Visualized Medicine: Emerging Techniques and Developing Frontiers (Advances in Experimental Medicine and Biology, 1199) [1st ed. 2023] 9813299010, 9789813299016

Table of contents :
Foreword I
Foreword II
Foreword III
Preface
Contents
1: Evolution from Medical Imaging to Visualized Medicine
1.1 Overview and Milestones
1.2 Traditional Medical Imaging
1.2.1 X-Ray and CT
1.2.2 Magnetic Resonance Imaging
1.2.3 Nuclear Imaging
1.2.4 Ultrasound Imaging
1.3 Molecular Imaging
1.3.1 Molecular Optical Imaging
1.3.2 Molecular Nuclear Imaging
1.3.3 Molecular MR Imaging
1.3.4 Multimodality Imaging
1.4 Visualized Medicine
1.4.1 VR/AR-Aided Technology
1.4.2 AI Personalized Diagnosis
1.4.3 Image-Guided Robotic Precision Surgery
1.4.4 Intelligent Traditional Chinese Medicine
1.5 Outlook
References
2: Medical Imaging Technology and Imaging Agents
2.1 Traditional Medical Imaging
2.1.1 Magnetic Resonance Imaging
2.1.1.1 Principle of MRI
2.1.1.2 Contrast Agents in MRI
Paramagnetic and Superparamagnetic Contrast Agents
Nanoparticle Contrast Agents
2.1.2 Computed Tomography
2.1.2.1 Principle of CT
2.1.2.2 Contrast Agents in CT
Iodine-Based Contrast Agents
Nanoparticle Contrast Agents
2.1.3 Positron Emission Tomography
2.1.3.1 Principle of PET
2.1.3.2 Contrast Agents in PET
PET Radioisotopes
Nanoparticle Contrast Agents
2.1.4 Single-Photon Emission Computed Tomography
2.1.4.1 Principle of SPECT
2.1.4.2 Contrast Agents in SPECT
SPECT Radionuclides
Functional Nanomaterials in SPECT
2.1.5 Ultrasound
2.1.5.1 Principle of US
2.1.5.2 Contrast Agents in US
Microbubble-Based Ultrasound Contrast Agents
Nanobubble-Based Ultrasound Contrast Agents
2.1.6 Optical Imaging
2.1.6.1 Principle of OI
2.1.6.2 Contrast Agents in OI
2.2 Emerging Imaging Technologies
2.2.1 Photoacoustic Imaging
2.2.1.1 Principle of PAI
2.2.1.2 Contrast Agents in PAI
2.2.2 Thermoacoustic Imaging
2.2.2.1 Principle of TAI
2.2.2.2 Contrast Agents in TAI
2.2.3 Raman Imaging
2.2.4 Up-Conversion Luminescence
2.2.5 NIR-Based Imaging Technologies
2.3 Multimodal Imaging Technologies
2.3.1 Common Multimodal Imaging
2.3.2 Contrast Agents in Common Multimodal Imaging
References
3: Molecular Imaging for Early-Stage Disease Diagnosis
3.1 The Early-Stage Diagnosis of Malignant Tumors
3.2 The Early-Stage Diagnosis of Cardio- or Cerebrovascular Diseases
3.3 The Early-Stage Diagnosis of Digestive System Disease
3.4 The Early-Stage Diagnosis of Central Nervous System Disease
3.5 The Early-Stage Diagnosis of the Other Diseases
References
4: Image-Guided Precision Treatments
4.1 Chemotherapy
4.2 Radiotherapy
4.2.1 3D Conformal Radiotherapy (3D-CRT)
4.2.2 Intensity-Modulated Radiotherapy (IMRT)
4.2.3 Image-Guided Radiotherapy (IGRT)
4.3 Thermal Therapy
4.3.1 Photothermal Therapy
4.3.2 Magnetic Hyperthermia Therapy
4.3.3 Thermal Ablation
4.3.3.1 Cryoablation (CA)
4.3.3.2 Radiofrequency Ablation (RFA)
4.3.3.3 Microwave Ablation (MWA)
4.4 Dynamic Therapy
4.4.1 Photodynamic Therapy
4.4.2 Sonodynamic Therapy
4.4.3 Chemodynamic Therapy
4.4.4 Other Dynamic Therapies
4.5 Gas Therapy
4.5.1 Nitric Oxide (NO)
4.5.2 Hydrogen (H2)
4.5.3 Hydrogen Sulfide (H2S)
4.5.4 Carbon Monoxide (CO)
References
5: Imaging-Navigated Surgery
5.1 Endoscopes
5.1.1 Milestones of the Endoscope Development
5.1.1.1 Hard-Tube Endoscopes
5.1.1.2 Semiflexible Endoscopes
5.1.1.3 Fiber Endoscopes
5.1.1.4 Ultrasound Endoscopes
5.1.1.5 Electronic Endoscopes
5.1.1.6 Capsule Endoscopes
5.1.2 Clinical Applications of Endoscopy
5.2 Surgical Robots
5.2.1 Milestones of Surgical Robot Developments
5.2.1.1 Puma 560
5.2.1.2 ROBODOC
5.2.1.3 AESOP
5.2.1.4 ZEUS
5.2.1.5 Da Vinci
5.2.2 Latest Advancements of Surgical Robots in China
5.2.2.1 MicroHand S by Tianjin University
5.2.2.2 HURWA by Peking Union Medical College Hospital
5.2.2.3 Luban by Peking Tiantan Hospital and Peking Institute of Technology
5.2.3 Clinical Applications of Surgical Robots and Future Perspectives
5.3 Nanorobots for Advanced Imaging Navigations and Minimally Invasive Surgery
5.3.1 Targeted Drug Delivery and Therapy
5.3.2 Precision Surgery
References
6: AI-Aided Disease Prediction in Visualized Medicine
6.1 Definition and Development History of AI
6.1.1 Definition
6.1.2 Development History
6.2 Industrial System of AI in Healthcare
6.2.1 Technical System of AI in Visualized Medicine
6.2.2 AI in Healthcare Industrial Ecology
6.2.3 Industrial Pattern of AI in Medical Health
6.3 Applications of AI in Medicine
6.3.1 Neurological System-Related Disease
6.3.1.1 Alzheimer’s Disease
6.3.1.2 Epilepsy
6.3.2 Cardiologist Diseases
6.3.2.1 ECG Classifier
6.3.2.2 Heart Failure
6.3.3 Pulmonary Disease
6.3.3.1 COVID-19 Pneumonia
6.3.3.2 Tuberculosis Diagnosis
6.3.3.3 Chronic Obstructive Lung Disease (COPD)
6.3.4 Ophthalmology-Related Systems
6.3.4.1 Diabetic Retinopathy Screening
6.3.4.2 Screening for Age-Related Macular Degeneration
6.3.4.3 Glaucoma Screening
6.3.5 Oncology
6.3.5.1 Breast Cancer
6.3.5.2 Colorectal Cancer
6.3.5.3 Thyroid Cancer
6.3.5.4 Dermatology
6.4 Limitations and Future Prospect
References
7: Brain-Computer Interfaces in Visualized Medicine
7.1 Brain-Computer Interface (BCI) and Its Visualization in Medicine
7.2 The Components of the BCI System
7.2.1 Neural Signal Acquisition
7.2.2 Signal Processing
7.2.2.1 Preprocessing
7.2.2.2 Feature Extraction
7.2.2.3 Classification and Decoding
7.2.2.4 Deep Learning Strategy
7.2.3 Device Output
7.2.4 User Feedback
7.3 Brain Signals Used for BCI System
7.3.1 Overview of Different Neural Signals’ Characteristics
7.3.2 Electrophysiological Signals Used for BCIs
7.3.2.1 Case I. Scalp EEG
7.3.2.2 Case II. Intracranial EEG for Human Clinical Recording
7.3.2.3 Case III. Utah Array
7.3.3 Magnetic Signals Used for BCIs
7.3.3.1 Comparison Between Electric and Magnetic Signals
7.3.4 Metabolic Signals Used for BCIs
7.3.4.1 Case I. fNIRS
7.3.4.2 Case II. fMRI
7.4 Brain-Computer Interfaces for Motor Recovery
7.4.1 The First Generation of the BrainGate System for Two-Dimensional Motor Control
7.4.2 The Second Generation of the BrainGate System for Three-Dimensional Motor Control
7.4.3 Neuromuscular Electrical Stimulation Sleeve (NMES)
7.4.4 The Chinese Noninvasive Artificial Nerve Rehabilitation Robot (ANRR) System for Motor Rehabilitation
7.4.5 The Chinese Implantable BCI System for Three-Dimensional Motor Control
7.5 Brain-Computer Interfaces for Communications
7.5.1 Fully Implanted BCI for Indirect Communication by Using a Typing Speller
7.5.2 Noninvasive BCI for Indirect Communication by Using a Typing Speller
7.5.3 Direct Communication by Synthesizing Speech from the Neural Activities of the Speech Cortex
7.5.4 Tonal Information Decoding for Direct Speech Communication BCI in a Tonal Language
7.6 Visualization Patterns and Applications of Brain-Computer Interfaces
7.6.1 Case I. Event-Related Potential Visualization
7.6.2 Case II. Time-Frequency Spectrogram Visualization
7.6.3 Case III. Topography Visualization
7.6.4 Case IV. Multivariate Pattern Analysis Visualization
7.6.5 Case V. Network Analysis Visualization
7.7 Visualization of User Feedback in the BCI System
7.7.1 The User Feedback’s Visualization Promotes Neural Rehabilitation Through Games
7.7.2 The User Feedback’s Visualization Helps to Interact with the Human in Multiple Circumstances
7.7.2.1 Case I. Virtual Reality (VR) for Visualization
7.7.2.2 Case II. Drone Control for Visualization
References
8: Organ Chips and Visualization of Biological Systems
8.1 Overview of Organ Chips
8.2 The Underlying Technology of Organ Chips
8.2.1 Microfluidic Technology
8.2.2 Three-Dimensional Cell Culture Technology
8.2.3 Biomarker Detection Technology
8.3 Types of Organ Chips
8.3.1 Single Organ Chip
8.3.1.1 Lung Chip
8.3.1.2 Liver Chip
8.3.1.3 Kidney Chip
8.3.1.4 Heart on a Chip
8.3.1.5 Other Single-Organ Chips
8.3.2 Multi-organ Chip
8.3.3 Organ Chips and Organoids
8.4 Applications and Challenges of Organ Chips
8.4.1 Application of Organ Chips
8.4.1.1 Disease Model
8.4.1.2 Drug Screening
8.4.1.3 People and Personalized Medicine on a Chip
8.4.2 Problems in Organ Chip Research
References

Citation preview

Advances in Experimental Medicine and Biology 1199

Zhe Liu Editor

Visualized Medicine

Emerging Techniques and Developing Frontiers

Advances in Experimental Medicine and Biology Volume 1199 Series Editors Wim E. Crusio, Institut de Neurosciences Cognitives et Intégratives d’Aquitaine, CNRS and University of Bordeaux Pessac Cedex, France Haidong Dong, Departments of Urology and Immunology Mayo Clinic, Rochester, MN, USA Heinfried H. Radeke, Institute of Pharmacology and Toxicology Clinic of the Goethe University Frankfurt Main Frankfurt am Main, Hessen, Germany Nima Rezaei , Research Center for Immunodeficiencies, Children's Medical Center Tehran University of Medical Sciences Tehran, Iran Ortrud Steinlein, Institute of Human Genetics LMU University Hospital Munich, Germany Junjie Xiao, Cardiac Regeneration and Ageing Lab, Institute of Cardiovascular Sciences School of Life Science, Shanghai University Shanghai, China

Advances in Experimental Medicine and Biology provides a platform for scientific contributions in the main disciplines of the biomedicine and the life sciences. This series publishes thematic volumes on contemporary research in the areas of microbiology, immunology, neurosciences, biochemistry, biomedical engineering, genetics, physiology, and cancer research. Covering emerging topics and techniques in basic and clinical science, it brings together clinicians and researchers from various fields. Advances in Experimental Medicine and Biology has been publishing exceptional works in the field for over 40 years, and is indexed in SCOPUS, Medline (PubMed), EMBASE, BIOSIS, Reaxys, EMBiology, the Chemical Abstracts Service (CAS), and Pathway Studio. 2021 Impact Factor: 3.650 (no longer indexed in SCIE as of 2022)

Zhe Liu Editor

Visualized Medicine Emerging Techniques and Developing Frontiers

Editor Zhe Liu Academy of Medical Engineering &Translational Medicine Tianjin University Tianjin, China

ISSN 0065-2598     ISSN 2214-8019 (electronic) Advances in Experimental Medicine and Biology ISBN 978-981-32-9901-6    ISBN 978-981-32-9902-3 (eBook) https://doi.org/10.1007/978-981-32-9902-3 © Springer Nature Singapore Pte Ltd. 2023 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 reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Research depends on physical labor and mental activity, and innovation requires logical inference, robust imagination as well as the courage of debate. This book is dedicated to the people who have made continuing contributions to visualized medicine, and also to the 12th birthday of my lovely daughter Sharyll, who always brings me a new vision to this colorful world. —Zhe LIU

