Comparison of perfusion, diffusion, and MR spectroscopy between low-grade enhancing pilocytic astrocytomas and high-grade astrocytomas. AJNR Am J Neuroradiol 35:1495–1502

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68. Rumboldt Z et al (2006) Apparent diffusion coefficients for differentiation of cerebellar tumors in children. AJNR Am J Neuroradiol 27(6):1362–1369 69. Zulfiqar M, Yousem DM, Lai H (2013) ADC values and prognosis of malignant astrocytomas: does lower ADC predict a worse prognosis independent of grade of tumor?—a meta-analysis. AJR Am J Roentgenol 200(3):624–629 70. Minehan KJ, Brown PD, Scheithauer BW, Krauss WE, Wright MP (2009) Prognosis and treatment of spinal cord astrocytoma. Int J Radiat Oncol Biol Phys 73(3):727–733 71. Brandao LA, Shiroishi MS, Law M (2013) Brain tumors: a multimodality approach with diffusion-­ weighted imaging, diffusion tensor imaging, magnetic resonance spectroscopy, dynamic susceptibility contrast and dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Imaging Clin N Am 21(2):199–239 72. Gimi B et  al (2012) Utility of apparent diffusion coefficient ratios in distinguishing common pediatric cerebellar tumors. Acad Radiol 19(7):794–800 73. Porto L et  al (2013) Differentiation between high and low grade tumours in paediatric patients by using apparent diffusion coefficients. Eur J Paediatr Neurol 17(3):302–307 74. Rykken JB et  al (2013) Intramedullary spinal cord metastases: MRI and relevant clinical features from a 13-year institutional case series. AJNR Am J Neuroradiol 34(10):2043–2049 75. Mechtler LL, Nandigam K (2013) Spinal cord tumors: new views and future directions. Neurol Clin 31(1):241–268 76. Flanagan EP et  al (2011) Primary intramedullary spinal cord lymphoma. Neurology 77(8):784–791 77. Fitsiori A et  al (2019) Imaging spectrum of Bing-­ Neel syndrome: how can a radiologist recognise this rare neurological complication of Waldenstrom’s macroglobulinemia? Eur Radiol 29(1):102–114 78. Varettoni M et  al (2017) Bing-Neel Syndrome: illustrative cases and comprehensive review of the literature. Mediterr J Hematol Infect Dis 9(1):e2017061 79. Bostrom A, Kanther NC, Grote A, Bostrom J (2014) Management and outcome in adult intramedullary spinal cord tumours: a 20-year single institution experience. BMC Res Notes 7:908 80. Manzano G, Green BA, Vanni S, Levi AD (2008) Contemporary management of adult intramedullary spinal tumors-pathology and neurological outcomes related to surgical resection. Spinal Cord 46(8):540–546 81. Chamberlain MC, Tredway TL (2011) Adult primary intradural spinal cord tumors: a review. Curr Neurol Neurosci Rep 11(3):320–328 82. Slooff JL (1964) Primary intramedullary tumors of the spinal cord and filum terminale. Saunders, Philadelphia, p 255 83. Woodroffe RW, Zanaty M, Kirby P, Dlouhy BJ, Menezes AH (2018) Resection of a pediatric intramedullary spinal cord tumor: 2-dimensional opera-

873 tive video. Oper Neurosurg (Hagerstown) 83. https:// doi.org/10.1093/ons/opy185 84. Joaquim AF, Riew KD (2015) Management of cervical spine deformity after intradural tumor resection. Neurosurg Focus 39(2):E13 85. McGirt MJ, Goldstein IM, Chaichana KL, Tobias ME, Kothbauer KF, Jallo GI (2008) Extent of surgical resection of malignant astrocytomas of the spinal cord: outcome analysis of 35 patients. Neurosurgery 63(1):55–60; discussion -1 86. Abul-Kasim K, Thurnher MM, McKeever P, Sundgren PC (2008) Intradural spinal tumors: current classification and MRI features. Neuroradiology 50(4):301–314 87. Raco A, Esposito V, Lenzi J, Piccirilli M, Delfini R, Cantore G (2005) Long-term follow-up of intramedullary spinal cord tumors: a series of 202 cases. Neurosurgery 56(5):972–981; discussion -81 88. Karikari IO, Nimjee SM, Hodges TR, Cutrell E, Hughes BD, Powers CJ, et  al (2011) Impact of tumor histology on resectability and neurological outcome in primary intramedullary spinal cord tumors: a single-center experience with 102 patients. Neurosurgery 68(1):188–197; discussion 97 89. Ahmed R, Menezes AH, Awe OO, Mahaney KB, Torner JC, Weinstein SL (2014) Long-term incidence and risk factors for development of spinal deformity following resection of pediatric intramedullary spinal cord tumors. J Neurosurg Pediatr 13(6):613–621 90. Scibilia A, Terranova C, Rizzo V, Raffa G, Morelli A, Esposito F et al (2016) Intraoperative neurophysiological mapping and monitoring in spinal tumor surgery: sirens or indispensable tools? Neurosurg Focus 41(2):E18 91. Adams H, Avendano J, Raza SM, Gokaslan ZL, Jallo GI, Quinones-Hinojosa A (2012) Prognostic factors and survival in primary malignant astrocytomas of the spinal cord: a population-based analysis from 1973 to 2007. Spine (Phila Pa 1976) 37(12):E727–E735 92. Winn HR (2017) Youmans and Winn neurological surgery. Seventh edition. ed. Elsevier, Philadelphia, PA 93. Ottenhausen M, Ntoulias G, Bodhinayake I, Ruppert FH, Schreiber S, Forschler A et al (2018) Intradural spinal tumors in adults-update on management and outcome. Neurosurg Rev 42:371–388 94. Harrop JS, Ganju A, Groff M, Bilsky M (2009) Primary intramedullary tumors of the spinal cord. Spine (Phila Pa 1976) 34(22 Suppl):S69–S77 95. Tobin MK, Geraghty JR, Engelhard HH, Linninger AA, Mehta AI (2015) Intramedullary spinal cord tumors: a review of current and future treatment strategies. Neurosurg Focus 39(2):E14 96. Kim DH, Kim JH, Choi SH, Sohn CH, Yun TJ, Kim CH et al (2014) Differentiation between intramedullary spinal ependymoma and astrocytoma: comparative MRI analysis. Clin Radiol 69(1):29–35 97. Ahmed R, Menezes AH, Awe OO, Torner JC (2014) Long-term disease and neurological outcomes in

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patients with pediatric intramedullary spinal cord tumors. J Neurosurg Pediatr 13(6):600–612 98. Garces-Ambrossi GL, McGirt MJ, Mehta VA, Sciubba DM, Witham TF, Bydon A et  al (2009) Factors associated with progression-free survival and long-term neurological outcome after resection of intramedullary spinal cord tumors: analysis of 101 consecutive cases. J Neurosurg Spine 11(5):591–599

J. Kim et al. 99. Klekamp J (2013) Treatment of intramedullary tumors: analysis of surgical morbidity and long-term results. J Neurosurg Spine 19(1):12–26 100. Hongo HT, Takai K, Komori T, Taniguchi M (2018) Intramedullary spinal cord ependymoma and astrocytoma: intraoperative frozen-section diagnosis, extent of resection, and outcomes. J Neurosurg Spine 30:1–7

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Author Queries Chapter No.: 23

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AU1

Please check if “DWI BLADE-DWI” should be changed to “BLADE-DWI” in sentence “We also demonstrate different…”.

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This does not make sense since the ADC values are elevated and you are discussing restricted diffusion

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Author’s Response

Part III

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Futures of Diffusion Imaging

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Future Directions for Diffusion Imaging of the Brain and Spinal Cord

24

Takayuki Obata, Jeff Kershaw, Akifumi Hagiwara, and Shigeki Aoki

The purpose of this book is to illustrate how DWI technology can be applied as an effective clinical tool. However, there are a number of DWI methods that are expected to be effective in principle, but are not yet widely used due to technological limitations and limited awareness amongst clinicians. The biggest bottleneck in the development of DWI as a clinical tool is the strength and stability of the magnetic field gradients. Some development is required in the reduction of artifacts related to strong magnetic field gradients, in the maintenance of a reliable magnetic field environment during rapid gradient switching, and in post-­acquisition correction techniques. Nevertheless, clinical applications continue to be investigated as hardware and software technology progresses. One trend in this field is to investigate new contrasts by varying the diffusion-time. The basics of diffusion-time-dependent DWI are introduced in the first two sections of this chapter. The final section discusses the extraction of microstructural information from DWI of the brain and spinal cord.

