Description
Book SynopsisImage 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 explor
Table of Contents
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