Foreword I

Medical imaging has seen over 100 years of development and has served human health management for decades. Nowadays, this traditional subject is confronted with ever-growing integration with several transdisciplinary topics such as artificial intelligence, meta-data analysis, smart biomaterials, nanorobots, and image-navigated surgery among which visualized medicine is one of the most vigorous fields to probably reshape the future of modern medicine. This book has timely drawn a clear roadmap regarding the evolution from traditional medical imaging, molecular imaging to today’s visualized medicine. In the first three chapters, a concise but comprehensive history was disclosed, and imaging-based contrast agents or probes were also categorized and discussed in concrete. As we know, the powerful imaging capability strongly relies on the implementation of imaging biomaterials, which have been widely designed, engineered, and developed for diagnosis and therapy (or theranostics) against human fatal diseases. In this sense, nanomedicine and nanotoxicology have come into effect as two major subjects in terms of biosafety and biotoxicity. In particular when facing the epidemic of COVID-­19, people have established reliable methods for rapid detection with bio-imaging probes, and they will definitely pay much more attention to the biosafety/biotoxicity and make further evaluable investigations in the field of medical imaging. In Chaps. 4 and 5, authors have summarized state-of-the-art advancements in the fields of image-based treatments and surgical operations in the past decades. In these arenas, visualized medicine has closely involved and undertaken indispensable roles in advanced diagnosis and therapy strategies. In Chapter 6 and 7, applications of artificial intelligence (AI) and brain– computer interfaces (BCI) as two specific topics in close relationship with visualized medicine have been elucidated, especially for their attractiveness to fundamental research scientists and industrial staff who have made enormous endeavor to translate these technologies into revolutionizing our life and improving human healthcare. In Chapter 8, the fast developments as well as advanced technologies of organ-on-a-chip (OOC) have been outlined, and in particular their applications in real-time visualization and high-resolution analysis on various human biological processes were discussed. In conclusion, this book Visualized Medicine edited by Prof. Dr. Zhe LIU aims to seek novel findings from traditional subjects and meet the urgent demands to utilize cutting-edge techniques and put medical imaging forward vii

viii

with innovative visions and viewpoints. Hence right from his book, it is believed that the vast readership will acquire more upgraded knowledge in visualized medicine, and more importantly produce quite a lot of novel ideas from what is going to be coming into our life tomorrow. Yuliang Zhao Academician, Chinese Academy of Sciences Beijing, China Director-General, National Center for Nanosciences and Technology Beijing, China Founder of Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety Beijing, China Editor-in-Chief, Nano Today Beijing, China

Foreword I

Foreword II

Visualized medicine, as an emerging field with great development prospects in the 21st century, will change our traditional understandings in many aspects. Although visualized medicine is still in its infancy, many techniques have shown their potential to change traditional paradigms of modern medicine. Medical imaging is the most important way for disease diagnosis and treatments, and several major imaging technologies have been widely applied to clinical uses such as CT, MRI, PET/SPECT, and ultrasound. In regard to my expertise, ultrasound imaging has undoubtedly attracted much attention and greatly favored scientists and patients’ requirements. During my professional careers in the past 50 years, I have personally witnessed the developments of ultrasound imaging in China from a child to an adult, from weak to strong, and from low levels to a developed stage. Some of my friends often ask me: what will the future of visualized medicine look like? To answer this question, the book of Visualized Medicine edited by Prof. Dr. LIU may give a comprehensive, systematic, and forward-looking interpretation on the future of visualized medicine. In particular, hand-held ultrasound scanners, also known as palm ultrasound or pocket ultrasound, are a type of ultrasound imaging apparatus that combine ultrasound imaging, image management, and analysis into one entity, and they are probably essential for every physician in the coming future. These products are expected to lead the frontier of ultrasound instrument developments due to their brilliant merits of high resolution, intelligent analysis, wireless connection, remote transmission, continuous power duration, and especially their availability for community hospitals. Among the hand-held ultrasound scanners, visualscopic inspectors (compared to stethoscope inspectors) as well as the derived components and scenarios have come into clinical use. Since 2016, the Ministry of Science and Technology of China has listed hand-held ultrasound scanners as a high-priority program, and corresponding projects have worked out with a series of products and acquired a final national approval in June 2022. The visualscopic inspectors transmit and receive ultrasonic waves that are reflected by human organs/tissues and forms a sectional image by an image processing chip. Compared to traditional piezoelectric ceramic materials, cMUT and pMUT semiconductors have made breakthroughs in high-frequency and three-dimensional imaging in recent years. In the meanwhile, the innovated 2D array ultrasonic probes and 3D stereo imaging systems have offered great opportunity to further broaden their scopes in image-guided surgery, interventional operations, ix

Foreword II

x

and preclinical small-animal investigations. Therefore, it is believed that these visualscopic inspectors will be undoubtedly bringing visualized medicine into a vast world where clinical physicians can perform intuitive and accurate diagnosis and treatments for their patients. In this sense, this book of Visualized Medicine will gather professional consensus in the scientific community and have a substantial impact in the standpoints of value medicine and health economy. Division of Ultrasound Instrument and Technology China Association of Medical Equipment Beijing, China Committee of Continuing Education of Ultrasound Medicine National Health Commission of the People’s Republic of China Beijing, China

Xing Yu

Foreword III

Medical imaging, as a traditional subject of medicine, came into being since 1895 when Wilhelm Conrad Röntgen, a German physicist, discovered natural X-ray irradiation and obtained the first X-ray image of his wife’s hand. Starting from this, medical imaging has experienced over 100 years of evolution and earned tremendous attention from fundamental research scientists, industrial engineers, and medical personnel. During this period, X-ray and CT scanners were invented for clinical diagnosis in 1900s. More than that, radiology and other imaging instruments such as PET, SPECT, MRI, and ultrasound were developed and soon commercialized for clinical uses to detect the occurrence of human diseases, so that people were able to improve their life span by years in the 20th century. Late in 1990s, the concept of molecular imaging was proposed. Due to this philosophy, medicine has witnessed an unprecedented progression of precision medicine and personalized/individualized medicine. This has set up a new vision to promote modern medicine, utilizing novel techniques such as targeted drugs, cancer immunology, molecular biology, and biomedical engineering to investigate ourselves and find solutions to our health problems. Dual-modal or multi-modal medical imaging has been performed by physicians, and detailed physiological/pathological information was accordingly acquired. Now, a new concept of visualized medicine has come to us, and some visualized medical techniques have come into effect for clinical diagnosis and even treatments. Cancer is still a major factor that threatens and tortures millions of people in pain. In this regard, visualized medicine may provide not only useful but also powerful tools. For example, with endoscopes and surgical robots we are capable of seeing the presence of disease, development, and probably metastasis in the human body and accurately remove the lesions under the image guidance. In addition, engineered nanorobots are competent to go into the body and perform microscopic surgery in the heart, liver, spleen, and other organs. In this way, the process of treatment is applicable and delicate. For patients, they no longer need to tolerate bleeding, injury, or wounds, but can go back home right after an interventional surgery. Therefore, visualized medicine is and will be playing a vitally important role. It is my pleasure to see the book Visualized Medicine: Emerging Techniques and Developing Frontiers edited by Dr. Zhe LIU, and it is published by Springer-Nature. Fundamental knowledge and techniques on medical imaging and molecular imaging are summarized in this book and, most i­ mportantly, xi

Foreword III

xii

the latest advancements on visualized medicine are carefully elucidated. As a feature to this book, cutting-edge technologies of artificial intelligence, metadata analysis, surgical robots, etc. have been included in the corresponding chapters. Thus, this book, in my opinion, has drawn an exciting picture on future trends of this subject and outlook the upgraded paradigms for disease diagnosis, therapy, and health managements. In the special moments of COVID-19 epidemics, this book will definitely give everyone a new angle to look into the development of modern medicine and also encourage us for further innovations. I personally expect more exciting technologies of visualized medicine to be invented, and much more valuable contributions are anticipated in the coming decades. University Professor of Genetics  Cancer Center, University of Heidelberg Heidelberg, Germany Editor-in-Chief, Cancer Letters Distinguished Professor, Tianjin Medical University Tianjin, China

Manfred Schwab

Preface

Today, people have seen tremendous improvements in every aspect of their life. However, there are still a number of challenges to people’s health, and fatal diseases threaten us and the next generations. Modern medicine has entered a new era where physiological/pathological changes in our body may be detected and medical measures need to be taken as soon as possible. In 1895, a German physicist, Wilhelm Röntgen discovered X-ray, and the first X-ray image was obtained, which showed everyone a clear anatomic structure of a human hand. This also built up a new subject of radiology and enabled us to study ourselves by using a tool called medical imaging. In the past century, the evolution from traditional medical imaging to molecular imaging extraordinarily broadened our scope to carefully look into human diseases, developed a series of imaging modalities and medical instruments for early-stage diagnosis, and then served efficient treatments and ever-­lasting healthcare. Moreover, with this powerful tool, fundamental science and technology have experienced substantial progress to further revolutionize today’s world. Time does not stop where it is but brings us much more unknown. Representative cutting-edge technologies such as artificial intelligence, meta-­ data analysis, flexible sensors, surgical robots, and virtual reality/augmented reality came into our lives in the past decades. They rapidly merged medical imaging techniques and produced advanced paradigms for disease theranostics. Visualized medicine is one of the products that influence our future understanding, investigations, and medical strategies in modern medicine. For example, it renders the easiest accessibility to monitor in real-time the occurrence and evolution of diseases inside our body, and meanwhile provides the easiest approach to treat diseases in the presence of image guidance. Precision medicine will be more conveniently performed in a visualized manner, especially for fatal human diseases such as cancers and cardio-cerebral vasculatures. Surgical robots are endowed with multi-functionality and intelligence for pre-operative surgical planning, intra-operative image navigation, and post-operative therapeutic assessment. Clinical image data will be of great value not only to health management, but also to case prediction and epidemiological analysis (e.g., in the case of COVID-19 epidemics), which help physicians make reliable decisions. Hence, visualized medicine needs to make contributions to our health, and must do more to our lives. To this end, this book came to us.

xiii

Preface

xiv

In addition to all authors’ arduous contribution, three worldwide distinguished scholars have been cordially invited to write the Foreword section for this book. It is believed that their valuable viewpoints will definitely advise our readers with perspective insights and forward-looking ideas, and this further helps us renew our understanding and keep our paces tuned in the frontier. As for the financial funding, this contribution was cordially supported by the National Natural Science Foundation of China (No. 21575106 and No. 82072057), the Emerging Engineering Education Research and Practice Program of Ministry of Education, China (No. E-YGJH20202801), and the Tianjin University International Teaching Resources Construction Program for Graduate Education (No. ENT20019). Tianjin, China

Zhe Liu

Contents

1 Evolution  from Medical Imaging to Visualized Medicine������������   1 Yu Shi and Zhe Liu 2 Medical  Imaging Technology and Imaging Agents ����������������������  15 Jieting Wu and Huanhuan Qiao 3 Molecular  Imaging for Early-Stage Disease Diagnosis����������������  39 Kuo Zhang, Haiyan Xu, and Kai Li 4 Image-Guided Precision Treatments����������������������������������������������  59 Yu Shi, Chen Zhang, Chenxi Liu, Xinyong Ma, and Zhe Liu 5 Imaging-Navigated Surgery������������������������������������������������������������  87 Yandai Lin, Chen Zhang, Chenxi Liu, Xinyong Ma, Qiang Yang, Binggang Guan, and Zhe Liu 6 AI-Aided  Disease Prediction in Visualized Medicine�������������������� 107 Juan Du, Mengen Huang, and Lin Liu 7 Brain-Computer  Interfaces in Visualized Medicine �������������������� 127 Xiaopeng Si, Yu Zhou, Sicheng Li, Xingjian Zhang, Shunli Han, Shaoxin Xiang, and Dong Ming 8 Organ  Chips and Visualization of Biological Systems������������������ 155 Tian Tian, Jun Liu, and He Zhu

xv

1

Evolution from Medical Imaging to Visualized Medicine Yu Shi and Zhe Liu

Abstract

The discovery of X-ray in 1895 and the first X-ray image of Mrs. Röntgen’s hand opened up a new era of radiology and the research of medical imaging. The evolution of traditional medical imaging has been lasting for over 100  years, serving the detection, diagnosis, and treatments of human diseases with a clear view of the anatomy information. In late 1990s, the concept of molecular imaging was proposed as the science and technology of molecular biology and bio-engineering rapidly developed, and it directly gave birth to the emergence of precision medicine for clinical lesion-targeted treatments against various cancers and cardiocerebrovascular diseases. Physiological and pathological changes in live bodies from zebrafish to human beings can be imaged to ensure an efficient image-guided therapy. Nowadays, the philosophy of medical and molecular imaging has been a powerful tool and indispensable modality for doctors to

make their decisions and give patients reliable advices. With the ever-emerging developments of advanced intelligent technologies such as flexible sensors, medical meta-data analysis, brain sciences, surgical robots, VR/AR, etc., modern medicine has been evolving from traditional medical and molecular imaging to visualized medicine, which has created novel accessible approaches along with cutting-­edge techniques for the revolutionized diagnostic and therapeutic paradigms. In this context, the history and milestones from medical imaging to visualized medicine will be elucidated. And in particular, representative visualized medicine advances including its application to COVID-19 epidemics will be discussed in order to look for its important contributions and a future perspective to modern medicine. Keywords

Traditional medical imaging · Molecular imaging · Visualized medicine · Artificial intelligence

Supplementary Information The online version contains supplementary material available at https://doi. org/10.1007/978-­981-­32-­9902-­3_1.