T. Obata (*) · J. Kershaw Applied MRI Research, National Institute of Radiological Sciences, QST, Chiba, Japan e-mail: [email protected]; [email protected] A. Hagiwara · S. Aoki Department of Radiology, Juntendo University, Tokyo, Japan e-mail: [email protected]; [email protected]

24.1 Introducing the Diffusion-Time

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The b-value is used in DWI as a measurement parameter that characterizes signal attenuation or decay. When DWI is performed on a liquid, the measurements agree well with a simple single-­ exponential signal model having an exponent equal to the product of the b-value and diffusion coefficient of the liquid. However, interpreting the signal behaviour for more complex samples like in  vivo tissue is not so simple. It is sometimes found that the signal may vary between measurements even when the same b-value is applied. This phenomenon can often be attributed to changes in another parameter known as the diffusion-time. Before discussing the diffusiontime in more detail, we would first like to briefly revise the meaning of the b-value for the pulsedgradient spin-­echo (PGSE) sequence.

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© Springer Nature Switzerland AG 2021 T. Moritani, A. A. Capizzano (eds.), Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine, https://doi.org/10.1007/978-3-030-62120-9_24

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and G is the strength of the magnetic gradient (Fig. 24.1). The first factor (γGδ)2 on the right side corresponds to the square of the proton-spin spatial frequency q. The frequency q characterises the distance-dependent proton spin shift generated by the 1st MPG (Fig.  24.1). The second factor, (Δ − δ/3), is often interpreted as the period of time, called the diffusion-time t, over which water diffusion is allowed to occur before making an observation. Unlike the relaxation times T1 and T2, which are intrinsic tissue-­specific parameters, the diffusion time is a measurement parameter that can be controlled by the user. Therefore, it is in the same category as TR and TE. An explanation of why the diffusion time is equal to (Δ − δ/3) for the PGSE sequence has been omitted here. If interested, please see a reliable textbook [1]. Using the definitions of q and t, Eq. (24.1) may be rewritten as,

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24.1.2 Gaussian or Free Diffusion Hardware developments have made it possible to vary q and t to some extent in practice. Potentially this makes it possible to obtain more subtle information about in vivo structures. Although we will describe some cases where the signal attenuation differs depending on the specific values of t and q even if the b-value is the same, we first want to introduce the concept of Gaussian or free diffusion. It is well known that the diffusion coefficient D is proportional to the mean square of the ­particle displacement () per unit time D µ x2 /t .

(24.3) As shown in Fig.  24.2a (blue line), if water can diffuse freely (i.e. molecular motion is unrestricted), then plotting the mean displacement against diffusion-time will produce a straight line



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of constant slope equal to the diffusion coefficient. In this situation a measurement will always produce the same result for the same b-value regardless of whether q and t have been varied. That is, all signal attenuation can be attributed to the b-value as a controlling parameter and the signal decays monoexponentially.

24.1.3 Restricted Diffusion 24.1.3.1 b  -Value-Dependent Signal with Constant Diffusion Time Free diffusion is rarely found in  vivo except in the CSF, which is primarily water. Figure 24.3 presents DWI measurements on an array of parallel cylindrical capillaries of diameter 20 μm in a glass plate sitting in water

Fig. 24.2 (a) Mean square displacement plotted against diffusion-time. Blue line, Gaussian or free diffusion case; red line, restricted diffusion. For the restricted diffusion case, as the ADC is equal to the slope of the dashed lines, it is clear that the ADC decreases at longer diffusion-time. (b) Schematic illustrating the restriction of water motion for increasing diffusion time

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(Fig. 24.3a) [2]. This phantom can be used as a model of intracellular diffusion in an axon-like space. Here, the MPG is applied perpendicular to the axis of the cylindrical capillaries. For such a system, at long diffusion-time will asymptotically approach a constant value that is dependent on the size and shape of the watercontaining space (red line, Fig.  24.2a). In this case, rather than being constant, the diffusion coefficient estimated with Eq. (24.3) will decrease with increased diffusion time. This means that plots of the logarithmic DWI signal vs. b-value will appear to be higher for longer diffusion times, with the slope of the plot reflecting the t-dependent ADC (Fig.  24.3b). It is expected that similar signal behaviour might be observed for the movement of water in cells if the cell membrane acts like a barrier.

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varying q with constant Δ. The signal appears to be higher for longer Δ. (c) The difference between the data sets is less obvious when plotted against q alone

This phantom experiment also exhibits non-­ monoexponential b-value-dependent signal change. As noted in Sect. 24.1.2, free water signal attenuation is independent of diffusion time and displays single exponential behavior vs. b-value. However, in vivo signal behavior is more complicated, and requires an alternative signal description (e.g., double exponential). It is interesting that the b-value-dependent logarithmic signal change observed for the glass plate phantom is neither linear nor downwardly convex (Fig. 24.3b), which is clearly different to the signal behaviour usually observed with in vivo DWI. For more information about this phenomenon please see [3]. It should also be noted that the DWI signal at long diffusion-time is mainly determined by q. For example, when the experimental data of Fig.  24.3b is plotted against q alone, the signal curves become more and more similar with increasing diffusion-time (Fig.  24.3c). q-space imaging (QSI) is one technique that tries to utilize this phenomenon to characterize/qualify microstructure. However, QSI does not work very well for inhomogeneous structures like in vivo tissue.

the b-value is plotted on the horizontal axis the signal asymptotically approaches a q-­dependent constant value (red dot-dashed line in Fig. 24.4). On the other hand, at low b-values, corresponding to short diffusion-times, the signal approaches the free water limit (dashed line in Fig. 24.4). This is easy to interpret. At very short diffusion-times the probability that a water molecules will collide with a barrier is very low, so the signal behaves almost like free water. In summary, for free diffusion the logarithmic signal change is linear as a function of b-value (dashed line in Fig. 24.4). However, in an environment where water movement is limited by barriers, the b-value-dependent signal change can be quite different. This is because, the DWI signal actually depends on the individual values of q and t even if the same b-value is used. Nevertheless, this type of signal behaviour is not easily observed in clinical imaging because in vivo microstructure is far more complex than the phantom example discussed here.

24.1.3.2 b  -Value-Dependent Signal with Constant q Next, we will discuss an example where diffusiontime is varied while q is kept constant (red open circles and solid line, Fig.  24.4). As mentioned above, the DWI signal at long diffusion-time is mainly determined by q, which means that when

24.2 In Vivo Application of Diffusion-Time-­ Dependent DWI 24.2.1 Shortening the Diffusion-Time Here, we would like to talk about DWI signal attenuation when the diffusion-time is set to be shorter than the usual clinical setting. While this

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Fig. 24.4  b-Value-dependent signal change in the glass capillary plate with either q or t constant. The red open circles and corresponding solid line represent the measured and simulated signal changes, respectively, for a

constant q. The red dot-dashed line is the asymptote of the red solid line as b → ∞; the red dashed line is the signal for free water. The blue open circles and solid line are the measured and simulated signals, respectively, for a constant t

is a promising way to investigate complex in vivo microstructure, it also pushes the gradient hardware towards its physical limit. Since the b-value is proportional to q2 and diffusion-time for PGSE, the maximum amplitude and slew rate of the gradient hardware will limit the minimum diffusion-time that can be reached while keeping the same b-value. One workaround for this problem is to use multiple MPG pairs with a relatively short, but practically achievable separation. When inserted into a spin-echo sequence, this multi-MPG-pair waveform provides an example of the oscillating-gradient spin-echo (OGSE) method. In using this method it is common to report the ADC as a function of the frequency of the MPG pairs instead of the diffusion-time (Fig.  24.5). Observations of in vivo tissue find

that the ADC is linearly dependent on frequency, and the slope of this dependence might be used to characterise tissue microstructure. Please refer to [4] for a more comprehensive introduction to the OGSE method. It is anticipated that biological application of the OGSE method can be used to obtain ­information about tissue structures. Experiments with animals have reported that the ADC in the cerebellum is frequency-dependent [5]. The granular cell layer seems to be particularly sensitive to frequency variation (Fig. 24.6), and it has been suggested that this is due to the relatively large nuclear volume of granular cells [6]. It has also been predicted that mesoscopic structural complexity can be characterized by observing the frequency dependence of the ADC [7, 8].

