Medical Image Synthesis

Methods and Clinical Applications

Xiaofeng Yang

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English

CRC Press
06 February 2024

Oncology; Medical imaging; Biomedical engineering; Medical physics

Series: Imaging in Medical Diagnosis and Therapy

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.

Edited by: Xiaofeng Yang
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
Weight: 590g
ISBN: 9781032152844
ISBN 10: 1032152842
Series: Imaging in Medical Diagnosis and Therapy
Pages: 308
Publication Date: 06 February 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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 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.