Y. Shi · Z. Liu (*) Academy of Medical Engineering and Translational Medicine, Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 Z. Liu (ed.), Visualized Medicine, Advances in Experimental Medicine and Biology 1199, https://doi.org/10.1007/978-981-32-9902-3_1

1

Y. Shi and Z. Liu

2

Abbreviations AI Artificial intelligence AR Augmented reality AV Augmented virtuality BLI Bioluminescence imaging CAs Contrast agents CAT Computed axial tomography CT Computed tomography FLI Fluorescence imaging Gd Gadolinium GFP Green fluorescent proteins ICH Intracerebral hemorrhage MoAbs Monoclonal antibodies MR Magnetic resonance MR Mixed reality MRI Magnetic resonance imaging NIH National Institute of Health NIR Near infrared NMR Nuclear magnetic resonance PET Positron emission tomography RSNA Radiology Society of North America

SPECT Single-photon emission computed tomography TCM Traditional Chinese medicine US Ultrasound VR Virtual reality WMIC World Molecular Imaging Congress

1.1 Overview and Milestones Medical imaging as a common diagnostic technique is utilized to image the interior of the human body for clinical analysis, diagnosis, monitoring, and visual representations of organs, tissues, or pathological/physiological processes [1, 2]. It can be seen as an inversely mathematical strategy, which means that pathological/ physiological circumstances are inferred from signals that are observed or computed. An overview to demonstrate major breakthroughs and significant milestones from traditional medical imaging, molecular imaging, to

Medical Imaging X-ray & CT 1895 X-ray discovered by Röntgen* 1896 X-ray for the first surgical operation in Ireland 1963 First CT through cross-section reconstructions* 1971 The first CT scanner and brain scanning 1972 The first commercial available CT scanner*

Visualized Medcine

Molecular Imaging Optical Imaging 1993 NIR Raman & fluorescence for cancer diagnosis 1994 GFP as a biomarker for gene expression

VR/AR-aided Technology

Molecular Nuclear Imaging Nuclear imaging 1934 Discovery of artificial radioactivity* 1936 The first medical application of an artificial radionuclide 1963 SPECT reported by David & Roy 1975 PET reported by Michael & Edward

1895 ... 1970 Magnetic Resonance Imaging 1946 Development of NMR in condensed matter* 1973 2D/3D MRI reconstruction & MRI for living mice Linear field gradient for NMR signal location* 1978 The first MRI scanning for human beings

1941 1949 1973 1986

Ultrasound Imaging The first ultrasound image of a human brain Ultrasound for the assessment of tissue thickness Hand-held medical ultrasonic scanner CO2 microbubbles as US CAS for tumor diagnosis

1986

99m

111

Tc/ In-labeled MoAb for melanoma diagnosis 1991 111In-labeled chemotactic peptide analogs for imaging infection

2015

1999 Molecular MRI

AI Personalized Diagnosis

2020 Robotic Precision Surgery

1988 Ga-labeled contrast agents for liver-targeted MRI 1999 Intracellular labeling by MRI Multimodality Imaging 1996 The first SPECT/CT scaner 1998 The first PET/CT scaner 2007 PET/CT/bioluminescence for tumor imaging Without Contrast Agent

With MPIO-αVCAM-1

Fig. 1.1  Milestones of medical imaging. *Nobel Prize for medical imaging

Intelligent Chinese Medicine

1  Evolution from Medical Imaging to Visualized Medicine

visualized medicine have been summarized in Fig. 1.1.

1.2 Traditional Medical Imaging Traditional medical imaging generally means imaging modalities including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging. Among them are X-ray and CT implement ionization radiation, and patients have a high risk of cancers in case of frequent X-ray exposures. Similarly, nuclear imaging by means of radioisotopes presents a highest sensitivity, but patients are usually exposed to a certain radioactive circumstance with the potential risk of cellular mutation and cancer occurrence. MRI has a reliable safety to human beings without any ionization risks, and it is capable to provide a detailed anatomical and histological information with desirable image contrast especially for soft tissues.

1.2.1 X-Ray and CT The origin of medical imaging dated back to the year of 1895 when the German physical scientist, Wilhelm Conrad Röntgen, occasionally discovered the X-ray radiation, and one day before Christmas, his wife visited the lab and got an X-ray image of her hand [3]. Röntgen was awarded with the first Nobel Prize in Physics in 1901 right for this epoch-making and applicable discovery. Only 1 year later in 1896, X-ray was used for the first time in a clinical surgery by John Hall-Edwards in Birmingham of England [4]. X-ray, as the earliest and most common traditional medical imaging, affords grayscale images according to the attenuation of X-ray through different tissues. In comparison, computed tomography, also called computed axial tomography (CAT), shows precise 3D anatomical structures of the body by multiple X-ray projections at different angles and computing algorithms. Allan Cormack, an African American physicist, carefully calculated the attenuation of X-ray passing through soft

3

tissues from different angles and created the first computed tomography (CT) scanner in 1963. He therefore won the 1979 Nobel Prize in Physiology or Medicine along with Godfrey Hounsfield who invented the first commercialized CT scanner and accomplished the first human brain scanning in early 1970s and worked out with the first wholebody scanner in 1975. CT possesses excellent spatial anatomical resolution in the range of millimeters and sub-­ millimeters, and it has witnessed a great progress in CT image sensitivity, resolution, and quality by the assistance of computerization [3].

1.2.2 Magnetic Resonance Imaging In 1937, Isaac Rabi attempted to measure the magnetic moment of lithium compounds to describe the spin orientation of nuclei in an oscillating magnetic field [5, 6]. For this contribution to fundamentals of magnetic resonance imaging (MRI), he was awarded with the Nobel Prize in Physics in 1944 [7, 8]. MRI severed for accurate anatomy since Paul Lauterbur developed 2D/3D imaging techniques verified by a first MRI of a living mouse in 1973 [9, 10]. At that time, Peter Mansfield confirmed the location of NMR signals with a linear field gradient and acquired a first human-body MR image in 1977 as well as the first scanner in 1978 [11–13]. For their prominent contributions to medical MR imaging, they were awarded with Nobel Prize in Medicine in 2003, and the first commercialized MR imaging system was produced by FONAR, in the United States in 1980 [14].

1.2.3 Nuclear Imaging Nuclear imaging started from the discovery of uranium by Henri Becquerel in 1896, and the radioactivity of artificial elements was discovered by Pierre Curie and Marie Curie in 1898 with the definition of the term radioactivity [15]. As the pioneers of nuclear medicine, these three scientists were awarded with the Nobel Prize in

Y. Shi and Z. Liu

4

1903. In 1934, Irene Joliot-Curie transformed one element into another with positron emission or β-decay (27Al + 4He → 30P + 1n.), which enabled a large scale of radioisotope production [16]. The medical nuclear imaging was deemed as the start of artificial radiation by Frederic and Irene Joliot-­ Curie who earned the Nobel Prize in Chemistry in 1935 [17]. After the findings of radioactive phosphorus isotopes, John Lawrence used 32P for leukemia treatments in 1936, which was the first medical application with radionuclides, and so Lawrence gained the name of Father of Nuclear Medicine [18]. In 1946, the radioisotope of 131I came into use for thyroid cancer imaging and treatments [19, 20], and another radioisotope of 99m Tc was discovered by Perrier and Segre owing to a generator for mass production of 99mTc in the 1960s [21, 22]. In the next years, rectilinear scanner and scintillation camera were created, and single-photon emission computed tomography (SPECT) and positron emission tomography (PET) were consecutively reported by David Kuhl and Roy Edwards, Michael Phelps and Edward Hoffman, respectively. Dual PET/ CT and SPECT/CT scanners were designed by Townsend and Hasegawa in 1990s, and the advanced dual-modality medical imaging has been broadly applied to clinical uses since the late 1990s.

1.2.4 Ultrasound Imaging Ultrasound (US), also known as sonography in medicine, is a quite safe imaging technique without nonionizing radiation, and it was firstly used in the ultrasound-echo device for mental flaw detections in 1940 [23]. Specifically, ultrasound was applied to brain disease diagnosis in 1941 and then was used to assess the thickness of bowel tissues by John Wild in 1949, who was named as Father of Medical Ultrasound [24]. Ultrasound imaging utilizes the reflection of ultrasonic waves by blood flow or different tissues with unique acoustic impedance under a certain frequency to reconstruct images for diagnosis of pregnancy, blood vessels, pelvis, abdomen, etc. [25]. The commercial ultrasound

imaging devices were developed until 1961, and a readily used handheld ultrasonic scanner was innovated in 1973, and CO2 microbubbles were proposed as US contrast agents for ultrasonography enhancement in hepatic tumors diagnosis in 1986 [26].

1.3 Molecular Imaging Due to the rapid development of various noninvasive imaging technologies and the urgent demand for early-staged detection and diagnosis of fatal treatments with a positive prognosis for patients, molecular imaging at a cellular level with in  vitro and in  vivo high-resolution image quality was proposed by Ralph Weissleder in his article of Molecular Imaging: Exploring the Next Frontier presented, and this concept acquired consensus in the second Biomedical Imaging Symposium of National Institute for Health (NIH) and Radiology Society of North America (RSNA) in 1999 [27]. Molecular imaging provides an opportunity to understand physiological or pathological changes at the molecular or cellular level with high sensitivity and biological specificity by conjugating molecular probes with targeting ligands and leading them to specific receptors [28]. It affords a new vision for us to disclose diseases and disease-related molecular mechanisms with spatial and temporal resolutions so as to improve the diagnosis accuracy and treatment precision [28, 29]. In addition, it also utilizes specific biomarkers for the early detection of chronic diseases and the real-time evaluation of disease treatments by recognizing premalignant molecular abnormalities instead of traditional time-­ consuming and invasive paradigms (Fig. 1.2) [30].

1.3.1 Molecular Optical Imaging The first medical optical imaging occurred in 1993 that NIR Raman and fluorescence spectroscopies were exploited to differentiate normal and malignant tissues [31]. Afterward,

1  Evolution from Medical Imaging to Visualized Medicine

Radiation type

5

γ-ray

X-ray

Ultraviolet

Atomic nuclei

Atoms

Molecules Bacteria

Visible

Infra-red Microwave

Radio

Scale of wavelength Insects

Cells

Human

Wavelength (l/m) 10–13

10–11

10–8

4x10–7

7x10–7

10–3

10–1

Frequency (n/GHz) 3x1012

3x1010

3x107

7.5x105

4x105

300

3

Energy (E/J) 10–12

2x10–14

2x10–17

3x10–19

2x10–22

Energy transitions Biological effect Imaging techologies

Isomeric transition

Bondbreaking

5x10–19

Electronic transitions

Ionization-DNA damage Nuclear imaging

X-ray & CT

Ultraviolet imaging

Electronic transitions

Vibrational transitions

10–24

Rotational transitions

Heating Microwave tomography Fluorescent Infra-red imaging thermography & Microwave radar imaging Photo-chemical effects

Nuclear & electronic spin Induced currents MRI & US

Images

Apparatus

Fig. 1.2  Various molecular imaging modalities and their characteristics

Martin Chalfie found the Aequorea victoria green fluorescent proteins (GFP) that were expressed by prokaryotic (Escherichia coli) or eukaryotic (Caenorhabditis elegans) cells, with which gene expression and localized proteins in living organisms could be detected without exogenous cofactors [32]. At present, molecular optical imaging refers to fluorescence imaging (FLI) or bioluminescence imaging (BLI) that employs fluorophores or biochemical reaction of enzymes (e.g., luciferases) as imaging probes. Due to a limited penetration in tissues (1–2 mm in depth) as well as the unavoidable nature of light scattering and absorption, medical optical imaging is always used for imaging and diagnosis of superficial diseases, but not applicable to a whole-body human imaging. In particular, the fluorescence imaging has found their superb application to the intraoperative navigation, which provides unprecedented convenience to the determination of tumor lesions and boundaries for surgical resection (Fig. 1.3) [33].

1.3.2 Molecular Nuclear Imaging A pool of small molecules, peptides, monoclonal antibodies (MoAbs), affibodies, aptamers, and proteins with varied molecular weight can be selected as targeting ligands to conjugate with radionuclides to serve molecular nuclear imaging. 99m Tc- and 111In-labeled fragments of monoclonal antibody were firstly applied to melanoma diagnosis by Siccardi in 1986 [33]. After that, peptides with a lower molecular weight but higher binding affinities and rapid blood clearance became radiopharmaceutical candidates [34]. Currently, molecular nuclear imaging including PET and SPECT has been widely investigated in fundamental research and has become a prevailing clinical technique for early detection and treatment assessment of various cancers in that it can dynamically provide pathological information with excellent sensitivity and track the potential cancerous lesions in vivo [35, 36].

Y. Shi and Z. Liu

6

a

b

c

Fig. 1.3  The NIR-II fluorescence imaging using a pH-­ responsive quantum dots (QDs) to navigate the photothermal therapy and FLI-guided surgery. (a, b) The fabrication and TEM images of QD-based fluorescent

probes; (c) NIR-II fluorescence images of 4  T1 breast tumor bearing mice at different time points. Note: Reprinted with permission from Wang et  al. [33]. Copyright 2022 Elsevier

1.3.3 Molecular MR Imaging

1.3.4 Multimodality Imaging

Molecular MR imaging exhibits a high spatial resolution and an unlimited penetration depth in tissues. So as to further enhance the image contrast and imaging sensitivity, various MR contrast agents (such as gadolinium (Gd)-based particles and superparamagnetic iron oxide particles) with good biocompatibility and targeting performances for T1- and T2-weighted MRI have been developed since the 1980s [37]. For instance, Gd-paramagnetic liposomes were synthesized in 1988 for liver-targeted MRI of Balb/c mice with a T1 signal enhancement of 150% in vivo [38]. In 1999, Sipe’s group reported a Fe-based MR imaging probe for intracellular labeling which specifically simulated perivascular mononuclear cells infiltration in acute inflammatory lesions [39]. Nowadays, the development and medical translation of novel MR imaging probes with multifunctionality has been a hot topic in the fields of both basic research and industrials.