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882 Fig. 24.5 The waveform (g), time integral (f), and spectra (F) corresponding to the MPGs of the pulsedgradient spin-echo (PGSE), sine- (SOGSE) and cosine- (COGSE) oscillating-gradient spin-echo sequences. A special characteristic of the COGSE MPG is that it does not sample the central frequencies that dominate the SOGSE and PGSE spectra. Reproduced with permission from [4]. *Permission not yet received

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The number of neuroradiology studies using OGSE has recently been increasing. Investigations of epidermoid cyst [9] and choroid plexus cyst [10] (Fig.  24.7), revealed that the OGSEmeasured ADC increased at shorter diffusiontime, indicating the spatial restriction of water diffusion inside those structures. The high DWI signal of these structures is not solely due to the T2 shine-through effect. Previous research on mouse glioblastoma models using OGSE showed that the ADC at short diffusion-time was significantly higher in tumor after radiation therapy, while PGSE failed to detect such changes [11]. This result indicates that using shorter diffusiontimes increases the sensitivity of DWI to subtle changes in cellular microstructure. The utility of

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OGSE in the differential diagnosis of malignant and benign head and neck tumors has also been demonstrated in humans [12]. OGSE may therefore aid the diagnosis and quantitative evaluation of treatment response for tumors in the future. The clinical relevance of diffusion-time also applies to cerebral infarction. It has been demonstrated that acute cerebral infarction displays low contrast on short diffusion-time DWI (Fig. 24.8) [13–15]. Technical advances in gradient hardware that allow higher maximum gradient and slew rate may introduce an unexpected reduction in the diffusion-time and cause critical transformation of the contrast in clinical images. Hence, it is important for clinicians to understand how ­diffusion-time can affect DWI.

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24.2.2 Increasing the Diffusion-Time What happens if the diffusion-time is made longer than the usual clinical setting? As shown in Fig. 24.4, if in vivo water is surrounded by impermeable barriers, the DWI signal will reach a nonzero lower limit as diffusion-time is increased (provided q is constant). As a result, when the diffusion-time increases, the measured diffusion coefficient (proportional to /t) approaches zero. However, when the diffusion-time is extended in real observations, it is found that the in vivo

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signal is attenuated and the ADC approaches a nonzero constant value. There are two main reasons for this. One is the existence of a space that allows water to diffuse long distances through the tissue. For long diffusion-times, for the water in this space is approximately proportional to the diffusion-time, which means that the diffusion coefficient asymptotically approaches a nonzero constant value. This is sometimes called extracellular or hindered diffusion. Figure  24.9 shows experimental results from a phantom containing hydrophobic fiber bundles as a model of extracellular diffusion. The time-­dependent signal changes in the experiment are small compared to those observed for the capillary phantom (Fig.  24.3), which indicates that diffusion was hindered rather than fully restricted. If in  vivo DWI signal can be modelled as the sum of intracellular and extracellular signal, the b-value-dependent signal curves (each acquired with a constant t) should be either constant or increase at longer t. In the intracellular and extracellular signal models described so far, b-value-dependent signal curves tend to increase with t for the former (Fig. 24.3), but the signal curves are less sensitive to t for the latter (Fig. 24.9). However, when measurements are taken on pathological tissue, there are certain observations that cannot be explained by the interpretations that have been discussed in this section. For example, in reports of cerebral infarction, the t-dependent signal decreases as t increases [16]. This suggests that some mechanism other than restricted or hindered diffusion must be occurring. A possible explanation for the decrease is that water exchange between the intra- and extracellular spaces makes some contribution to the DWI ­signal. In fact, this has been investigated in a report that compared optical one-cell imaging signals with DWI to validate the inter-compartmental water exchange-time measured for expression-­ controlled aquaporin-4 (AQP4) cells. It was shown that the t-dependent signal attenuation increases more for the high-permeability (AQP4 high expression) cells than for

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DWI at short t. (c, d) In comparison to the long t result, the ADCs of the choroid plexus cysts are higher at short t. Figure reproduced with permission from the report by Maekawa et al. [10]. *Permission not yet received

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mm2, diffusion-time  =  47.3  ms), (d) PGSE DWI (b-value = 1000 s/mm2, diffusion-time = 22.3 ms) obtained 2 days after the image in Fig. a. Figure reproduced with permission from the report by Hori et al. [13]. *Permission not yet received

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the low-­ permeability (no AQP4 expression) cells (Fig. 24.10). Many other concepts for investigating the tissue microstructure utilising the relationship between diffusion-time and DWI signal have been proposed [18–23].

24.3 Extracting Microstructural Information from DWI The last section of this chapter is devoted to microstructural imaging of the brain and spinal cord using multi-shell DWI. A variety of DWI

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techniques, such as diffusion tensor imaging, diffusional kurtosis imaging, and q-space imaging, have been used to extract in vivo microstructural information. However, the metrics estimated with these techniques are not specific to microstructural features such as axon density and diameter. Hence, the relationship between observed changes in these metrics and pathology is often difficult to explain. In contrast, it is thought that DWI-based modeling of microstructural features will provide more specific information. We discuss the basics, limitations, and clinical applications of microstructural modeling for DWI.

24.3.1 Basics and Limitations To extract microstructural information, microstructural modelling assumes at least two compartments in the brain, namely, intra-neurite and extra-neurite compartments. Some models assume an additional isotropic free water compartment, but the signal from water inside the tightly packed myelin sheath is usually ignored because the T2 is very short. The most widely used method for estimating microstructural features from DWI data is the neurite orientation dispersion and density imaging (NODDI) model. NODDI assumes 3 compartments: a restricted intracellular compartment (neurite density index, NDI), a hindered extracellular compartment, and CSF with free diffusion [24]. Neurites are composed of either an axon or a dendrite and are modelled as zero-radius sticks. NODDI provides information about the orientation of neurites through the orientation dispersion index (ODI). In contrast to DTI, which is sensitive to partialvolume averaging of fiber orientations, NODDI incorporates curving and fanning of fibers into the model so it is considered to be more robust for estimating the volume of axons [25]. The g-ratio, which is the ratio of the inner to the outer diameter of a myelinated axon, is associated with the speed of conduction [26]. Larger axons and thicker myelin sheaths give rise to faster conduction of electrochemical information, but there is always a trade-off between these dimensions because of the limited space in the nervous system. The g-ratio has a limited

dynamic range in healthy WM and depends on age and region. Combining DWI modelling and myelin imaging, we can estimate the g-ratio and axon volume fraction (AVF) in a voxel [25]. The g-ratio can differentiate between demyelination and axonal degeneration, whereas conventional T2-weighted or FLAIR images cannot. However, modelling has some limitations. To obtain microstructural information at a scale of μm from MRI data on a scale of mm resolution in a feasible scan time, strict assumptions, which may not be true in pathological states, are required to solve complex mathematical equations. Further, modelling usually targets white matter in the brain; suitable methods for the gray matter of the brain and spinal cord still need to be developed.

24.3.2 Clinical Applications Techniques for microstructural modelling of DWI data, especially NODDI, have been widely applied for healthy populations and patients. Two recent studies have shown that the NDI calculated using NODDI has a stronger relationship with age and is more sensitive to microstructural changes in late childhood and adolescence than DTI metrics [27, 28]. Granberg et al. [29] found that the normal-appearing white matter (NAWM) in patients with multiple sclerosis (MS) had significantly lower NDI than healthy controls. However, Hagiwara et  al. [30] reported that, relative to NAWM, the myelin volume fraction estimated with synthetic MRI was more sensitive than the AVF, calculated using a combination of synthetic MRI and NODDI, to damage in plaque and periplaque WM (Fig.  24.11). Kamagata et  al. [31] investigated the diagnostic utility of DTI and NODDI in the substantia nigra pars compacta and striatum in Parkinson’s disease. Multivariate logistic analysis revealed that NDI and mean diffusivity in the contralateral substantia nigra pars compacta are independent predictors of Parkinson’s disease. From ROC analysis, NDI in the contralateral substantia nigra pars compacta had the best diagnostic performance (mean cutoff, 0.62; sensitivity, 0.88; specificity, 0.83) among all DTI and NODDI metrics. In a separate study, Kamagata et al. also showed that mean kurtosis

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Fig. 24.11  Representative images from a patient with multiple sclerosis. T2WI (a) and maps of the myelin volume fraction (b), axon volume fraction (c), and g-ratio (d) are shown. Two plaques indicated by arrows appear in these images. Even though myelin is severely damaged in these plaques (b, 5.53% and 7.23%), the degrees of axon

damage are milder (c, 31.30% and 22.95%). Because myelin is severely damaged in these plaques, the corresponding g-ratios are close to 1.00 (d, 0.94 and 0.91). Figures reproduced with permission from the report by Hagiwara et al. [30]. *Permission not yet received

and NDI in the GM were the most accurate biomarkers of Parkinson’s disease amongst all the DTI, diffusional kurtosis imaging, and NODDI metrics [32]. Kamiya et al. [33] and Irie et al. [34] investigated the utility of NODDI for patients with idiopathic normal pressure hydrocephalus and showed that ODI and NDI in the corticospinal tract were significantly lower than healthy controls, suggesting that ODI can capture the reversible straightening of nerve fibers, whereas decreased NDI may indicate irreversible microstructural damage.

