AI-driven Medical Image Analysis in Precision Radiation Therapy provides a comprehensive overview of the latest developments in artificial intelligence for medical imaging, focusing on applications in precision radiation therapy. Written by a team of experts, it offers an accessible perspective on how AI is transforming cancer treatment for a broad audience, from computer science and engineering to the medical sector. The text covers key techniques such as image synthesis, segmentation, and registration, but its primary focus is on practical clinical applications. It showcases recent studies in image-guided and adaptive radiation therapy, real-time tumor motion tracking, and treatment response assessment for both photon and proton therapies. Furthermore, the book addresses the real-world challenges of implementing these AI techniques in a clinical setting, equipping readers with the practical knowledge needed for successful integration. It is an essential guide for students and newcomers, as well as a valuable reference for experienced medical physicists and radiation oncologists.
Key Features:
- Provides in-depth coverage of cutting-edge AI applications in medical image processing, including image synthesis, segmentation, and registration techniques specifically designed for radiation therapy contexts.
- Discusses real-world implementations of AI-driven technologies in precision radiation therapy.
- Addresses the practical challenges of integrating AI systems into clinical workflows.
AI-driven Medical Image Analysis in Precision Radiation Therapy provides a comprehensive overview of the latest developments in artificial intelligence for medical imaging, focusing on applications in precision radiation therapy.
Introduction.
Chapter 1 Medical Image Synthesis.
Chapter 2 Medical Image
Segmentation.
Chapter 3 Medical Image Registration.
Chapter 4 Empowering
Innovation in Medical Imaging with AI-Based Radiomics.
Chapter 5
Vision-Language Models for Medical Image Analysis.
Chapter 6 AI-driven
Image-Guided Radiation Therapy.
Chapter 7 Adaptive Radiation Therapy.
Chapter
8 Motion Management in Radiation Therapy.
Chapter 9 Proton and Flash Therapy.
Chapter 10 Artificial Intelligence in Brachytherapy.
Chapter 11 Treatment
Response Assessment and Prediction.
Chapter 12 The Promise and Peril of AI in
Clinical Practice. Perspectives. Bibliography. Index.
Xiaofeng Yang is the Paul W. Doetsch Professor and Vice Chair for Medical Physics Research in the Department of Radiation Oncology at Emory University School of Medicine. He directs the Deep Biomedical Imaging Lab, where he develops innovative AI-enabled analytical and computational methods to advance quantitative imaging in cancer diagnosis and treatment, with the goal of improving the accuracy and precision of cancer radiotherapy. Dr. Yang also holds adjunct faculty appointments in the Medical Physics and Machine Learning programs at the Georgia Institute of Technology, the Department of Biomedical Informatics at Emory University, and the Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech. He is a Fellow of SPIE.
Tonghe Wang, PhD, DABR, is an assistant attending physicist in the Department of Medical Physics at the Memorial Sloan Kettering at main campus. Dr. Wang received his BS in physics from Peking University in China in 2013 and PhD in medical physics from Georgia Institute of Technology in 2017, with research experience in iterative CT reconstruction. Dr. Wang completed a medical physics residency at Emory University in 2019 and stayed at Emory as an assistant professor and board-certified medical physicist before joining Memorial Sloan Kettering Cancer Center in 2022. Dr. Wang provide clinical physics services in all aspects of radiation therapy and specialize in brachytherapy and Gamma Knife. Dr. Wang is currently working on a variety of research projects, including image segmentation and image synthesizing. He is interested in improving automation in clinical workflow and enabling advanced treatment strategy.