Every single imaging has its advantages and disadvantages in terms of sensitivity, image resolution, cost and availability, time span, and medical utility (Table  1.1) [40]. In this sense, multimodality imaging is defined as the incorporation of dual or multiple imaging modalities to gain complementary advantages and merged biological information in a single imaging process [41]. And corresponding multimodality imaging scanners have been designed and extensively applied to clinical uses [42]. Hasegawa and colleagues made a pioneering contribution in 1990 [43]. They combined the radionuclide emission with X-ray tomography into a single module to facilitate the first SPECT/ CT dual-modal imaging device [44–46]. The first PET/CT prototype scanner was built by CTI PET Systems in Knoxville in 1998 [47, 48]. As the accuracy and confidence of imaging interpretation were improved, the scan duration for dual-modal imaging was reduced [49]. In 2007, optical imaging was firstly combined with nuclear

7

1  Evolution from Medical Imaging to Visualized Medicine Table 1.1  A comparison of various imaging modalities Modality X-ray Computed tomography Nuclear imaging MR imaging Ultrasound imaging Optical imaging

Molecular nuclear imaging Molecular MR imaging Multimodality imaging

Advantages High sensitivity High tissue penetration 3D anatomical information High tissue resolution High sensitivity Deep penetration depth High resolution Low cost, easy availability High sensitivity, low cost Real-time navigation accessible High sensitivity Targeting availability High resolution Targeting availability Complementary advantages of merged imaging modality

Disadvantages Ionization radiation Ionizing radiation Radiation risks Poor spatial resolution Time-consuming, low sensitivity Low reproducibility Inconvenience for whole-body human imaging, poor tissue penetration Poor spatial resolution Time-consuming –

imaging by Christophe for gene expression, and the PET/CT/BLI tri-modal imaging system was shortly developed for melanoma metastases with an expected image co-registration [50]. Presently, the multimodality imaging techniques have found tremendous medical applications in the clinics and also serve the COVID-19 epidemic diagnosis [51, 52].

Imaging utilities Bones, blood vessels Lung, digestive tract Tumors, chest Cardiac conditions Tumors, chest, head Musculoskeletal and neurological conditions. Pregnancy, blood vessels, pelvis, abdomen, etc. Superficial tumors

Early-stage tumor detection and diagnosis Brain, tumors, bones Early-stage detection and diagnosis of diseases

imaging analysis, med-data managements, image-guidance surgery, artificial intelligence (AI)-based image assay, VR/AR-aided simulation, brain imaging, and cognitive investigations [54]. The earliest AI-aided medical application was an AI-guided expert system (MYCIN) for infection diagnosis that was established at Stanford University in 1972 [55]. Nowadays, the combination of molecular imaging and artificial intelligence will reshape 1.4 Visualized Medicine the future of personalized diagnosis, health management, and robotic precision surgery. The Precision Medicine Initiative was started by AI-aided image diagnosis will relieve medical Obama in 2014, and in the next year, the eighth doctors from tremendous image data analysis, annual meeting of World Molecular Imaging and only valuable images will be sent to doctors Congress (WMIC) proposed Precision Medicine for further judgments and final medical Visualized as the main theme to outlook the future determinations. Similar to robotic precision trends of molecular imaging [53]. Precision surgery, AI-assisted image animation will be medicine calls for molecular imaging as a lesion-­ playing a crucial role in both preoperative directed technique for precise detection and arrangements and intraoperative image determination, a visualized strategy to record navigations [56]. Moreover, health management medical treatment processes for individualization, appliances associated with flexible bio-sensors and a useful means to guide devices and apparatus will make the postoperative recovery wellfor targeting therapy. Thus, molecular imaging, organized, intelligently proposed, and remotely as the basis of visualized medicine, provides the visualized. All the health information from the requisites of cellular/molecular theranostic medication dosage and patients’ physical data availability and has built a bridge to merge state- will be dynamically collected and transmitted to of-the-art intelligent technologies such as healthcare staffs in a real-time visualized manner.

8

Hence, the development of visualized medicine is on the highway to bring more and more upcoming revolutions to modern medicine.

1.4.1 VR/AR-Aided Technology Digital reality, such as virtual reality (VR) and augmented reality (AR) as brand-new tools for 3D medical visualization, will largely expand physicians’ field of vision into complexed anatomy during the process of surgery planning and virtual education [57]. Virtual reality builds up an animated circumstances or a completely simulated scene by virtual representations of the real medical situation, and augmented reality reinforced the virtual representation by overlapping with the real-world surroundings [58]. In short, AR integrates the reality with supplementary virtual information so that VR-AR may be conceptualized into a virtuality-reality continuum as shown in Fig. 1.4 [58, 59]. The VR/ AR-aided technology has been applied in medicine since the early 1990s, and it is currently taking substantial changes to traditional medical diagnosis and treatments, especially to the styles of visualization medicine [60, 61]. VR/AR aided-­ technology may also serve interventional and surgical operations and help physicians make critical decisions and interpretations on public health managements [62]. As an example, Miura et al. set up an AR-aided medical system for precise depth perception while suturing in a laparoscopic surgery [63]. This system could estimate the distance and the angel between the forceps tip and the plane by mimic virtual shadows, of which color and Fig. 1.4  An illustration of the VR/AR-aided technology and the virtuality-reality continuum

Y. Shi and Z. Liu

orientation changed with the operator’s direction on a display by image processing. The proof-of-­ principle tests revealed that the suturing error decreased as expected, which facilitated the robotic surgery with a 3D endoscope. Besides that, Zhang established a co-axial projective imaging (CPI-2) system for AR-aided surgical tele-mentoring in skin cancer surgery [64]. This system realized a wirelessly remote transmission of surgical images with virtual annotations between the experienced specialist at a remote site and a less experienced healthcare personal at a local ward. Compared to a monitor-aided tele-­ mentoring, the CPI-2 system worked with higher efficiency and more precision in remote image guidance owing to the reduced focus shift and the subjective mapping elimination.

1.4.2 AI Personalized Diagnosis With the unprecedented breakthroughs in machine learning algorithms-based artificial intelligence (AI) technology, AI personalized medical applications have been built for disease diagnosis, image segmentation, and outcome predictions [65–67]. Machine learning, traditionally divided into unsupervised, supervised, and reinforced learning [68], refers to an algorithm for the performance improvement in a set of prediction/decision-making tasks by model building of training databases [69, 70]. Deep learning is a subset of machine learning with hierarchical organizations and multiple levels for automatic extraction of meaningful features. Traditionally, suspected lesions require a reasonable and systematic judgment on

1  Evolution from Medical Imaging to Visualized Medicine

radiology images beyond human eyes, and in this sense machine learning may be applied with electronic medical records to serve lesion diagnosis, radiotherapy dose prediction, treatment planning, image segmentation, as well as patient classifications [70, 71]. In addition, AI-aided radiomics has brought much more accuracy than computer-aided diagnosis, and it has been widely applied in oncology-oriented image analysis [56]. For instance, intracerebral hemorrhage (ICH) is usually diagnosed by X-ray/ CT for location and size determinations of hemorrhages. Nevertheless, this process is always time-consuming and fallible just because of manual segmentation. Anupama developed an ICH diagnosis model by the synergy of GrabCut-­ based segmentation and deep learning (GCS-DL) [72]. This model exploited Gabor filtering for noise removal to improve the image quality. On the other hand, it was applied to lesion identification, when Softmax layer severed as a classifier. This model demonstrated desirable performances in the ICH diagnosis (sensitivity, 94.01%; specificity, 97.78%; precision, 95.79%; and accuracy, 95.73%).

1.4.3 Image-Guided Robotic Precision Surgery Surgical robots have been approved for clinical uses in the United States in 2000. Generally, the control module is manipulated by a physician, and the mechanical arms work for precision and delicate surgical operations such as endoscopy, incision, stitching, stabilization, and treatments. Thus, the imaging/visualization module is one of the three important modules in surgical robots which transmits high-definition images/videos and detective signals between the control module and arm terminals, reads out a preoperative surgery plan to the monitoring system, and facilitates the real-time image guidance/ navigation in a robotic precision surgery [73]. In 2020, Charles Evans, the head of gastrointestinal surgery at the University Hospital of Coventry & Warwickshire, offered an urgent surgery to seven patients with gravely bowel cancer with surgery

9

robots [74]. The surgery robots not only provided high efficiency for complicated surgical operations but also allowed for the tiniest incisions to avoid the infection risks and shorten the recovery in h­ ospital. Since 2000, a number of commercial surgical robots have been developed and entered the international market, and many manufacturers such as CMR Surgical, Medtronic, Verb Surgical, and Auris Health have produced a series of robots for versatile clinical applications. In corporation with advanced AI technologies, surgical robotics are expected to largely broaden their clinical applications, expand the scope for image guidance and visualization utilities, enhance their decision-making capability, and serve the precision surgery with advanced automation and intelligence in the coming future.

1.4.4 Intelligent Traditional Chinese Medicine Traditional Chinese medicine (TCM) has experienced thousands of years of evolution and seen roaring developments in recent years, and visualization, listening, enquiry, and palpation are common diagnostic techniques [75]. In the 1970s, it began to combine AI technology and initiated the AI-assisted theranostic innovations in which AI helped bridge TCM and modern medical sciences to the evidence-based medicine by means of advanced med-data acquisition, meta-data analysis, and healthcare managements [55]. For instance, GUO proposed a CM RuleDeep learning model by the combination of deep learning and AI-aided TCM diagnosis rules to achieve the diagnosis of pneumonia with dyspnea and cough [76]. The profiles of human pulses, including pulse shape, frequency, rhythm, patency, amplitude of fluctuation, etc., reflect comprehensive health information of viscera function, bloodstreams, and physiological status of the human body. The generation of pulses is closely related to heart-­ beating, blood circulation, and the coordination of various viscera, and so characteristic recognition and significance analysis on pulses may be considered as a specific index to guide

10

Y. Shi and Z. Liu

Fig. 1.5 An intelligent TCM-based pulse detection apparatus for pulse parameter analysis, pulse mapping output, pulse characteristics tracking, and disease situation

evaluations. Image courtesy of Dr. Peng ZHOU, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University

clinical dialectical treatments. Zhou et  al. designed an intelligent pulse detection apparatus with advanced stepless pneumatic pressurization and high-precision anti-overload sensors (Fig. 1.5). It proved that this instrument accurately simulated traditional Chinese medicine palpation fingering, collected pulse position and breath numbers, analyzed pulse parameters and categories, and output standard pulse mapping. Moreover, it recorded and tracked the changes of pulse characteristics at different periods, which were quite valuable for the evaluation of disease situation and therapy assessment. With the evergrowing demands for AI-assisted TCM diagnosis and a roaring number of TCM database to be established, it is believed that the intelligent traditional Chinese medicine will be playing a brilliant role and greatly enrich the spectrum of visualization medicine.

analysis, machine learning-based simulation, and next-generation treatment strategy (Fig.  1.6). During this process, state-of-the-art medical instruments will be deeply integrated with intelligent meta-data managements and multifunctional biomaterials to produce innovative approaches for visualized medicine. In particular, the COVID-19 outbreak has propelled visualized medicine forward on fast large-scale diagnosis, meta-data trafficking, and drug development with AI-related technology. As we have seen, VR techniques provide an opportunity for simulated and interactive education to physicians [77, 78], and Prof. Honggang Yu’s team built up a deep learningbased diagnostic system for COVID-19, which detected tiny lung lesions by CT images and apparently rivaled expert radiologists [79]. Surgical robots have replaced person-to-person contact under the COVID-19 pandemic condition and achieved a remotely controlled treatment to COVID-19 patients in Wuhan, China [80]. An ontology-based side effect prediction framework (OSPF) utilizing deep learning technology has been developed to evaluate the TCM prescription side effects on COVID-19 [81]. Today, visualized medicine shows great importance, and due to its

1.5 Outlook Visualized medicine goes beyond traditional medical imaging and molecular imaging, and provides abundant anatomic and pathological information to better serve intelligent meta-data

1  Evolution from Medical Imaging to Visualized Medicine

11

Fig. 1.6  The constitute of visualized medicine and its application in COVID-19

7. Shampo MA, Kyle RA, Steensma DP.  In Isidor Rabi—1944 Nobel Laureate in Physics, Mayo Clinic Proceedings, Elsevier: 2012; p e11. 8. Rabi II, Zacharias JR, Millman S, Kusch P.  A new method of measuring nuclear magnetic moment. Phys Rev. 1938;53(4):318. 9. Mansfield P, Grannell PK. NMR 'diffraction' in solids? Acknowledgments  This work was financially supported J Phys C Solid State Phys. 1973;6(22):L422–6. by the National Natural Science Foundation of China (No. 10. Lauterbur PC.  Image formation by induced local 21575106 and No. 82072057), the Emerging Engineering interactions: examples employing nuclear magnetic Education Research and Practice Program of Ministry of resonance. Nature. 1973;242(5394):190–1. Education (No. E-YGJH20202801), and the Tianjin 11. Mansfield P.  Multi-planar image formation using University International Teaching Resources Construction NMR spin echoes. J Phys C Solid State Phys. Program for Graduate Education (No. ENT20019). The 1977;10(3):L55–8. authors are grateful for the technical support from Xinru 12. Damadian R. Apparatus and method for detecting KONG, Yanxi YANG, and Xueli REN. cancer in tissue. United States Patent 3789832. Mar 17, 1972. 13. Damadian R, Goldsmith M, Minkoff L.  NMR in cancer: XVI. FONAR image of the live human body. References Physiol Chem Phys. 1977;9(1):97. 14. Damadian R. Field focusing n.m.r. (FONAR) and the 1. Doi K.  Diagnostic imaging over the last 50 years: formation of chemical images in man. Philos Trans R research and development in medical imaging science Soc Lond Ser B Biol Sci. 1980;289(1037):489–500. and technology. Phys Med Biol. 2006;51(13):R5–R27. 15. Curie I.  Un nouveau type de radioactivité. Comptes 2. Wolbarst AB, Hendee WR. Evolving and experimental rendus des seances de l'Academie des sciences. Technologies in Medical Imaging. Radiology. 1934;198:254–6. 2006;238(1):16–39. 16. Jacquemond L-P.  Irène Joliot-Curie and the 3. Pichler BJ, Wehrl HF, Judenhofer MS. Latest advances Discovery of “Artificial Radioactivity”. In Women in in molecular imaging instrumentation. J Nucl Med. their Element: Selected Women’s Contributions to the 2008;49(Suppl 2):5S. Periodic System, World Scientific: 2019; pp 361–373. 4. Council, B.  C. Major John Hall-Edwards-­ 17. Joliot F, Curie I. Artificial production of a new kind of Birmingham City Council. https://web.archive.org/ radio-element. Nature. 1934;133(3354):201–2. web/20120928204852 / http://www.birmingham.gov. 18. Lawrence JH, Tuttle LW, Scott KG, Connor uk/xray. CL. Studies on neoplasms with the aid of radioactive 5. Rabi II, Millman S, Kusch P, Zacharias JR. The phosphorus. I.  The total phosphorus metabolism molecular beam resonance method for measuring of normal and leukemic mice. J Clin Invest. nuclear magnetic moments. The magnetic moments 1940;19(2):267–71. of 3Li6, 3Li7 and 9F19. Phys Rev. 1939;55(6):526. 19. Hertz S, Roberts A.  Radioactive iodine in the study 6. Rabi II.  Space quantization in a gyrating magnetic of thyroid physiology; the use of radioactive iodine field. Phys Rev. 1937;51(8):652. therapy in hyperthyroidism. J Am Med Assoc. 1946;131:81–6.

innovations, brand-new technologies and devices will reshape our future, come into use for healthcare, and make the science of medicine visible and colorful.