Even though NODDI was originally developed for the brain, it has also been applied to and validated within the spinal cord. For example, Grussu et  al. [35] characterized differences between spinal cord WM and GM using NODDI and demonstrated that NODDI is superior to DTI in terms of fitting and reproducibility. They also histologically validated the ODI measured with NODDI for characterizing MS lesions [36]. Schilling et al. [37] showed the normative values of DTI and NODDI metrics in the spinal cord, and revealed that DTI lacks specificity. However,

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they cautioned that NODDI indices in the spinal cord are not as strongly associated with the histology as previously reported for the brain. Hori et al. [38] investigated the utility of microstructural modelling using a combination of NODDI and myelin imaging for patients with cervical spondylotic myelopathy. They revealed that in patients the AVF of some tracts was lower than that in healthy controls, whereas myelin volume fraction showed no difference between patients and healthy controls. The g-ratio of lateral corticospinal tracts was lower in the patients than in the healthy controls. It was therefore concluded that the pathological microstructural changes in the spinal cord of patients with cervical spondylotic myelopathy may be partial axonal degenerations with preserved myelin.

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In this chapter, some fundamental ideas related to diffusion-time-dependent DWI have been introduced. The parameter most widely used to control DWI signal attenuation is the b-value, which is calculated as the product of the spatial frequency q squared and diffusion-time t. However, in some circumstances the DWI signal may vary depending on the specific values of q and t even if the b-value is the same. Although this is not used in current clinical diagnosis, technological improvements and increased awareness within the medical field may lead to important clinical applications in the future. Also included in this chapter was a brief introduction to some of the methods used to extract microstructural information from DWI data. We are optimistic that further development will produce new and more accurate techniques for the diagnosis of pathological states in the brain and spinal cord.

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15. Boonrod A, Hagiwara A, Hori M, Fukunaga I, Andica C, Maekawa T, Aoki S (2018) Reduced visualization of cerebral infarction on diffusion-weighted images with short diffusion times. Neuroradiology 60(9):979–982 16. Latt J, Nilsson M, van Westen D, Wirestam R, Stahlberg F, Brockstedt S (2009) Diffusion-weighted MRI measurements on stroke patients reveal water-­ exchange mechanisms in sub-acute ischaemic lesions. NMR Biomed 22(6):619–628 17. Obata T, Kershaw J, Tachibana Y, Miyauchi T, Abe Y, Shibata S, Kawaguchi H, Ikoma Y, Takuwa H, Aoki I, Yasui M (2018) Comparison of diffusion-weighted MRI and anti-Stokes Raman scattering (CARS) measurements of the inter-compartmental exchange-time of water in expression-controlled aquaporin-4 cells. Sci Rep 8(1):17954 18. Fieremans E, Novikov DS, Jensen JH, Helpern JA (2010) Monte Carlo study of a two-­ compartment exchange model of diffusion. NMR Biomed 23(7):711–724 19. Li H, Jiang X, Xie J, McIntyre JO, Gore JC, Xu J (2016) Time-dependent influence of cell membrane permeability on MR diffusion measurements. Magn Reson Med 75(5):1927–1934 20. Nilsson M, Latt J, van Westen D, Brockstedt S, Lasic S, Stahlberg F, Topgaard D (2013) Noninvasive mapping of water diffusional exchange in the human brain using filter-exchange imaging. Magn Reson Med 69(6):1573–1581 21. Ozarslan E, Shepherd TM, Koay CG, Blackband SJ, Basser PJ (2012) Temporal scaling characteristics of diffusion as a new MRI contrast: findings in rat hippocampus. NeuroImage 60(2):1380–1393 22. Roth Y, Ocherashvilli A, Daniels D, Ruiz-Cabello J, Maier SE, Orenstein A, Mardor Y (2008) Quantification of water compartmentation in cell suspensions by diffusion-weighted and T(2)-weighted MRI. Magn Reson Imaging 26(1):88–102 23. Thelwall PE, Shepherd TM, Stanisz GJ, Blackband SJ (2006) Effects of temperature and aldehyde fixation on tissue water diffusion properties, studied in an erythrocyte ghost tissue model. Magn Reson Med 56(2):282–289 24. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4):1000–1016 25. Stikov N, Campbell JS, Stroh T, Lavelee M, Frey S, Novek J, Nuara S, Ho MK, Bedell BJ, Dougherty RF, Leppert IR, Boudreau M, Narayanan S, Duval T, Cohen-Adad J, Picard PA, Gasecka A, Cote D, Pike GB (2015) In vivo histology of the myelin g-ratio with magnetic resonance imaging. NeuroImage 118:397–405 26. Rushton WA (1951) A theory of the effects of fibre size in medullated nerve. J Physiol 115(1):101–122 27. Genc S, Malpas CB, Holland SK, Beare R, Silk TJ (2017) Neurite density index is sensitive to age related differences in the developing brain. NeuroImage 148:373–380 28. Mah A, Geeraert B, Lebel C (2017) Detailing neuroanatomical development in late childhood

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25.1 C  urrent State of Diffusion Imaging in the Head and Neck While diffusion-weighted imaging (DWI) has been present in clinical practice for more than a decade, its primary use in neuroradiology has centered around its utility in brain pathologies including acute ischemic stroke, abscesses, and tumors. Multiple studies that have focused on DWI in the head and neck over the past 15 years or so have been crucial in increasing our understanding of the clinical applications of DWI in head and neck pathologies. The advent of Echo Planar Imaging (EPI) and intravoxel incoherent motion (IVIM) has allowed for the rapid acquisition necessary to collect DWI data with a significant reduction in motion artifact. With improved imaging technology and advances in MRI sequences, it has become possible to tackle the inherent challenges in applying DWI in the head and neck that include geometric contour variations, motion artifact from pulsating vessels and physiological maneuvers such as swallowing and susceptibility artifacts from air-soft tissue interfaces.

A. Srinivasan (*) Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected]

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For the most part, the different applications of DWI in head and neck imaging have been attributed to its ability to serve as a surrogate marker for tissue cellularity. Apparent diffusion coefficient (ADC) derived from DWI is a quantitative metric measuring the freedom of water molecular motion and has been demonstrated to have an inverse correlation with cellularity; for example, higher ADC values for a specific voxel correlate with a lower cellularity in that voxel [1] (Figs. 25.1, 25.2, 25.3, and 25.4). However, there are other tissue factors that determine DWI signal and ADC that could play a role in influencing the ADC and the interpretation of the images. Some non-tumor causes of reduced ADC include increased viscosity and presence of macromolecules (increased protein content of inspissated secretions). In this chapter, we will look at the current state of techniques and clinical applications of DWI in head and neck imaging, and discuss the future state.

25.2 Techniques: Current and Future

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While there are a number of techniques available for acquiring DWI, routine head and neck DWI continues to employ EPI and is typically acquired as a single shot technique with 2 or 3 b values due to simplicity of operation and shorter

© Springer Nature Switzerland AG 2021 T. Moritani, A. A. Capizzano (eds.), Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine, https://doi.org/10.1007/978-3-030-62120-9_25

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Fig. 25.1 (a–d) Axial T2-weighted (a) and post-­ gadolinum T1-weighted (b) images in a 42-year-old female demonstrate a T2 hyperintense, enhancing lesion in the right carotid sheath that was proven to be a schwan-

noma. Corresponding trace DWI image (c) and ADC map (d) show the DWI signal intensity to be lower than the spinal cord and the ADC higher, seen typically with benign lesions in the head and neck

acquisition time. Non-EPI techniques that take longer to acquire are usually employed in regions such as skull base and temporal bone where significant susceptibility artifacts can degrade image quality [2].

25.2.1 EPI Diffusion The main advantages of EPI diffusion are its short acquisition time and relative insensitivity to motion. This makes it attractive for its applica-

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Fig. 25.2 (a–c) Axial T2-weighted (a) and post-­ gadolinum T1-weighted (b) images in a 74-year-old male demonstrate a heterogeneous enhancing lesion in the left external auditory canal that was proven to be squamous

cell carcinoma. Corresponding trace DWI image (c) shows high signal intensity similar to that observed in the brainstem, seen typically in hypercellular lesions such as malignancies

tion in routine head and neck imaging. This can be performed either as a single-shot technique where the entire K-space is filled with one TR or multi-shot where multiple TRs are used. The latter technique, while longer in acquisition compared to single-shot technique, has the benefit of reduced susceptibility effects and geometric distortion and improved spatial resolution.

