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.
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.
Part 1: Methods and Principles
1. Non-Deep-Learning-Based Medical Image
Synthesis Methods
2. Deep Learning-Based Medical Image Synthesis Methods Part
2: Applications of Inter-Modality Image Synthesis
3. MRI-Based Image
Synthesis
4. CBCT/CT-Based Image Synthesis
5. CT-Based
DVF/Ventilation/Perfusion Imaging
6. Image-Based Dose Planning Prediction
Part 3: Applications of Intra-Modality Image Synthesis
7. Medical Imaging
Denoising
8. Attenuation Correction for Quantitative PET/MR Imaging
9.
High-Resolution Medical Image Estimation
10. 2D-3D Transformation for 3D
Volumetric Imaging
11. Multi-Modality MRI Synthesis
12. Multi-Energy CT
Transformation and Virtual Monoenergetic Imaging
13. Metal Artifact Reduction
Part 4: Other Applications of Medical Image Synthesis
14. Synthetic
Image-Aided Segmentation
15. Synthetic Image-Aided Registration
16. CT Image
Standardization Using Deep Image Synthesis Models Part 5: Clinic Usage of
Medical Image Synthesis
17. Image-Guided Adaptive Radiotherapy Part 6:
Perspectives
18. Validation and Evaluation Metrics
19. Limitation and Future
Trends
Xiaofeng Yang received B.S., M.S., and Ph.D. degrees in biomedical engineering from Xian 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.