12 20. Seidlin SM, Marinelli LD, Oshry E.  Radioactive iodine therapy; effect on functioning metastases of adenocarcinoma of the thyroid. J Am Med Assoc. 1946;132(14):838–47. 21. Bonte FJ, Curry TS, Oelze RE. Tumor scanning with intravenous I-131 HSA. Radiology. 1966;86(4):742–3. 22. Williams JE.  Donner laboratory: the birthplace of nuclear medicine. J Nucl Med. 1999;40(1):16N. 23. Dussik K.  On the possibility of using ultrasound waves as a diagnostic aid. Neurol Psychiatr. 1942;174:153–68. 24. Watts G. John Wild. BMJ. 2009;339:b4428. 25. Donald I, Macvicar J, Brown T.  Investigation of abdominal masses by pulsed ultrasound. Lancet. 1958;271(7032):1188–95. 26. Matsuda Y, Yabuuchi I. Hepatic tumors: US contrast enhancement with CO microbubbles. Radiology. 1986;161(3):701–5. 27. Lanza GM, Abendschein DR, Yu X, Winter PM, Karukstis KK, Scott MJ, Fuhrhop RW, Scherrer DE, Wickline SA.  Molecular imaging and targeted drug delivery with a novel, ligand-directed paramagnetic nanoparticle technology. Acad Radiol. 2002;9(2):S330–1. 28. Weissleder R, Mahmood U.  Molecular Imaging. Radiology. 2001;219(2):316–33. 29. Wickline SA, Lanza GM.  Nanotechnology for molecular imaging and targeted therapy. Circulation. 2003;107(8):1092–5. 30. Weissleder R. Molecular imaging: exploring the next frontier. Radiology. 1999;212(3):609–14. 31. Liu C, Das BB, Glassman WS, Tang GC, Zhu H-R, Akins DL, Lubicz S, Cleary J, Prudente R, Celmer EJ.  In NIR Raman and fluorescence spectroscopies diagnose cancer!, Physiological Imaging, Spectroscopy, and Early-Detection Diagnostic Methods, SPIE: 1993; pp 188–194. 32. Chalfie M, Tu Y, Euskirchen G, Ward WW, Prasher DC.  Green fluorescent protein as a marker for gene expression. Science. 1994;263(5148):802–5. 33. Wang P, Li J, Wei M, Yang R, Lou K, Dang Y, Sun W, Xue F, Liu X.  Tumor-microenvironment triggered signal-to-noise boosting nanoprobes for NIR-­IIb fluorescence imaging guided tumor surgery and NIR-II photothermal therapy. Biomaterials. 2022;287:121636. 34. Liu S, Edwards DS, Barrett JA. 99mTc labeling of highly potent small peptides. Bioconjug Chem. 1997;8(5):621–36. 35. Thakur ML.  Radiolabelled peptides: now and the future. Nucl Med Commun. 1995;16(9):724–32. 36. Tian J, Bai J, Yan XP, Bao S, Li Y, Liang W, Yang X. Multimodality Molecular Imaging. IEEE Eng Med Biol Mag. 2008;27(5):48–57. 37. Moats RA, Fraser SE, Meade TJ. A “smart” magnetic resonance imaging agent that reports on specific enzymatic activity. Angew Chem Int Ed Engl. 1997;36(7):726–8.

Y. Shi and Z. Liu 38. Kabalka G, Buonocore E, Hubner K, Davis M, Huang L.  Gadolinium-labeled liposomes containing paramagnetic amphipathic agents: targeted MRI contrast agents for the liver. Magn Reson Med. 1988;8(1):89–95. 39. Sipe JC, Filippi M, Martino G, Furlan R, Rocca MA, Rovaris M, Bergami A, Zyroff J, Scotti G, Comi G.  Method for intracellular magnetic labeling of human mononuclear cells using approved iron contrast agents. Magn Reson Imaging. 1999;17(10):1521–3. 40. Kim EE.  Targeted molecular imaging. Korean J Radiol. 2003;4(4):201–10. 41. Townsend D. Multimodality imaging of structure and function. Phys Med Biol. 2008;53(4):R1. 42. Moseley M, Donnan G.  Multimodality imaging: introduction. Stroke. 2004;35(11_suppl_1):2632–4. 43. Hasegawa BH, Stebler B, Rutt BK, Martinez A, Gingold EL, Barker CS, Faulkner KG, Cann CE, Boyd D. A prototype high-purity germanium detector system with fast photon-counting circuitry for medical imaging. Med Phys. 1991;18(5):900–9. 44. Lang TF, Hasegawa BH, Liew SC, Brown JK, Blankespoor SC, Reilly SM, Gingold EL, Cann CE. Description of a prototype emission transmission computed tomography imaging system. J Nucl Med. 1992;33(10):1881–7. 45. Blankespoor S, Xu X, Kaiki K, Brown J, Tang H, Cann C, Hasegawa B.  Attenuation correction of SPECT using X-ray CT on an emission-transmission CT system: myocardial perfusion assessment. IEEE Trans Nucl Sci. 1996;43(4):2263–74. 46. Tang HR, Da Silva AJ, Matthay KK, Price DC, Huberty JP, Hawkins RA, Hasegawa BH. Neuroblastoma imaging using a combined CT scanner–scintillation camera and 131I-MIBG. J Nucl Med. 2001;42(2):237–47. 47. Beyer T, Kinahan P, Townsend D, Sashin D.  In The use of X-ray CT for attenuation correction of PET data, Proceedings of 1994 IEEE Nuclear Science Symposium-NSS'94, IEEE: 1994; pp 1573–1577. 48. Kinahan PE, Townsend D, Beyer T, Sashin D. Attenuation correction for a combined 3D PET/CT scanner. Med Phys. 1998;25(10):2046–53. 49. Meltzer CC, Luketich JD, Friedman D, Charron M, Strollo D, Meehan M, Urso GK, Dachille MA, Townsend DW. Whole-body FDG positron emission tomographic imaging for staging esophageal cancer: comparison with computed tomography. Clin Nucl Med. 2000;25(11):882–7. 50. Deroose CM, De A, Loening AM, Chow PL, Ray P, Chatziioannou AF, Gambhir SS.  Multimodality imaging of tumor xenografts and metastases in mice with combined small-animal PET, small-animal CT, and bioluminescence imaging. J Nucl Med. 2007;48(2):295–303. 51. Casas G, Rodríguez-Palomares JF.  Multimodality cardiac imaging in cardiomyopathies: from diagnosis to prognosis. J Clin Med. 2022;11(3):578.

1  Evolution from Medical Imaging to Visualized Medicine 52. Overcast WB, Davis KM, Ho CY, Hutchins GD, Green MA, Graner BD, Veronesi MC.  Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors. Curr Oncol Rep. 2021;23(3):1–15. 53. Manning HC.  World molecular imaging congress 2015: precision medicine visualized. Mol Imaging Biol. 2015;17(3):295–6. 54. Jain P. AI and the Future of Work in the United States. https://www.american.edu/sis/centers/security-­ technology/ai-­and-­the-­future-­of-­work-­in-­the-­united-­ states.cfm. 55. Feng C, Zhou S, Qu Y, Wang Q, Bao S, Li Y, Yang T.  Overview of artificial intelligence applications in Chinese medicine therapy. Evid Based Complement Alternat Med. 2021;2021:6678958. 56. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94. 57. Sutherland J, Belec J, Sheikh A, Chepelev L, Althobaity W, Chow BJW, Mitsouras D, Christensen A, Rybicki FJ, La Russa DJ. Applying modern virtual and augmented reality technologies to medical images and models. J Digit Imaging. 2019;32(1):38–53. 58. Milgram P, Takemura H, Utsumi A, Kishino F.  In Augmented reality: A class of displays on the reality-­virtuality continuum, Telemanipulator and telepresence technologies, Spie: 1995; pp 282–292. 59. Milgram P, Kishino F.  A taxonomy of mixed reality visual displays. IEICE Trans Inf Syst. 1994;77(12):1321–9. 60. Chinnock C. Virtual reality in surgery and medicine. Hosp Technol Ser. 1994;13(18):1–48. 61. Phillips JR.  Virtual reality: a new vista for nurse researchers? Nurs Sci Q. 1993;6(1):5–7. 62. Borad A.  The future role of augmented reality and virtual reality in medical imaging. https://www. einfochips.com/blog/the-­future-­role-­of-­augmented-­ reality-­and-­virtual-­reality-­in-­medical-­imaging/. 63. Miura S, Seki M, Koreeda Y, Cao Y, Kawamura K, Kobayashi Y, Fujie MG, Miyashita T.  Virtual shadow drawing system using augmented reality for laparoscopic surgery. Adv Biomed Eng. 2022;11:87–97. 64. Zhang F, Contreras CM, Shao P, Zhao L, Wu B, Li C, Lin F, Zhong X, Lang Z, Liu P.  Co-axial projective imaging for augmented reality telementoring in skin cancer surgery. Ann Biomed Eng. 2022:1–11. 65. Singh R, Wu W, Wang G, Kalra MK.  Artificial intelligence in image reconstruction: the change is here. Phys Med. 2020;79:113–25.

13

66. Wang M, Zhang Q, Lam S, Cai J, Yang R. A review on application of deep learning algorithms in external beam radiotherapy automated treatment planning. Front Oncol. 2020;10:580919. 67. Wang Y, Gou K, Guo X, Ke J, Li S, Li H. Advances in regulating physicochemical properties of mesoporous silica nanocarriers to overcome biological barriers. Acta Biomater. 2021;123:72–92. 68. Koza JR, Bennett FH, Andre D, Keane MA, Automated design of both the topology and sizing of analog electrical circuits using genetic programming. In Artificial intelligence in design’96, Springer: 1996; pp 151–170. 69. Koza JR, Bennett FH, Andre D, Keane MA.  Automated design of both the topology and sizing of analog electrical circuits using genetic programming. In Artificial Intelligence in Design’96, Gero, J.  S.; Sudweeks, F., Eds. Springer Science & Business Media: 1996; pp XI, 782. 70. Barragán-Montero A, Javaid U, Valdés G, Nguyen D, Desbordes P, Macq B, Willems S, Vandewinckele L, Holmström M, Löfman F.  Artificial intelligence and machine learning for medical imaging: a technology review. Phys Med. 2021;83:242–56. 71. Vial A, Stirling D, Field M, Ros M, Ritz C, Carolan M, Holloway L, Miller AA. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Transl Cancer Res. 2018;7(3):803–16. 72. Anupama C, Sivaram M, Lydia EL, Gupta D, Shankar K.  Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Pers Ubiquitous Comput. 2020;26:1–10. 73. Davenport TH, Glaser J.  Just-in-time delivery comes to knowledge management. Harv Bus Rev. 2002;80(7):107–11. 126 74. Cox D.  The future role of augmented reality and virtual reality in medical imaging. https://www. einfochips.com/blog/the-­future-­role-­of-­augmented-­ reality-­and-­virtual-­reality-­in-­medical-­imaging/. 75. Ma D, Wang S, Shi Y, Ni S, Tang M, Xu A.  The development of traditional Chinese medicine. J Tradit Chin Med Sci. 2021;8:S1–9. 76. Guo Y, Ren X, Chen Y-X, Wang T-J.  Artificial intelligence meets Chinese medicine. Chin J Integr Med. 2019;25(9):648–53. 77. Londei R, Esposito M, Diotte B, Weidert S, Euler E, Thaller P, Navab N, Fallavollita P. Intra-operative augmented reality in distal locking. Int J Comput Assist Radiol Surg. 2015;10(9):1395–403.