25.2.2 Non-EPI Diffusion (TSE Diffusion) While multi-shot EPI diffusion can reduce the susceptibility artifacts associated with EPI diffusion, non-EPI techniques can provide a further reduction in artifacts, increase spatial resolution, thereby providing an overall improvement in

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Fig. 25.3 (a–c) Axial post-gadolinum T1-weighted (a) image in a 63-year-old male with a history of previously treated adenocarcinoma in the nasal cavities demonstrates an enhancing posttreatment mass at the site of the treated

primary cancer. Corresponding trace DWI image (b) and ADC map (c) show high DWI signal intensity and low ADC, suggesting hypercellularity. Final pathology was recurrent adenocarcinoma

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b

Fig. 25.4 (a, b) Axial post-gadolinum T1-weighted (a) image in a 50-year-old male with treated squamous cell carcinoma of the mandibular gingiva (oral cavity) demonstrates asymmetric mass-like enhancement within the right masticator space. Corresponding trace DWI image

(b) shows no areas of high signal intensity that would be suggestive of a hypercellular process. Multiple biopsies revealed only benign granulation tissue with no evidence of recurrent malignancy

image quality. They do have a relatively lower signal-to-noise ratio (SNR) that necessitates ­multiple averages for better SNR and prolongs scanning time.

including “f” representing the perfusion fraction (or the contribution of water moving in capillaries), “D” representing the tissue diffusion coefficient, and “D*” representing the pseudodiffusion coefficient (or diffusion within the microcirculation). Above b values of 200 s/mm2, the contribution of D* is negligible since it is greater than D by several orders of magnitude [3–5].

25.2.3 Intravoxel Incoherent Motion It is important to realize that ADC values are influenced by the choice of b values, a phenomenon partly explained in the work by Le Bihan et al. that demonstrated that the water molecular motion contributing to DWI signal arose from different compartments: extracellular space diffusion, intracellular space diffusion, and intravascular space diffusion (or perfusion) [3]. Intravoxel incoherent motion (IVIM) imaging enables separation of water molecular diffusion from perfusion in the microcirculation from that due to diffusion in the extravascular space. Using this technique, multiple parameters can be generated

25.2.4 Reproducibility of ADC Values In a study by Kolff-Gart et al., the authors evaluated the reproducibility of ADC values in the head and neck region in healthy subjects and attempted to identify the most suitable reference tissue [6]. They studied 7 subjects with both EPI and TSE sequences on 5 different MR imaging systems at 3 time points in 2 institutions. Mean ADC values for different tissues (submandibular gland, sternocleidomastoid muscle, spinal cord,

100 101 102 103 104 105 106 107

108 109 110 111 112 113 114 115 116 117

A. Srinivasan

896 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146

147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162

subdigastric lymph node, and tonsil) were evaluated for intra- and intersubject, intersystem, and intersequence variability. Their results showed that the ADC values derived from EPI-DWI with 2 b-values and calculated EPI-DWI with 2 b-­values extracted from EPI-DWI with 6 b-­values did not differ significantly with the least standard error of measurement seen in the tonsil and spinal cord. They also noted that the intersystem difference for mean ADC values and the influence of the MR imaging system on ADC values among the subjects were statistically significant. Based on their results, they concluded that the spinal cord was the most appropriate reference tissue and that EPI-DWI with 6 b-values was the most reproducible sequence [6]. This study shows that quantitative ADC measurements performed as part of clinical care would be more accurate if the patients were scanned on the same MR imaging system and with the same sequence. ADC values differed significantly between MR imaging systems and sequences. If this is not possible for various reasons, the next best option would be to use the spinal cord as an internal standard. This was also corroborated in another study by Koontz et  al. who were able to successfully use ADC ratios between tumor tissue and spinal cord to differentiate benign and malignant head and neck lesions [7].

25.2.5 Enhanced Dual-Stage Correlated Diffusion Imaging Correlated diffusion imaging (CDI) is a new form of diffusion imaging which accounts for the joint correlation of diffusion signal attenuation across multiple gradient pulse strengths and timings in order to improve the separability of cancerous and healthy tissues [8]. However, CDI does not capture anatomical information. Dualstage CDI (D-CDI) is a recent version of CDI where an additional signal mixing stage between the correlated diffusion signal from the first signal mixing stage (CDI) and an auxiliary diffusion signal is performed to incorporate anatomical context. This technique has been evaluated so far in prostate cancer and found to

be beneficial over T2-weighted images and DWI used in the form of ADC maps; future studies in the head and neck may prove its utility too since the basic principles of cancer detection and characterization would be similar across different tissues [8].

25.3 Clinical Applications 25.3.1 Benign Versus Malignant Lesions While pathological confirmation using tissue biopsy is the gold standard for differentiating benign and malignant lesions in the head and neck, there are deeper anatomical locations that may not be accessible for a biopsy. Furthermore, biopsies themselves are not zero risk procedures, suggesting the need for robust noninvasive techniques to predict presence of malignancy. Multiple studies have demonstrated the ability and reliability of DWI in characterizing the malignant potential of a head and neck lesion. Based on the hypothesis that malignant tumors demonstrate lower ADC values compared to benign tumors due to their relatively higher cellularity, Wang et  al. demonstrated that benign and malignant lesions yielded significantly different ADC values, and using an ADC value of 1.22 × 10−3 mm2/s, the authors were able to categorize lesions as benign (above the cutoff) or malignant (below), with an accuracy of 86%, sensitivity of 84%, and specificity of 91% [9]. Other authors have also shown this utility using different MRI systems and magnet strengths. Razek et al. demonstrated an apparent diffusion coefficient cutoff value of 1.15  ×  10−3  mm2/s yielded a sensitivity of 95%, a specificity of 91%, and an accuracy of 93% [10]. Similarly, Srinivasan et al. compared 33 patients with head and neck masses on a 3T magnet and found that there was a statistically significant difference between benign and malignant lesions with the latter showing lower ADC values than the former. In their study, the threshold value of 1.3  ×  10−3  mm2/s provided the best differentiation [11].

163 164 165 166 167 168

169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206

25  Diffusion Imaging of the Head and Neck in the Future 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232

233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251

Since absolute ADC values vary based on the magnet strength, manufacturer, and number of b values, it would be better to use an internal reference standard such as the adjacent brainstem or spinal cord to determine whether a lesion may be benign or malignant. If the clinical workflow does not permit quantitative ROI measurements, a qualitative assessment may suffice in most instances; lesions that appear darker on the ADC map than the brainstem/spinal cord can be expected to have higher cellularity and hence more likely in the malignant spectrum. Exceptions to this rule of thumb would be the presence of high protein content within inspissated secretions and benign lesions with high cellularity such as paragangliomas. In a few scenarios, DWI can help distinguish different malignant tumors from one another. Dreissen et  al. demonstrated that positive HPV status correlates with low mean ADC when compared with non-HPV squamous cell cancers of the oropharynx [12]. Given the significant differences in treatment response of HPV+ and HPV− oropharyngeal cancers, this differentiation may lead to more patient focused treatment options based on MRI findings.

25.3.2 Prediction and Monitoring of Treatment Response Predicting a tumor’s response to treatment using noninvasive imaging based biomarkers can be helpful to tailor individualized management options. To this effect, many DWI based biomarkers have been studied by different investigators with variable results. Guo et  al. found a notably lower pretreatment ADC value, and D value (IVIM derived bi-exponential diffusion coefficient) in responders compared to non-­ responders as well as a higher posttreatment ADC, and concluded that lower pretreatment ADC value correlated with therapy response [4]. In another study by Kim et  al., the authors showed that complete responders had significantly lower pretreatment ADC values than partial responders; furthermore, pretreatment ADC value achieved 65% sensitivity and 86% speci-

897

ficity in predicting response [13]. This was also corroborated in a separate study by Srinivasan et al. who showed that a histographic analysis of ADC values may be of benefit as well; in their study, lesions with lower pretreatment ADC and more than 45% of their volume below a threshold of 1.15 × 10−3 mm2/s had better response to chemoradiation 2 years after treatment [14]. Other metrics apart from ADC may also have utility in predicting response. Using IVIM in metastatic lymph nodes, Hauser et  al. showed that an elevated perfusion fraction (or f-value) at baseline examination may predict locoregional failure after treatment [5]. However, the clinical applicability of DWI in this field is not settled yet. Contrasting results have been reported in other studies; King et  al. failed to demonstrate a significant correlation between pretreatment ADC and treatment response [15]. Having a reliable way to assess treatment response during early stages and at the end of therapy is important for making management decisions on continuing therapy and for prognostication. ADC has been investigated as a potential biomarker for evaluating early therapy response. In a study by Paudyal et  al., the authors used IVIM technique and found a significant difference in change in D (bi-exponential diffusion coefficient) between complete responders and incomplete responders, and suggested that with more research, MRI may not only be able to assess response, but also characterize its magnitude to allow for individualized treatment options [16]. Other authors have also shown similar results, thereby suggesting that ADC can be a valuable metric for this application.

25.3.3 Recurrence Versus Posttreatment Changes Based on the hypothesis that recurrent malignancies would demonstrate higher cellularity and therefore lower ADC compared to benign posttreatment masses, multiple authors have shown the utility of ADC for this differentiation.

