1st Edition

Medical Image Synthesis Methods and Clinical Applications

Edited By Xiaofeng Yang Copyright 2023
    318 Pages 38 Color & 36 B/W Illustrations
    by CRC Press

    318 Pages 38 Color & 36 B/W Illustrations
    by CRC Press

    Image synthesis across and within medical imaging modalities is an active area of research with broad applications in radiology and radiation oncology. This book covers the principles and methods of medical image synthesis, along with state-of-the-art research.

    First, various traditional non-learning-based, traditional machine-learning-based, and recent deep-learning-based medical image synthesis methods are reviewed. Second, specific applications of different inter- and intra-modality image synthesis tasks and of synthetic image-aided segmentation and registration are introduced and summarized, listing and highlighting the proposed methods, study designs, and reported performances with the related clinical applications of representative studies. Third, the clinical usages of medical image synthesis, such as treatment planning and image-guided adaptive radiotherapy, are discussed. Last, the limitations and current challenges of various medical synthesis applications are explored, along with future trends and potential solutions to solve these difficulties.

    The benefits of medical image synthesis have sparked growing interest in a number of advanced clinical applications, such as magnetic resonance imaging (MRI)-only radiation therapy treatment planning and positron emission tomography (PET)/MRI scanning. This book will be a comprehensive and exciting resource for undergraduates, graduates, researchers, and practitioners.


    Part 1: Methods and Principles

    1. Non-Deep-Learning-Based Medical Image Synthesis Methods 
    Jing Wang, Xiaofeng Yang
    2. Deep Learning-Based Medical Image Synthesis Methods 
    Yang Lei, Tonghe Wang, Xiaofeng Yang

    Part 2: Applications of Inter-Modality Image Synthesis 

    3. MRI-Based Image Synthesis 
    Tonghe Wang, Xiaofeng Yang
    4. CBCT/CT-Based Image Synthesis 
    Hao Zhang
    5. CT-Based DVF/Ventilation/Perfusion Imaging 
    Ren Ge, Yu-Hua Huang, Jiarui Zhu, Wen Li, Jing Cai
    6. Imaged-Based Dose Planning Prediction 
    Dan Nguyen

    Part 3: Applications of Intra-Modality Image Synthesis 

    7. Medical Imaging Denoising
    Yao Xiao, Kai Huang, Hely Lin, Ruogu Fang
    8. Attenuation Correction for Quantitative PET/MR Imaging 
    Se-In Jang, Kuang Gong
    9. High-Resolution Medical Image Estimation using Deep Learning 
    Xianjin Dai
    10. 2D-3D Transformation for 3D Volumetric Imaging 
    Zhen Tian
    11. Multimodality MRI Synthesis 
    Liangqiong Qu, Yongqin Zhang, Zhiming Cheng, Shuang Zeng, Xiaodan Zhang, Yuyin Zhou
    12. Multi-Energy CT Transformation and Virtual Monoenergetic Imaging 
    Wei Zhao
    13. Metal Artifact Reduction 
    Zhicheng Zhang, Lingting Zhu, Lei Xing, Lequan Yu

    Part 4: Other Applications of Medical Image Synthesis 

    14. Synthetic Image-Aided Segmentation 
    Yang Lei, Richard L.J. Qiu and Xiaofeng Yang
    15. Synthetic Image-Aided Registration 
    Yabo Fu, Xiaofeng Yang
    16. CT Image Standardization Using Deep Image Synthesis Models 
    Md Selim, Jie Zhang, Jin Chen

    Part 5: Clinic Usage of Medical Image Synthesis 

    17. Image-Guided Adaptive Radiotherapy 
    Yang Sheng, Jackie Wu, Taoran Li

    Part 6: Perspectives 

    18. Validation and Evaluation Metrics 
    Jing Wang, Xiaofeng Yang
    19. Limitation and Future Trends
    Xiaofeng Yang


    Xiaofeng Yang received B.S., M.S., and Ph.D. degrees in biomedical engineering from Xi’an Jiaotong University, China. He finished his Ph.D. training and thesis at Emory University. He completed his postdoctoral and medical physics residency training at the Department of Radiation Oncology, Emory University School of Medicine, where he is currently an Associate Professor. He is also an adjunct faculty in the Medical Physics Department at Georgia Institute of Technology, Biomedical Informatics Department at Emory University, and the Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Institute of Technology. Dr. Yang is a board-certified medical physicist with expertise in image-guided radiotherapy, deep learning, and multimodality medical imaging, as well as medical image analysis. He is the Director of the Deep Biomedical Imaging Laboratory at Emory University. His lab focuses on developing novel AI-aided analytical and computational tools to enhance the role of quantitative imaging in cancer treatment and to improve the accuracy and precision of radiation therapy. His research has been funded by the NIH, DOD, and industrial funding agencies. He has published over 180 peer-reviewed journal papers, and has received many scientific awards from SPIE Medical Imaging, AAPM, ASTRO, and SNMMI in the past several years. Dr. Yang was the recipient of the John Laughlin Young Scientist Award from the American Association of Physicists in Medicine in 2020. He currently serves as Associate Editor for Medical Physics and Journal of Applied Clinical Medical Physics.