2

Medical Imaging Technology and Imaging Agents Jieting Wu and Huanhuan Qiao

Abstract

Medical imaging is a technology that studies the interaction between human body and irradiations of X-ray, ultrasound, magnetic field, etc. and represents anatomical structures of human organs/tissues with the implication of irradiation attenuation in the form of grayscales. With these medical images, detailed information on health status and disease diagnosis may be judged by clinical physicians to determine an appropriate therapy approach. This chapter will give a systematic introduction on the modalities, classifications, basic principles, and biomedical applications of traditional medical imaging along with the types, construction, and major features of the corresponding contrast agents or imaging probes. Keywords

Traditional imaging · Multimodal imaging · Classifications · Basic principles · Major contrast agents Supplementary Information The online version contains supplementary material available at https://doi. org/10.1007/978-­981-­32-­9902-­3_2. J. Wu · H. Qiao (*) Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China e-mail: [email protected]

Abbreviations Sm Samarium-153 Dy Dysprosium-165 166 Ho Holmium-166 169 Yb Ytterbium-169 201 Tl Thallium-201 68 Ga Gallium-68 82 Rb Rubidium-82 BLI Bioluminescence imaging CA Contrast agent CARS Coherent anti-Stokes Raman scattering CCD Charge-coupled device cROMP Colloidal radio-opaque and polymer CT Computed tomography DMPE 1 , 2 - D i m y r i s t o y l - s n - g l y c e r o 3-phosphoethanolamine DOT Diffuse optical tomography DTPA Diethylene triamine penta-acetic acid EM Energy migration EPR Enhanced permeability and retention ESA Excited state absorption ET Energy transfer FDA Food and Drug Administration FDG 2-[18F] Fluoro-2-deoxy-D-glucose FI Fluorescence imaging FRET Fluorescence resonance energy transfer Gd-DOTA Tetra-aza-cyclo-dodecane tetraacetic acid GSA Ground state absorption 153 165

© Springer Nature Singapore Pte Ltd. 2023 Z. Liu (ed.), Visualized Medicine, Advances in Experimental Medicine and Biology 1199, https://doi.org/10.1007/978-981-32-9902-3_2

15

16

IND Investigational new drug MB Microbubble MOT Medical optical tomography MRI Magnetic resonance imaging NB Nanobubble NIR Near-infrared NMR Nuclear magnetic resonance NPs Nanoparticles OCT Optical coherence tomography OI Optical imaging PAI Photoacoustic imaging PEO Polyethylene oxide PET Positron emission tomography PMT Photon migration tomography PS-b-PAA Poly (styrene-b-poly-acrylic acid) PTT Photo-thermal therapy QDs Quantum dots RGD Arginine-glycine-aspartic acid SLNs Sentinel lymph nodes SNPs Supramolecular nanoparticles SNR Signal-to-noise ratio SPECT Single-photon emission computed tomography SRS Stimulated Raman scattering

J. Wu and H. Qiao

TAI Thermo-acoustic imaging TAT Thermos-acoustic tomography TCO Trans-cyclo-octene UCAs Ultrasound contrast agents UCL Up-conversion luminescence UCNP Up-converting nanoparticle US Ultrasound

With the wide application of medical imaging, the importance of medical imaging technology in clinical practice is increasingly prominent. Medical imaging technology is not only very simple and convenient to operate; the final result of medical imaging technology diagnosis is not far from the actual symptoms of the patient [1–6]. With the continuous progress of science and technology, medical imaging technology and the accuracy of imaging equipment are also constantly improved. The application of medical imaging technology in clinical diagnosis can greatly improve the accuracy of clinical diagnosis (Fig. 2.1) [7].

Fig. 2.1  Multimodal imaging modalities for small animal imaging. Note: Reprinted with permission from Youn et al. [7]. Copyright © 2012 Published by Elsevier Korea LLC

2  Medical Imaging Technology and Imaging Agents

In this section, we will introduce advance technologies and modalities for biomedical imaging according to their classification. In addition, multimodal imaging, a combination of different imaging modalities with higher spatial resolution, greater sensitivity, and more detailed anatomical/biological information about the target disease will be focused on.

2.1 Traditional Medical Imaging Since X-rays were discovered in 1895, medical imaging has become an extremely high-tech field, including magnetic resonance imaging (MRI), computed tomography (CT) scanning, optical imaging (OI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and ultrasound (US) imaging [8–12]. All modalities have their own principles, advantages, and disadvantages in terms of resolution, sensitivity, depth of organizational penetration, and contrast quantification [4].

2.1.1 Magnetic Resonance Imaging In 1946, American physicists Felix Bloch and Edward Purcell discovered that nuclei in magnetic fields tilt when stimulated by high-­ frequency electromagnetic fields. When the high-frequency field is shut down, the nucleus releases the absorbed energy and returns to its original state. Thus, the nuclear magnetic resonance (NMR) phenomenon was discovered, which had previously been used only for chemical analysis. Felix Bloch and Edward Purcell shared the Nobel Prize in Physics in 1952. At the beginning of the long tough conditions for imaging, the range of application is limited by larger. Until 1968, Richard Robert Ernst improved the trigger pulse sequence and the analysis of algorithms, which greatly increased the sensitivity and imaging speed of signals. The NMR technology is gradually mature. Richard Robert Ernst also won the 1991 Nobel Prize in Chemistry. In 1973, Paul Lauterbur and Peter Mansfield have

17

completed the initial MRI system. Paul and Peter shared the 2003 Nobel Prize in Medicine for their work in magnetic resonance imaging.

2.1.1.1 Principle of MRI It has been known for decades that magnetic fields can pass through human tissues without causing any harmful negative effects on living organisms. Since then, the application of magnetic fields has expanded to the biomedical fields of diagnosis and treatment, such as drug and gene delivery [13–15], hyperthermia for cancer treatment [16, 17], and magnetic separation [18]. MRI is based on a different set of physical principles related to the behavior of atoms in the water in the magnetic field (usually described by their nuclei or protons). When exposed to a strong magnetic field (B0 > 2.0 T), the application of a B0 magnetic field causes a magnetic moment or rotation of hydrogen along the wave axis (Z axis). When these protons are disturbed by radiofrequency pulse from their equilibrium state, a voltage is generated in the receiving coil, which is characterized by a change in the magnitude of the voltage over time. Therefore, the relaxation time is measured, and then processed by the Fourier transform, and the signal is provided to construct the threedimensional image (Fig. 2.2) [19, 20]. 2.1.1.2 Contrast Agents in MRI The specificity of MRI was dramatically improved by using contrast agents, which has led to extensive expansion of this research area over the last three decades. Recently, great efforts have been made to develop and use MRI contrast agents. Paramagnetic and Superparamagnetic Contrast Agents Most MRI contrast agents are either paramagnetic gadolinium ionic compounds or superparamagnetic (iron oxide) magnetite particles [21]. Paramagnetic contrast agents are usually made of dysprosium (Dy3+), lanthanide metal gadolinium (Gd3+), or transition metal manganese (Mn2+), and the most commonly used

18

J. Wu and H. Qiao

Fig. 2.2  Principle of MRI. Schematic illustration of the MRI effect on the magnetic moment of hydrogen nuclei from water molecules contained in tissues to yield reconstructed images

metal atom is the lanthanide ion gadolinium [21] because it has higher magnetic moment and is the most stable unpaired electron ion. Due to the presence of unpaired electrons, the paramagnetic gadolinium contrast agents can shorten the adjacent water quality of T1 and T2 relaxation time. These effects increase the signal strength of T1-weighted image and reduce signal strength of T2-weighted image [22, 23]. Because of the increased risk of toxicity, these effects limited the application of gadolinium contrast agents in clinical use. Therefore, in the traditional clinical practice, T1 has a strong influence on T2 relaxation after giving extracellular drug evaluation of contrast agents containing transition metal ions, such as high-spin manganese ion (II) and (III) oxide superparamagnetic iron oxide ions [24–26]. Nanoparticle Contrast Agents Traditional contrast agents are usually poisonous, nonspecific, and hard to cross biological barriers and discharge rapidly [27, 28]. In recent years, the field of MRI contrast agents had developed polymer contrast agents. These contrast agents target a particular organization, go across biological barriers, and increase the residence time in the body, so they can be used as drug delivery agents. In order to increase stability, enhance image contrast, and reduce the toxicity

of contrast medium, Gd chelating agents such as tetraazacyclododecane tetraacetic acid (Gd-DOTA), diethylenetriaminepentaacetic acid, or macromolecular ligand have been exploring [27, 29]. Many nano-polymer with different structures are also being studied: micellar nanoparticle and polymers, dendritic and hyperbranched polymers, and polymersomes [27, 30–32]. These polymer composite shell and internal core can be modified by a specific customization, which have more advantages than the use of contrast agents (Fig.  2.3) [33]. The polymeric carriers can contain environment responsive groups with targeting ligand coupling and can also be loaded all sorts of contrast agents (CAs) and chemotherapy drugs, so as to improve the treatment efficiency of the sensitivity and provide additional therapy function (Fig.  2.3) [34].

2.1.2 Computed Tomography Since Roentgen discovered X-rays, it began to be used to detect human disease on medicine. But for many diseases, conventional X-ray imaging is all light body structure of overlapping images, which couldn’t provide certain scan results [35]. Then, scientists began looking for a kind of new things to remedy the inadequacy of checking

2  Medical Imaging Technology and Imaging Agents

19

Fig. 2.3  Contrast agents in MRI. Synthesis of dendritic MRI contrast agents with octasilsesquioxane core. Strategic use of dendrimer size to achieve organ-­specific

imaging. Note: Reprinted with permission from Tang et al. [27]. Copyright © 2012 Elsevier Ltd.

lesions with X-ray technology. In 1967, Allan Macleod Cormack completed the CT reconstruction related mathematical problems. In 1972, Godfrey Newbold Hounsfield invented the CT scanner. It is found to be the advent of a revolutionary progress in medical imaging equipment and laid the foundation of the modern medical imaging equipment. In 1979, Allan Macleod Cormack and Godfrey Newbold Hounsfield were awarded the Nobel Prize in Physiology or Medicine due to the contribution on CT [36, 37]. In 1974, Robert Ledley, an engineer at Georgetown Medical Center, designed the full-­body CT scanner, which ushered in the era of full-body scanning.

incident X-ray photon. (iii) The total attenuation of the volume is measured by detecting the outgoing X-ray beam, and the opacity of the organization can be shown on the twodimensional tomographic image [39, 40]. The section image can be reconstructed using an algorithm, and the volumetric 3D reconstruction can be done by superimposing the cross section [41].

2.1.2.1 Principle of CT CT relies on processing of X-ray images taken from different angles using software programs, which is a noninvasive modality that is used to obtain three-dimensional images of tissues of interest. The CT scanner works as shown in Fig.  2.4 [38]. (i) X-rays must be produced and passed through the specimen. X-ray photons are produced by high-voltage electron beams that are accelerated in a vacuum chamber and directed to a heavy metal anode. (ii) The resulting electromagnetic radiation then penetrates the sample. According to the nature of molecules in vivo, X-ray photons interact with each other, leading to absorption, reflection, or scattering of

2.1.2.2 Contrast Agents in CT The contrast materials that dominate in CT usage today were not initially developed specifically for CT; these agents were initially developed as safe agents for fluoroscopic and plain-film radiography and then later adopted for use with CT. Many of the agents show similar enhancement at CT imaging. Iodine-Based Contrast Agents Barium sulfate suspension and water-soluble aromatic compound iodide are used as current CT contrast agents [42]. Due to the inherent toxicity of Ba2+ ions, the use of barium sulfate is limited to the gastrointestinal tract imaging. Because of the need to inject large number of CT imaging contrast agents, the high osmotic pressure of ionic contrast agent has become the focus of attention [43]. Injectable CT contrast agent is also composed of iodide compounds, and the most iodinated contrast agent is 1,3,5-­triiodobenzene. Functional groups such as

J. Wu and H. Qiao

20

a

b

Tra

nsla

te

Translation

X-ray tube

X-ray tube

c N0

X-ray pencil beam Scanned slice

Ni Detector

X-ray tube Rotate-1° increments

Slice Plane thickness = T Slice width X-ray beam thickness = T

Scintillation detector

N0

Rays

Nal detector

µ1 µ2 µ3 µ4 µ5 µ6 µ7 ............... µN

Ni

Wi

Fig. 2.4  Principle of CT. (a) CT arrangement; (b) X-ray transmission measurements; and (c) reconstruction matrix. Note: Reprinted with permission from Goldman et al. [38]. Copyright © 2007 by the Society of Nuclear Medicine, Inc.

carboxylic acids and amines are introduced into the iodized aromatic ring to improve its biocompatibility and water solubility [44]. And the nonionic water-soluble iodine contrast medium has also been developed [45]. Nanoparticle Contrast Agents An ideal CT contrast agent must contain nontoxic, physicochemical (viscosity and osmotic pressure) ingredients that are compatible with high concentrations of drug delivery and can be removed from the body in a short time [46]. As is known to all, nanomaterials have the advantages of extending the circulating half-life, passive accumulation in tumor sites through enhanced permeability and retention (EPR) effect, convenient surface modification, and integration of various functions, which are beneficial to the application in vivo [47]. Therefore, the advances in nanoscience provide possibilities for new type of CT contrast agents. In a recent report, approximately 37% of the iodine content was obtained through the use of a soft colloidal radioopaque and polymer (cROMP) nanoparticles consisting of cross-linked polystyrene-b-acrylic acid (PS-b-­PAA) and Lipiodol (Fig.  2.5) [48]. The half-life of cROMP is about 56 min in the rat blood, and it distributes to reticuloendothelial system after intravenous injection. Other CT contrast agents based on nanoparticles comprised of gold, silver, bismuth, tantalum, and other metal elements with high content are rapidly gaining popularity.