252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289

290 291 292 293 294 295 296

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329

Vaid et al. examined 80 treated head and neck cancer patients and were able to show that recurrent malignancy demonstrated decreased ADC values when compared to posttreatment changes [17]. Using a threshold ADC value of 1.2 × 10−3 mm2/s, they achieved 90.13% sensitivity and 82.5% specificity for this differentiation. In a study of 50 laryngeal cancer patients who underwent either surgery or chemoradiation, Desouky et al. compared DWI and dynamic contrast enhanced imaging and demonstrated that an ADC threshold value of 0.9667 × 10−3 mm2/s differentiated benign and malignant lesions with 100% sensitivity, and 74.2% specificity [18]. Vandacaveye et al. evaluated 26 patients with recurrent head and neck squamous cell cancer and showed that the signal was significantly higher for HNSCC than for nontumoral tissue on native b-1000 images, with a 71.6% sensitivity and 71.3% specificity [19]. In this study, differentiation based on ADC values (lower for HNSCC than for benign tissue) achieved a 94.6% sensitivity and 95.9% specificity. In addition, DWI detected nodal metastases smaller than 1 cm, and gave fewer false positive results in both the original tumor bed and in residual adenopathies, when compared with CT, TSEMRI, and FDG-­PET.  While ADC is often utilized in clinical practice for differentiating recurrence from posttreatment changes, the lack of a consensus on well-defined ADC values and thresholds makes it difficult to compare results across institutions.

330

25.4 What the Future Holds

297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328

331 332 333 334 335 336 337 338 339 340 341

While DWI has shown utility in multiple head and neck applications, the major obstacle in its routine integration in day-to-day clinical practice has been the lack of standardization of protocols and well-defined threshold values for quantitative analysis. This can be overcome to some extent if ADC values can be standardized against an internal structure such as the brain/spinal cord or by using qualitative analysis. In addition, there are issues with susceptibility artifacts and image distortion that can prevent appropriate assessment of

head and neck tissues but this can be overcome to a large degree by utilizing parallel imaging, reducing echo train length and by adopting technical improvements such as reduced field of view diffusion imaging [20]. However, it is important to remember that DWI alone may not be the answer to the problem in many instances and hence the proper integration of DWI with conventional imaging information and other advanced techniques such as perfusion are key to a better understanding of pathologies in the head and neck.

342 343 344 345 346 347 348 349 350 351 352 353

References

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1. Chen L, Liu M, Bao J, Xia Y, Zhang J, Zhang L et al (2013) The correlation between apparent diffusion coefficient and tumor cellularity in patients: a meta-­ analysis. PLoS One 8(11):e79008 2. Schakel T, Hoogduin JM, Terhaard CH, Philippens ME (2013) Diffusion weighted MRI in head-and-­ neck cancer: geometrical accuracy. Radiother Oncol 109(3):394–397 3. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161(2):401–407 4. Guo W, Luo D, Lin M, Wu B, Li L, Zhao Y et al (2016) Pretreatment intra-voxel incoherent motion diffusion-­ weighted imaging (IVIM-DWI) in predicting induction chemotherapy response in  locally advanced hypopharyngeal carcinoma. Medicine 95(10):e3039 5. Hauser T, Essig M, Jensen A, Laun FB, Munter M, Maier-Hein KH et  al (2014) Prediction of treatment response in head and neck carcinomas using IVIM-­ DWI: evaluation of lymph node metastasis. Eur J Radiol 83(5):783–787 6. Kolff-Gart AS, Pouwels PJW, Noij DP, Ljumanovic R, Vandecaveye V, de Keyzer F, de Bree R, de Graaf P, Knol DL, Castelijns JA (2015) Diffusion-weighted imaging of the head and neck in healthy subjects: reproducibility of ADC values in different MRI systems and repeat sessions. AJNR 36(2):384–390 7. Koontz NA, Wiggins RH 3rd (2017) Differentiation of Benign and Malignant head and neck lesions with diffusion tensor imaging and DWI.  AJR 208(5):1110–1115 8. Khalvati F, Zhang J, Haider MA, Wong A (2016) Enhanced dual-stage correlated diffusion imaging. Conf Proc IEEE Eng Med Biol Soc 2016:5537–5540 9. Wang J, Takashima S, Takayama F, Kawakami S, Saito A, Matsushita T et  al (2001) Head and neck lesions: characterization with diffusion-weighted echo-planar MR imaging. Radiology 220(3):621–630

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25  Diffusion Imaging of the Head and Neck in the Future 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424

10. Razek AA, Elkhamary S, Mousa A (2011) Differentiation between benign and malignant orbital tumors at 3-T diffusion MR-imaging. Neuroradiology 53(7):517–522 11. Srinivasan A, Dvorak R, Perni K, Rohrer S, Mukherji SK (2008) Differentiation of benign and malignant pathology in the head and neck using 3T apparent diffusion coefficient values: early experience. AJNR 29(1):40–44 12. Driessen JP, van Bemmel AJ, van Kempen PM, Janssen LM, Terhaard CH, Pameijer FA et al (2016) Correlation of human papillomavirus status with apparent diffusion coefficient of diffusion-weighted MRI in head and neck squamous cell carcinomas. Head Neck 38(Suppl 1):E613–E618 13. Kim S, Loevner L, Quon H, Sherman E, Weinstein G, Kilger A et al (2009) Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res 15(3):986–994 14. Srinivasan A, Chenevert TL, Dwamena BA, Eisbruch A, Watcharotone K, Myles JD, Mukherji SK (2012) Utility of pretreatment mean apparent diffusion coefficient and apparent diffusion coefficient histograms in prediction of outcome to chemoradiation in head and neck squamous cell carcinoma. J Comput Assist Tomogr 36(1):131–137 15. King AD, Mo FK, Yu KH, Yeung DK, Zhou H, Bhatia KS et al (2010) Squamous cell carcinoma of the head

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and neck: diffusion-weighted MR imaging for prediction and monitoring of treatment response. Eur Radiol 20(9):2213–2220 16. Paudyal R, Oh JH, Riaz N, Venigalla P, Li J, Hatzoglou V et al (2017) Intravoxel incoherent motion diffusion-­ weighted MRI during chemoradiation therapy to characterize and monitor treatment response in human papillomavirus head and neck squamous cell carcinoma. JMRI 45(4):1013–1023 17. Vaid S, Chandorkar A, Atre A, Shah D, Vaid N (2017) Differentiating recurrent tumours from post-treatment changes in head and neck cancers: does diffusion-­ weighted MRI solve the eternal dilemma? Clin Radiol 72(1):74–83 18. Desouky SE, Paudyal R, OH JH (2015) Role of dynamic contrast enhanced and diffusion weighted MRI in the differentiation between post treatment changes and recurrent laryngeal cancers. Egypt J Radiol Nucl Med 46:379–389 19. Vandecaveye V, De Keyzer F, Nuyts S, Deraedt K, Dirix P, Hamaekers P et al (2007) Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)radiotherapy: correlation between radiologic and histopathologic findings. Int J Radiat Oncol Biol Phys 67(4):960–971 20. Vidiri A, Minosse S, Piludu F, Curione D, Pichi B, Spriano G et  al (2017) Feasibility study of reduced field of view diffusion-weighted magnetic resonance imaging in head and neck tumors. Acta Radiol 58(3):292–300

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Part IV

1

How to use this book

2

26

1

How to Use This Book

2

Toshio Moritani and Per-Lennart Westesson

3 4 5 6 7 8 9 10 11 12 13

The aim of this chapter is to help the reader understand major topics and various cases of DWI in this book visually. In Tables 26.1, 26.2, 26.3, 26.4, 26.5, 26.6, and 26.7 we demonstrated DWI signal characteristics and the corresponding ADC and T2 characteristics. Each table is essentially a list of differential diagnoses. When combined with the knowledge of patient symptomatology and demographic criteria, the radiologist will be able to narrow the differential diagnosis to a few con-

90º

180º

TE/2

ditions. These tables take into account that the same condition may have variable imaging characteristics. This chapter makes direct reference to other chapters of the book, where a full description is then provided. We also demonstrated 33 representative figures/images from each chapter with the figure numbers, including color schemas, important tables and pathologies, so that the readers can go back to the text in each chapter and get more detaled information about the topics.