2.1.3 Positron Emission Tomography PET is based on metabolic imaging and quantitative analysis by using short-lived nuclides (e.g., 11C, 13N, 15 O, 18F positron nuclide tracer) to study human physiology, biochemical changes, neurotransmitter receptors, and genetic alterations [49]. PET not only can quickly get three-­ dimensional quantitative results of more bedding fault image and threedimensional scanning but also can dynamically observe the body’s physiological and biochemical changes or drug metabolites in the molecular level. PET has obtained widespread clinical acceptance, particularly for its role in oncology [50–53]. Together with 2-[18F]fluoro-2-deoxy-D-glucose (FDG), PET has become a key tool in the treatment of a variety of malignant tumor infections and inflammation [53–55].

2.1.3.1 Principle of PET PET is both an imaging technique and a functional method with quantitative capabilities in addition to the distribution pattern of radioactive drugs, which can be used in treatment decisions. A thorough understanding of the principles of PET is essential to the correct interpretation of PET results. Compounds or drugs are labeled as positrons that emit radionuclides. PET imaging is achieved by using these radioactive tracers. PET medical imaging mainly consists of short-lived positron emission isotopes such as 11C, 13N, 15O, and 18F produced by cyclotrons or gallium-68 (68Ga) and

2  Medical Imaging Technology and Imaging Agents

a

21

b edge of the tube

CNR

120 100

H H N S

IODINE

O

NH H

Clot

Targeted

O

Control

NHNH O

O

0.8 mm

80 60 40 20 0

N H

(CH2)4

cROMP (I)-Biotin x

Fig. 2.5  cROMP and in vitro targeting. (a) Preparation of cROMP nanoparticles. (b) In vitro targeting: representative CT images of human plasma clots treated with cROMP; the clots treated with control cROMP particles are

Fig. 2.6  Principle of PET. Annihilation radiation and coincidence detection. Note: Reprinted with permission from Basu et al. [56]. Copyright © 2014 Elsevier Inc. All rights reserved

impossible to distinguish from the surrounding water. Note: Reprinted with permission from Pan et  al. [48]. Copyright © 2009, American Chemical Society

g

Coincidence circuit Proton rich nucleus

b+

Positron range

rubidium-82 (82Rb) produced by generators. Positrons are positively charged nuclear particles with the same mass as electrons (Fig. 2.6). When an emitted positron collides with an electron in adjacent tissue, two 511  keV gamma rays (photons) are emitted in opposite directions. PET detectors are designed to record only pairs of photons opposite the detector when the photons reach a narrow window of time (usually 3–15 nanoseconds), which is called coincidence detection [56–58].

b–

3 – 15 ns limiting

Residual energy leading to nonco-linearity

2.1.3.2 Contrast Agents in PET Positron radionuclide labeled molecular imaging agents provide an opportunity for noninvasive monitoring of the biodistribution and pharmacokinetics of PET in  vivo. In the past decade, PET has been widely recognized for its advantages of high sensitivity and quantitative analysis in disease diagnosis, prognosis assessment, and treatment monitoring.

22

PET Radioisotopes Many positron emission isotopes have been used in PET imaging [59, 60]. According to the physical half-life, they can be divided into two classes. The short-life positron emitters include 15 O, 13N, 11C, 18F, and 68Ga [61]. Long-lived positron emitters have a half-life of several hours or days, such as 64Cu, 76Br, 89Zr, 124I, and 74As [62]. The most commonly used imaging agent in PET is fluorodeoxyglucose (18F-FDG), which is a useful tracer for the imaging of tissues requiring glucose, and is widely used in the imaging of tumors and inflammation [63]. However, unlike MRI, where clinical contrast agents are limited, PET tracers can be used in the completely different bioengineering imaging. For instance, 18 F-fluoride has a high affinity for hydroxyapatite, a major component of bone and calcified tissue, that helps to image bone metabolism and vascular calcification [64, 65]. Information about diseaserelated cell receptors and their concentrations in tissues can also be obtained by using radiolabeled peptides such as arginine-glycine-­aspartic acid (RGD; angiogenesis) and octreotide (neuroendocrine tumor) with 18F or 68Ga, or trastuzumab (HER2 receptor) with 89Zr or 64Cu [66, 67]. Nanoparticle Contrast Agents At present, positron emitter-labeled nanoparticles (NPs) have been widely applied and used in the field of electroosmosis. The main challenge is to develop specific targeted nanoprobes with simple and robust radiation labeling strategies. Common isotopes that can chelate or bind to NPs (similar to gadolinium ions used in MRI) include 18F, 11C, 15O, 13N, 64Cu, 124I, 68Ga, 82Rb, and 86Y [68]. Almutairi et  al. reported a unique 76 method of constructing Br-labeled biodegradable dendrimers for angiogenic PET imaging [69]. The nanoprobe was prepared with pentaerythritol as a dendritic core and functionalized with tyrosine groups, which was used for 76Br labeling and forming a protective shell of heterofunctional polyethylene oxide chain (PEO) to prevent dehalogenation in  vivo. The 76Br-labeled dendritic macromolecules

J. Wu and H. Qiao

showed good stability in serum of PBS and mouse within 48  h, and the pharmacokinetics could be regulated by appropriate levels of dendritic branches and PEO length. In another study, Hou et  al. reported trans-­ cyclooctene NPs (TCO NPs) for pretargeted PET imaging (Fig.  2.7) [70]. The TCO group is encapsulated in supramolecular NPs to prevent potential degradation in vivo. Compared with the traditional nanoparticle imaging platform with weak tumor uptake and excessive liver distribution, this method has approximately equal uptake in tumor and liver.

2.1.4 Single-Photon Emission Computed Tomography SPECT imaging is the most widely used method of nuclear imaging. This technique was introduced in the early 1980s as a tool for evaluating local cerebral perfusion and receptor density studies. It is less expensive than PET and has great availability in most neurological units [71]. The advantage of SPECT imaging is that it removes information from the plane rather than simply blurs it out like X-ray tomography did in early nuclear medicine [72, 73]. Another advantage of SPECT scanning is that it improves the quantification of cardiac function, the measurement of tumor/organ volume, and the quantification of radioisotope uptake [74].

2.1.4.1 Principle of SPECT SPECT scans were viewed as slices in transverse, sagittal, or coronal plane, which redirected to oblique short axis and/or long axis slices [75, 76]. Understanding the principle of SPECT is not only for producing high-quality scan images but also for recognizing image artifacts. SPECT imaging is based on detecting photons in a dedicated gamma camera. This photon is emitted by a single-photon radionuclide during its radioactive decay. In a gamma camera crystal, the photon is converted into an electrical pulse containing information about the position and energy of the incident ray after the gamma ray energy is converted into light [77]. The resulting

2  Medical Imaging Technology and Imaging Agents

a

23

b

g

c

d

f

e

Fig. 2.7  Schematic representation of a new approach for pretargeted PET imaging that leverages the utilities of supramolecular nanoparticles (SNPs) and bioorthogonal chemistry. (a) The preparation of supramolecular TCO NPs. (b, c) The accumulation and release of TCO NPs after intravenous injection in tumor. (d) 64Cu-Tz is injected for bioorthogonal reaction with tumor-retained TCO/

CD-PEI. (e) The unreacted 64Cu-Tz was cleared quickly from the body. (f) The resulting 64Cu-DHP/CD-PEI confines radioactivity in tumor, resulting in high-contrast PET imaging. (g) Chemical structures of the bioorthogonal reactions between TCO/CD-PEI and 64Cu-Tz. Note: Reprinted with permission from Zhao et al. [70]. Copyright © 2022 Informa UK Limited

distribution of this interaction was obtained in a circular orbit around the subject and then digitized and reconstructed into a tomographic image (Fig. 2.8) [78].

2.1.4.2 Contrast Agents in SPECT Compared with PET tracers, SPECT tracers have a relatively long half-life, which is more suitable for imaging with slow kinetics. In clinical

J. Wu and H. Qiao

24

a

b

c

Fig. 2.8  Principle of SPECT and contrast agents of CT and SPECT. (a) Schematic representation of the principle behind PET. (b) Schematic representation of the principle

behind SPECT. (c) Comparison between PET and SPECT. Note: Reprinted with permission from Fan-Lin Kong et al. [78]. Copyright © 2013 Fan-Lin Kong et al.

practice, a range of single-photon computed tomography radiotherapeutic drugs can be produced in the field using just-in-time kits, eliminating the need for expensive on-site cyclotron/radiochemical production equipment commonly used for PET tracers [79, 80]. Moreover, SPECT tracers have the advantages of low cost and easy access compared with PET tracers.

holmium (166Ho), ytterbium (169Yb), samarium (153Sm), technetium, rhenium, and dysprosium (165Dy) are also mainly used in research short-­ range therapies [77, 82].

SPECT Radionuclides When selecting radionuclides for SPECT imaging, various selection criteria such as physical half-life, energy range (100–300  keV), and stability of daughter radionuclides must be considered. With a physical half-life of 6 h, 99mTc is widely recognized as the radionuclide in SPECT which prevents excessive doses of radiation to patients and allows optimal imaging because its main g-line is at 140 keV. Another advantage of 99mTc is ease of access, and it doesn’t provide any extra dose to the patient or blur the image [81, 82]. In addition to 99mTc, Indium (111In), thallum (201Tl),

Functional Nanomaterials in SPECT When isotopes with longer half-lives are properly bound to NP probes, SPECT tracing can provide information about local organ function in  vivo, which help us to understand the distribution of radionuclides and the characterization of parameters such as radio-labeling stability, surface anchoring stability, and enzyme activity. 99m Tc has been combined with IONP, AgNP, AuNP, SNP, etc. Other 99mTc tracers such as 99m Tc-sulfur nanocolloidal particle were used for solid lipid nanoparticle (SLN) imaging of prostate cancer [83]. Applying filtered 99mTc sulfur nanocolloids to SLN localization was found to be comparable to 99mTc-Nanocoll human serum albumin preparations in clinical studies [84].

2  Medical Imaging Technology and Imaging Agents

2.1.5 Ultrasound Ultrasound (US) refers to sound waves with frequencies greater than 20,000 cycles per second (Hz) that cannot be detected by the human ear. In a variety of established models, ultrasound is a low-cost, nonionizing radiation, facilitates bedside examinations, and provides patients with short, comfortable treatment. Real-time and freehand operation makes it easy to quickly access almost any part of the human body. It has many clinical applications, such as detection and characterization of liver diseases [85, 86], exploring myocardial perfusion and wall motion [87–89], and imaging and perfusion of cerebral blood vessels [90, 91]. In the late 1940s, A-type (amplitude mode) ultrasonic diagnostic device applied to clinical practice was commonly used to measure interface distance and viscera diameter values and identify the physical properties of lesions. Then B-type (brightness mode) and M-type (motion mode) ultrasonic diagnostic device merged. Classified by scanning methods, B-type ultrasound has been developed for four generations, including manual linear scanning, mechanical scanning, electronic linear scanning, and electronic sector scanning. M-type is to add slow sawtooth wave in the brightness modulation type, which makes the echo spot scanning from left to right automatically, so it is also known as ultrasonic spot scanning method. In 1982, Aloka company in Japan developed the first two-dimensional color Doppler imaging device. Based on the Doppler effect, the D-type (Doppler mode) ultrasound diagnostic device began to appear and applied to show the blood flow, heart, and other motion information.

2.1.5.1 Principle of US In order to accurately interpret ultrasound images, it is essential to have a basic understanding of the physical principles involved in the generation of ultrasonic images. Diagnostic ultrasound is commonly used at frequencies between 2 and 15 MHz (106 cycles per second). Intravascular transducers are commonly used with frequencies up to 30 MHz,

25

and ultrasound biomicroscopic systems with frequencies up to 100  MHz have been reported [92, 93]. At these frequencies, the acoustic impedance of sound waves is transmitted through soft tissue. The acoustic impedance of a given tissue is the product of the propagation velocity of sound wave and the tissue density. In most soft tissues and blood, the rate of transmission is almost the same, at 1540  m per second [94]. Thus, the acoustic impedance of most soft tissues is primarily a function of tissue density. When two tissues of different densities are adjacent to each other, acoustic impedance mismatch will occur, and sound waves will be reflected through the mismatch [95].

2.1.5.2 Contrast Agents in US Ultrasound contrast agents are the basis of ultrasound molecular imaging [96]. The special importance of ultrasound contrast agents lies in producing strong acoustic scattering in the internal gas. When contrast agent microbubbles pass into the body’s blood circulation, the acoustic characteristics of human tissue are changed (backscattering coefficient, attenuation coefficient, acoustic velocity, resonant frequency, nonlinear effects, etc.). Microbubble-Based Ultrasound Contrast Agents Microbubble (MB) has greatly improved the imaging of organs and the management of diseases. Some MB on the market has been approved by the Food and Drug Administration (FDA) for clinical use. They have been used clinically to image the heart, liver, and veins [97, 98]. While MB provides a good comparison, it also has some disadvantages such as stability [99]. As the gas dissolves into the surrounding water environment, the size of the MB gradually diminishes and collapses. In addition, due to the size of MB, the imaging is limited to the vascular system. These limitations led us to reduce the size of ultrasound contrast agents (UCAs) to the nanoscale.

26

Nanobubble-Based Ultrasound Contrast Agents Although all commercially available UCAs are MB, nanobubble (NB) is easy to reach the tumor parenchyma through the small vascular endothelial space in the defective tumor vessels. The vascular endothelial space is usually too small for MB to pass through in tumors. Therefore, it has focus on NB in tumor imaging [100]. Some studies showed that the image enhancement of NB was similar to MB such as SonoVue or Definity in  vitro, but the tumor enhancement time of NB was longer than MB in vivo [101–103]. This enhancement is due to an increase in signal which is attributed to NB exudation in tumor blood vessels. They also found that the addition of DSPEPEG2000 lipids prevented NB from being cleared by the reticuloendothelial system, thereby increasing its retention. Another advantage of small NB size is that it doesn’t get trapped in the blood bank after intravenous injection. By contrast, MB generally accumulates in the blood and does not circulate as expected, which is a major limitation of its use in tumor-­targeted imaging. At the same time, MB will remain in its original form for a long time.