TE/2

RF Acquisition:

SS

Spin Warp Fast Spin-Echo

RO PE Echo

Echo Planar Imaging (EPI)

G δ

∆

25

T. Moritani (*) Division of Neuroradiology, University of Michigan, Ann Arbor, MI, USA e-mail: [email protected] P.-L. Westesson University of Rochester Medical Center, Rochester, NY, USA e-mail: [email protected] © Springer Nature Switzerland AG 2021 T. Moritani, A. A. Capizzano (eds.), Diffusion Weighted MR Imaging of the Brain, Head and Neck, and Spine, https://doi.org/10.1007/978-3-030-62120-9_26

903

14 15 16 17 18 19 20 21 22 23 24

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904 t1.1 t1.2 t1.3 t1.4

Table 26.1  Differential diagnoses for lesions with a high diffusion signal associated with low ADC and isointense T2 signal Diagnoses

t1.5 t1.6 t1.7 t1.8 t1.9 t1.10 t1.11 t1.12 t1.13 t1.14 t1.15 t1.16

Reference images DWI ADC T2WI high low iso

Infarction/hypoxia/trauma Nonaccidental head Fig. injury 19.15d Hyperacute infarction Fig. 9.1b Hypoxic ischemic Fig. encephalopathy 19.10c Toxic/metabolic Methotrexate Fig. leukoencephalopathy 15.2b

Fig. 19.15e Fig. 9.1c Fig. 19.10d

Fig. 19.15b Fig. 9.1a Fig. 19.10a

Fig. 15.2c

Fig. 15.2a

Table 26.2  Differential diagnoses for lesions with a high diffusion signal associated with iso-high ADC and a high intense T2 signal Diagnoses Degeneration Amyotrophic lateral sclerosis Demyelination Acute disseminated encephalomyelitis (ADEM) Multiple sclerosis (MS) Progressive multiple leukoencephalopathy (PML) Epilepsy Postictal encephalopathy Infarction Venous infarction Infection Subdural empyema Toxic/metabolic Marchiafava-Bignami disease Tumor Epidermoid Brain stem glioma Anaplastic astrocytoma Low-grade oligoastrocytoma Ganglioglioma Pilocytic astrocytoma Vasculitis/vasculopathy Primary angiitis of central nervous system (PACNS) Neuro-Behçet’s disease Tacrolimus neurotoxicity (PRES)

Reference images DWI high ADC iso-high

T2 high

Fig.14.18b

Fig. 14.18c

Fig. 14.18a

Fig. 13.12c Fig. 13.3c Fig. 13.21b

Fig. 13.12d Fig. 13.3d Fig. 13.21c

Fig. 13.12a Fig. 13.3a Fig. 13.21a

Fig. 12.16d

Fig. 12.16e

Fig. 12.16a

Fig. 9.7c

Fig. 9.7d

Fig. 9.7b

Fig. 16.12b

Fig. 16.12c

Fig. 16.12a

Fig. 15.8c

Fig. 15.8d

Fig. 15.8a

Fig. 16.16c Fig. 18.7d Fig. 3.2c Fig. 18.8c Fig. 18.29c Fig. 19.38b

Fig. 16.16d Fig. 18.7e Fig. 3.2d Fig. 18.8d Fig. 18.29d Fig. 19.38c

Fig. 16.16a Fig. 18.7a Fig. 3.2a Fig. 18.8a Fig. 18.29a Fig. 19.38a

Fig. 11.2c Fig. 11.9c Fig. 11.20b

Fig. 11.2d Fig. 11.9d Fig. 11.20c

Fig. 11.2a Fig. 11.9a Fig. 11.20a

t2.1 t2.2 t2.3 t2.4 t2.5 t2.6 t2.7 t2.8 t2.9 t2.10 t2.11 t2.12 t2.13 t2.14 t2.15 t2.16 t2.17 t2.18 t2.19 t2.20 t2.21 t2.22 t2.23 t2.24 t2.25 t2.26 t2.27 t2.28 t2.29

26  How to Use This Book

905

Table 26.3  Differential diagnoses for lesions with a high diffusion signal associated with a low ADC and high intense T2 signal Diagnoses

Degeneration Wallerian degeneration

Reference images DWI ADC T2 high low high Fig. Fig. Fig. 14.20c 14.20d 14.20b

Demyelination Multiple sclerosis (cytotoxic Fig. plaque) 13.4b Epilepsy Status epileptics Fig. 8.20b Postictal encephalopathy Fig. 12.17b Hematoma Fig. Late subacute hematoma 10.6c (Extracellular methemoglobin) Subdural hematoma (SDH) Fig. 10.8c Infarction/Ischemia Hyperacute reversible Fig. ischemia (2 h) 9.5b Acute infarction (24 h) Fig. 3.3d Subacute infarction (10 day) Fig. 9.3c Infection Abscess Fig. 16.1c Septic emboli Fig. 16.4b Ventriculitis Fig. 16.14c Acquired immunodeficiency Fig. syndrome (AIDS) 16.47b Aspergillosis (disseminated) Fig. 16.23b Creutzfeldt-Jakob disease Fig. (CJD) 14.13b Group B Streptococcus Fig. meningitis 16.17b Herpes simplex virus Fig. encephalitis 19.19b Toxoplasmosis (exceptional Fig. case) 8.35d Toxic/metabolic Carmofur Fig. leukoencephalopathy 15.3b Heroin-induced Fig. leukoencephalopathy 15.5b

Fig. 13.4c

Fig. 13.4a

Fig. 8.20c Fig. 12.17a

Fig. 8.20a Fig. 12.17d

Fig. 10.6e

Fig. 10.6a

Fig. 10.8e

Fig. 10.8d

Fig. 9.5c Fig. 3.3e Fig. 9.3d

Fig. 9.5a Fig. 3.3c Fig. 9.3a

Fig. 16.1d Fig. 16.4c Fig. 16.14d Fig. 16.47c Fig. 16.23c Fig. 14.13c Fig. 16.17c Fig. 19.19c Fig. 8.35c

Fig. 16.1a Fig. 16.4a Fig. 16.14b Fig. 16.47a Fig. 16.23a Fig. 14.13a Fig. 16.17a Fig. 19.19a Fig. 8.35a

Fig. 15.3c Fig. 15.5c

Fig. 15.3a Fig. 15.5a

Table 26.3 (continued) Diagnoses

Central pontine myelinolysis (CPM) Extrapontine myelinolysis (EPM) L-2-hydroxyglutaric aciduria Mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) Phenylketonuria (PKU) Trauma Nonaccidental head injury

Reference images DWI ADC T2 high low high Fig. Fig. Fig. 15.17b 15.17c 15.17a Fig. Fig. Fig. 15.18b 15.18c 15.18a Fig. Fig. Fig. 19.75b 19.75c 19.75c Fig. Fig. Fig. 15.24f 15.24e 15.24g

t3.1 t3.2 t3.3 t3.4 t3.5 t3.6 t3.7 t3.8 t3.9 t3.10 t3.11 t3.12 t3.13

Fig. Fig. Fig. 19.72b 19.72c 19.72a

Fig. 19.16b Contusion Fig. 17.20b Diffuse axonal injury (DAI) Fig. 17.2c Tumor Glioblastoma (solid) Fig. 18.13d Lymphoma Fig. 18.47d Meningioma Fig. (meningothelial) 18.38c Meningioma (atypical) Fig. 18.43c Metastasis (lung) Fig. 16.9c Primitive neuroectodermal Fig. tumor (PNET) 18.34c Fig. Radiofrequency 16.10b thalomotomy (coagulative necrosis) Vasculitis/vasculopathy Primary angiitis of central Fig. nervous system (PACNS) 11.4e Pneumococcal meningitis Fig. and vasculitis 11.10b Sickle cell disease Fig. 19.6b Hemolytic uremic syndrome Fig. (HUS) 11.25b

Fig. 19.16d Fig. 17.20c Fig. 17.2d

Fig. 19.16a Fig. 17.20a Fig. 17.2a

Fig. 18.13e Fig. 18.47e Fig. 18.38d Fig. 18.43d Fig. 16.9d Fig. 18.34d Fig. 16.10c

Fig. 18.13a Fig. 18.47a Fig. 18.38a Fig. 18.43a Fig. 16.9a Fig. 18.34a Fig. 16.10a

Fig. 11.4f Fig. 11.10c Fig. 19.6c Fig. 11.25c

Fig. 11.4b Fig. 11.10a Fig. 19.6a Fig. 11.25a

t3.14 t3.15 t3.16 t3.17 t3.18 t3.19 t3.20 t3.21 t3.22 t3.23 t3.24 t3.25 t3.26 t3.27 t3.28 t3.29 t3.30 t3.31 t3.32 t3.33 t3.34 t3.35 t3.36 t3.37 t3.38 t3.39 t3.40 t3.41 t3.42 t3.43 t3.44 t3.45 t3.46 t3.47 t3.48 t3.49 t3.50 t3.51 t3.52 t3.53 t3.54

T. Moritani and P.-L. Westesson

906 t4.1 t4.2 t4.3 t4.4

Table 26.4  Differential diagnoses for lesions with an iso diffusion signal associated with a high ADC and high intense T2 signal Diagnoses

t4.5 t4.6 t4.7 t4.8 t4.9 t4.10 t4.11 t4.12 t4.13 t4.14

Vasculitis/Vasculopathy Posterior reversible encephalopathy syndrome (PRES) Vasogenic edema (metastasis) Tacrolimus neurotoxicity

t4.15 t4.16

Postictal encephalopathy

t4.17

Metabolic disease Wernicke encephalopathy

t4.18 t4.19

t5.1 t5.2 t5.3 t5.4 t5.5 t5.6 t5.7 t5.8 t5.9 t5.10 t5.11 t5.12 t5.13 t5.14 t5.15 t5.16 t5.17 t5.18 t5.19 t5.20 t5.21 t5.22 t5.23 t5.24 t5.25 t5.26 t5.27 t5.28

Reference images DWI ADC iso high T2 high Fig. 3.4c

Fig. 3.4d

Fig. 3.4b

Fig. 3.5e Fig. 11.20b Fig. 12.14d

Fig. 3.5d Fig. 11.20c Fig. 12.14e

Fig. 3.5a Fig. 11.20a Fig. 12.14a

Fig. 15.21b

Fig. 15.21c

Fig. 15.21a

Table 26.5  Differential diagnoses for lesions with a low diffusion signal associated with a high ADC and high intense T2 signal Diagnoses

Reference images DWI ADC low high

Infarction Chronic infarction Fig. 9.4c (10 months) Tumor Glioblastoma (necrosis) Fig. 16.8c Craniopharyngioma Fig. 18.55c Metastasis (lung) Fig. 18.60c Arachnoid cyst Fig. 18.54c Vanishing white matter disease Fig. 19.83b Van der Knaap disease Fig. 19.80b Vasogenic edema (toxoplasmosis) Fig. 8.35b

T2 high

Fig. 9.4d

Fig. 9.4b

Fig. 16.8d Fig. 18.55b Fig. 18.60d

Fig. 16.8a Fig. 18.55a Fig. 18.60a

Fig. 18.54d

Fig. 18.54a

Fig. 19.83c

Fig. 19.83a

Fig. 19.80c

Fig. 19.80a

Fig. 8.35c

Fig. 8.35a

Table 26.6  Differential diagnoses for lesions with a low diffusion signal associated with a high ADC and isointense T2 signal Diagnoses Normal Neonate

Reference images DWI low ADC high

T2 iso

Fig. 2.4a

Fig. 2.4c

t6.4 t6.5 t6.6

Fig. 2.4b

Table 26.7 Differential diagnoses for lesions with artifacts Diagnoses

t6.1 t6.2 t6.3

Reference images DWI ADC

Susceptibility artifacts Physiological iron Fig. 2.1a deposition Oxy/deoxy hemoglobin Fig. 10.3d Deoxy/intracellular Fig. met-hemoglobin 10.5c Epidural hematoma Fig. (EDH) 17.22c Hemosiderin/ferritin Fig. 10.3k Hemorrhagic infarction Fig. 9.21b Disseminated aspergillosis Fig. 11.11c Metastasis (melanoma) Fig. 18.61d Contusion Fig. 17.19c Eddy current artifacts Fig. 3.7a N/2 ghosting artifacts Fig. 3.10a Motion artifacts Fig. 3.12a

t6.7

t7.1 t7.2 t7.3

T2

t7.4 t7.5

Fig. 2.1b Fig. 2.1c

t7.6 t7.7

Fig. 10.3f Fig. 10.5e Fig. 17.22d Fig. 10.3m

Fig. 10.3b Fig. 10.5a Fig. 17.22b Fig. 10.3i

t7.8 t7.9

Fig. 9.21c

Fig. 9.21a

Fig. 11.11d

Fig. 11.11a

Fig. 18.61e

Fig. 18.61b

Fig. 17.19d

Fig. 17.19b

t7.10 t7.11 t7.12 t7.13 t7.14 t7.15 t7.16 t7.17 t7.18 t7.19 t7.20 t7.21 t7.22 t7.23 t7.24 t7.25 t7.26 t7.27 t7.28

Fig. 3.7b Fig. 3.7c

t7.29 t7.30

Fig. 3.10b

Fig. 3.10c

Fig. 3.12b

Fig. 3.12c

t7.31 t7.32 t7.33 t7.34 t7.35

26  How to Use This Book

907

26

27

28

T. Moritani and P.-L. Westesson

908

In

Probability

Signal

Diffusion tensor imaging

S(b) = –bD S(0)

Motion proving gradient

Displacement

In

S(b) 1 = –bD + b 2D 2(K ) 6 S(0)

Probability

Signal

Diffusion kurtosis imaging

Motion proving gradient

Displacement

Probability

Signal

q-Space Imaging

Fourier transform

Displacement

Motion proving gradient

29

Skull Subarachnoid space Para-arterial CSF influx Neuron Paraarterial space

AQP4

30

Astrocyte vascular endfeet

Artery

Convec t i ve flow

ISF

Vein

Waste

Paravenous efflux

26  How to Use This Book

909 Ln (Sb/S0)

Mono-exponential model = ADC

≈D IVIM (bi-exponential model)

b value

31

REST

ACTIVATION

Normal blood flow Oxyhemoglobin

32

High blood flow Deoxyhemoglobin

Arcuate fasciculus

SMA Middle frontal gyrus Broca’s area Pars orbitulis Pars triangularis Pars opercularis Wernicke’s area

33

Superior temporal gyrus Supramarginal gyrus Angular gyrus

T. Moritani and P.-L. Westesson

910 Ischemia/Hypoxia

Status epilepticus, migraine, SMART, MELAS reversible white matter disease

Energy failure (primary injury, core of infarction)

Secondary excitotoxic injury (marginal area in penumbra)

ATP ↓ O2

Ca ↑ second messengers

Excitotoxic injury

Intramyelinic edema, reactive astrocyte swelling Degradation of cytoskeleton

Ca

Respiratory failure

O2 Mitochondria

O2

ER Cytochrome C Protein synthesis inhibiton

Caspase-12

Lysosome

Caspase-3

Caspase-9

Proteolytic enzyme release

Reversible Golgi Nucleus

ATP ↓↓

DNA fragmentation 34

Severe mitochondrial dysfunction

Pan-necrosis (neuron, glia, endothelium)

ATP ↓

ATP ∼

Selective (programmed) neuronal necrosis gliosis

Programmed neuronal cell death/apoptosis gliosis

26  How to Use This Book

35

36

911

T. Moritani and P.-L. Westesson

912

Membranous barrier/deformation

Macromolecules Intra/extracellular matrix Mucous fluid Gelatinous cyst

Hypercellularity Abscess Hematoma Coagulative necrosis

Stokes -Einstein’s formula ADC=kT / 6πr x ŋ (viscosity)

Membranous deformation

Macromolecule

intramyelinic edema cellurarity

Water molecule

37

38

Cellular/cytotoxic edema (intracellular organella swelling) Intramyelinic edema Axonal swelling Epidermoid

axonal swelling

26  How to Use This Book

913

39

Hyperacute (oxyHb)

Acute (deoxyHb)

Early Subacute (metHb)

Late Early Chronic Subacute (hemichrome/ (extracellular hemosiderin rim) metHb)

Late Chronic (hemosiderin rim/ deposition)

T1

Iso

Iso

Bright

Bright

Iso Center with Dark Rim

Dark Center with Dark Rim to Dark

T2

Iso

Dark

Dark

Bright

Iso Center with Dark Rim

Bright Center with Dark Rim to Dark

DWI

Bright

Dark

Dark

Bright

Bright Center with Dark Rim Dark Center with Dark Rim to Dark

ADC

Dark

NAC

NAC

Dark

Dark Center with NAC Rim

Bright Center with NAC Rim to NAC

40

OxyHb diamagnetic Feb2+ (ferrous)

DeoxyHb paramagnetic Fe2+ (ferrous)

MetHb paramagnetic Fe3+ (ferric)

oxidative denaturation

oxidative denaturation

deoxgenation

Hemichrome diamagnetic Fe3+ (ferric)

eeO2

ee-

e-

ee-

eeWater

Unpaired electrons: T2* relaxation Heme protein 41

Electron-water molecule interaction: T1 relaxtion

Hemosiderin paramagnetic

T. Moritani and P.-L. Westesson

914

42

43

Prec un eu s

Fo

gulat e gyrus Cin

x rni AT A

al mp Hi p p oca

s gyru

44

45

26  How to Use This Book

46

47

915

916

48

T. Moritani and P.-L. Westesson

26  How to Use This Book

49

917

918

50

T. Moritani and P.-L. Westesson

26  How to Use This Book

51

52

919

920

53

T. Moritani and P.-L. Westesson

26  How to Use This Book

54

921

922

55

T. Moritani and P.-L. Westesson

26  How to Use This Book

56

57 58

923