J. Wu and H. Qiao

Fig. 2.9  Principle of optical imaging. Scheme for a principle setup of planar fluorescence imaging and the problem of visualizing objects embedded deep in tissues. Note: Reprinted with permission from Licha et al. [106]. Copyright © 2005 Elsevier B.V. All rights reserved

can interact with light (interactions include absorption and scattering). The photons crash into the chromophores and disappear by absorption, releasing all the energy into the 2.1.6 Optical Imaging molecular electrons [105]. This absorption occurs only at a unique molecular-specific frequency. Optical imaging (OI) is a technique that use near-­ The emitted light is then filtered and detected by infrared (NIR) light (700–1000  nm) and visible a charge-­coupled device (CCD) camera (Fig. 2.9) light (400–700  nm) to provide molecular/ [106]. morphological/functional information to detect The early attempt to diagnose breast cancer absorption, scattering, and fluorescence with visible light was in the first half of the properties of cells or tissues [104]. In the 1990s, twentieth century, when a method called the identification of tissue optical properties and “diaphonography” was used, with a sensitivity the development in determining composition of about 58% [107, 108]. The most common used tissues opened a new era of OI, which made it technology is fluorescence imaging (FI). FI is an possible to identify structural changes and imaging technique using fluorescent molecular functional anomalies in tissues. groups and fluorescence signals [109, 110]. Unlike fluorescence imaging, bioluminescent 2.1.6.1 Principle of OI imaging (BLI) is produced by the oxidation of OI first excites an endogenous or exogenous luciferin in  vivo. Compared with FI, BLI can chromophobe within the range of interest through provide better signal-­to-­noise ratio (SNR) and an external light source. The term chromophore higher signal sensitivity without interference refers to any molecule in biological tissue that

2  Medical Imaging Technology and Imaging Agents

from its own fluorescence and endogenous bioluminescence [111].

2.1.6.2 Contrast Agents in OI Different from traditional imaging methods, OI has noninvasive, high spatial and temporal resolution and high sensitivity as a nonionizing radiation method [112]. The optical signal can be sensitive to interference such as fluorescence resonance energy transfer (FRET), photoelectron transfer, and self-quenching. Optical imaging has been hindered by the short penetration depth of the tissue and the background signal caused by light scattering in vivo. And the optical imaging probe can overcome these barriers [113]. Currently, various fluorescent dyes are widely used as optical imaging probes [114]. Many advances in optical imaging probes and techniques have been made using near-infrared light and/or anti-Stokes imaging techniques to maximize penetration depth and minimize background signals [115]. In addition, the

27

problem of limited photobleaching and absorption coefficient of fluorescent dyes can be overcome by developing nanoparticle-based optical probes. Quantum dots (QDs) have excellent optical properties, including high quantum yield, absorption coefficient, and optical stability, enabling long-term fluorescence imaging without photobleaching [116]. In addition, quantum dots have high multiphoton absorption coefficients, which can reduce out-of-focus excitation to achieve high resolution fluorescence imaging. An additional coating can further protect the QD core from oxidation. Surface chemistry influences the quantum dot’s propensity to aggregate, particularly in biological solutions (Fig.  2.10) [117]. Cadmium-free quantum dots have been developed as nontoxic fluorescent probes. In order to further improve the penetration depth, the second near-infrared region (1000–1700 nm) was also studied as an optical window. To date, quantum dots such as Ag2S and Ag2Se, as well as carbon nanotubes, have been applied to second

a

b

c

d

Fig. 2.10 Additional coating of the QD core. (a) Fluorescence-SERS QD-embedded silver bumpy nanoprobes. (b) Mn-doped ZnS (ZnS:Mn) QDs combining dual ligands, hydrophilic glutathione (GSH) ligands, and silica. (c) Dual-functional nanoprobe composed of an iron

oxide core surrounded by QD-embedded silica nanoparticles. (d) SiO2@QDs@PDA NPs. Note: Reprinted with permission from Pham et  al. [117]. Copyright © © 2021 MDP

J. Wu and H. Qiao

28

near-­infrared imaging through the single-photon process [116, 118, 119]. Up-conversion is a unique mechanism of anti-­ Stokes emission, in which multiple low-energy photons are absorbed through a true electron intermediate state and followed by a high-energy photon [120]. Due to its high efficiency, up-­ converting nanoparticle (UCNP) does not require a high-power pulsed laser. In addition, due to the long luminous life of UCNPs, time-gated imaging can be used to remove the scattered light from the tissue [121]. Several lanthanide-doped UCNPs are also designed to improve depth penetration, minimize tissue damage, reduce spontaneous fluorescence, and increase multicolor capacity for preclinical in vivo imaging and photothermal therapy (PTT) [122, 123]. When depth penetration is not a limiting factor, NPs loaded with fluorescent dyes can be used for image-guided tumor surgery [124–126]. For instance, the trial of fluorescent nucleocapsid silicon NPs (Cornell dots or C-dots) was approved by FDA Investigational New Drug (IND) for targeted molecular imaging [127]. NP-enabled optical imaging detection is expected to continue

Fig. 2.11  Principle of photoacoustic imaging. Schematic cartoons showing principles of different PAI systems. Note: Reprinted with permission from Liu et al. [128]. Copyright ©2020 Ivyspring International Publisher

to be an important area for NPs in cancer management.

2.2 Emerging Imaging Technologies Each imaging modality has its drawbacks, and none is ideal for providing all the information needed. Therefore, multimodal imaging can be used as a strategy to overcome the limitations of each imaging method and to provide complementary morphological, functional, and molecular information about tissue-engineered structures. In addition, multimodal imaging strategies tend to take advantage of the synergistic characteristics of different imaging techniques.

2.2.1 Photoacoustic Imaging Photoacoustic imaging (PAI) is an emerging biomedical imaging technology capable of displaying cellular and molecular functions with high detection sensitivity and spatial resolution. Over the past two decades, great efforts and

2  Medical Imaging Technology and Imaging Agents

progress have been made in the development of various PAI technologies with improved resolution and sensitivity. PAI’s main advantages include (1) large imaging depth in intact biological tissue; (2) high expandable penetration and spatial resolution; (3) no speckle artifact; and (4) no ionizing radiation. As a result, PAI has become a powerful tool for basic life science research, preclinical application, and drug discovery [128].

2.2.1.1 Principle of PAI PAI takes advantage of the advantages of ultrasound and optical imaging to convert light energy into ultrasonic energy. The photon energy absorbed by biological tissues temporarily increases the temperature after the nanometer laser pulse irradiation, causing thermoelastic expansion (Fig.  2.11) [128]. Ultrasonic or piezoelectric sensors can detect the resulting sound waves. 2.2.1.2 Contrast Agents in PAI In addition to conventional measurements such as anatomy and flow rate, PAI can provide information including the optical, physiological, and mechanical properties of tissues. PAI contrast agent must consist of a signal compound that generates the imaging signal and a targeted ligand that can read out a specific biological entity or process [129]. Many tissues exhibit inherent contrast in PAI, but the differences between healthy and diseased tissues in this contrast are often insufficient to be

29

adequately detected. NPs of PAI contrast agent have been developed for this purpose. Both organic and inorganic NPs are used in this relatively new imaging technique. Inorganic NP frames of metals (especially gold), QDs, UCNPs, and other semiconductors have been successfully used to visualize preclinical cancer lesion [130–133]. Carbon nanotubes also have been used in the study of the molecule PAI, although their biocompatibility remains an important issue [134]. Up to now, NP-enabled PAI technology is still in the early stages of exploration.

2.2.2 Thermoacoustic Imaging Thermoacoustic imaging (TAI) is another emerging biomedical imaging technology, with excitation sources involve far infrared light or microwaves. TAI provides higher spatial resolution than microwave imaging and receives images at a greater depth than most optical imaging techniques [135]. In a broad sense, TAI is an application of the photoacoustic effect. But TAI is based on different absorption mechanisms; it can capture information about relevant physiological and pathological dielectric properties within tissues (such as the distribution differences of certain polar molecules and ions). TAI may be used for early cancer detection and foreign material detection [136].

Amplifier Power Amplifier

Coupling Medium

Filter

Acoustic Transducer Acoustic Signal

Signal Generator

Electrode B

Imaging Object Electrode A

Fig. 2.12  The system of thermoacoustic imaging. Note: Reprinted with permission from Song et  al. [137]. Rights managed by AIP Publishing

30

2.2.2.1 Principle of TAI Under the conditions of heat limitation and pressure limitation, pulsed microwave irradiation of biological tissues can stimulate polar molecules of biological tissues to perform high-speed rotational motion and directional motion with ion generators (such as sodium ions and potassium ions, etc.). After absorbing energy, polar molecules and electrically charged ions generate motion and collide with surrounding tissues to produce heat, realizing the conversion of microwave energy to heat energy, thus leading to local heat expansion and cold contraction and generating ultrasonic signals (thermoacoustic signals). Ultrasonic transducer is used to receive thermoacoustic signals, which carries the microwave absorption difference characteristics of biological tissues. The signal is collected and stored in the computer by the data acquisition module. Image reconstruction algorithm is used to do the corresponding image reconstruction, so as to realize the imaging of biological tissues by TAI (Fig. 2.12) [137]. 2.2.2.2 Contrast Agents in TAI Some magnetic nanoparticles have been applied in TAI. Qin et al. investigated the feasibility of using Fe3O4 magnetic nanoparticles as a contrast agent to detect liver cancer in thermoacoustic tomography (TAT) in  vivo and in  vitro [138]. NMG2[Gd(DTPA)] is a paramagnetic ion compound with seven unpaired electrons in the 4f orbital of the Gd3+ ion. Charged ions and unpaired electrons interact with the microwave field to convert the absorbed microwave energy into heat energy. Qin et  al. confirmed the enhanced effect of NMG2[Gd (DTPA)] on thermoacoustic CT in in  vivo and in  vitro experiments [135].

2.2.3 Raman Imaging Raman scattering has long been used to analyze chemical components in biological systems. Because of its high chemical specificity and noninvasive detection ability, Raman scattering has been widely used in the screening, diagnosis,

J. Wu and H. Qiao

Fig. 2.13 Principle of Raman imaging, SRS, and CARS.  Energy level diagrams showing the processes of spontaneous Raman, SRS, and CARS. Note: Reprinted with permission from Vanden-Hehir et al. [146]. Copyright © 2019 by MDPI

and intraoperative guidance of cancer in recent years [139–141]. In order to overcome the weak signal of spontaneous Raman scattering, coherent Raman scattering and surfaceenhanced Raman scattering [142–145] were developed and applied in the field of cancer research. However, a major limitation of Raman imaging is the slow imaging speed due to the inefficiency of Raman scattering process. This problem can be largely overcome by a new Raman imaging technique called stimulated Raman scattering (SRS) microscopy. The SRS is a nonlinear optical process that excites molecules from the ground state to the vibrational excited state more efficiently than spontaneous Raman using two strong lasers (Fig.  2.13) [146]. SRS microscopy uses high-­ frequency modulating transmission and locking detection to achieve high sensitivity imaging [147–149]. It is closely related to coherent anti-­ Stokes Raman scattering (CARS) microscopy, another widely used nonlinear Raman imaging technique (Fig.  2.13). CARS microscopes use the same laser excitation and provide similar chemical contrast and sensitivity. However, automotive microscopes often suffer from nonresonant backgrounds, distortion to obtain Raman spectra, and limited sensitivity [150, 151].

31

2  Medical Imaging Technology and Imaging Agents

a

b

c

d

EM

core

EM

EM

shell

ESA ET

UCL

GSA

GSA

activator

activator

ESA

ET

UCL

GSA

activator

ETU

ET

UCL

GSA

UCL

GSA

sensitizer

activator

ETU

sensitizer accumulator migrator migrator

activator

EMU

Fig. 2.14 Principle of conversion luminescence. Key activator, AA), (c) GSA/ET (sensitizer-activator, SA), and mechanisms for UCL involve GSA, ESA, and ET steps, (d) EM mechanism. Note: Reprinted with permission from resulting in (a) GSA/ESA, (b) GSA/ET (activator-­ Nadorta et al. [152]. Copyright © 2020 Clearance Center

Fig. 2.15  Schematic of NIR-based optical coherence tomography system. Note: Reprinted with permission from Lee et al. [155]. Copyright © 2017 by Lee et al.

2.2.4 Up-Conversion Luminescence Up-conversion luminescence (UCL) is a new optical imaging technology. Up-conversion photoluminescence is a nonlinear effect in which several low-energy excited photons produce high-energy emission photons. This essentially interesting process has many applications in display technologies, solar energy harvesting, and biomedical imaging. The UCL process contains exciting a material (a solid material or a molecular system) with a lower energy photon, thus stimulating the emission of a higher energy photon. The process must satisfy the conservation of energy principle and ensure that two or less excited photons are

required for each high-energy photon emission (Fig. 2.14) [152]. The UCL process can occur in many complex but very efficient ways. In fact, it is one of these complex pathways (energy ­transfer up-conversion) that has transformed the field of UCL.  UCL involves photophysics at several centers: energy transfer (ET), ground state absorption (GSA), excited state absorption (ESA), and alternative energy transfer (EM).

2.2.5 NIR-Based Imaging Technologies In the past decade, considerable progress has been made toward a novel biomedical imaging

32

model that uses NIR light (700 nm