Description
Machine Learning in MRI: From Methods to Clinical Translation, Volume Thirteen in theAdvances in Magnetic Resonance Technology and Applications series presents state-of-the-art machine learning methods in magnetic resonance imaging that can shape and impact the future of patient treatment and planning. Common methods and strategies along the processing chain of data acquisition, image reconstruction, image post-processing, and image analysis of these imaging modalities are presented and illustrated. The book focuses on applications and anatomies for which machine learning methods can bring, or have already brought. Ideas and concepts on how processing could be harmonized and used to provide deployable frameworks that integrate into the clinical workflows are also considered.Pitfalls and current limitations are discussed in the context of how they could be overcome to cater for clinical needs, making this an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. By giving an interdisciplinary presentation and discussion on the obstacles and possible solutions for the clinical translation of machine learning methods, this book enables the evolution of machine learning in medical imaging for the next decade.- Brings together applied researchers, clinicians, and computer scientists to give an interdisciplinary perspective on the methods of machine learning in MRI and their potential clinical translation- Gives a clear presentation of the key concepts of machine learning- Shows how machine learning methods can be applied to MR image acquisition, MR image reconstruction, MR motion correction, MR image post-processing, and MR image analysis- Includes application chapters that show how the methods can translate into medical practice
Table of Contents
Part One: Basics of Machine Learning and Magnetic Resonance Imaging1. The statistics behind Machine Learning2. The Ingredients for Machine Learning3. Introduction to the Physics behind MRPart Two: MR Image Acquisition4. Adjust to your imaging scenario: learning and optimizing MR sampling5. MR Imaging in the low field: Leveraging the power of machine learning6. The Smart spin: Machine learning for magnetic resonance spectroscopyPart Three: MR Image Reconstruction7. Get the Image: Machine Learning for MR image reconstruction8. Enhance the Image: Super resolution in MRI9. Freeze the motion: Machine Learning for motion correction10. Map the Image: Machine learning for quantitative MR Mapping11. Am (A)I hallucinating: Robustness of MR Image reconstructionPart Four: MR image Post-Processing12. Cut it here: Image Segmentation for MRI13. Quality Matters: Automated MR Image Quality control14. What is beyond the image? Machine Learning for MR Image Analysis15. Give me that other image: machine learning for image-to-image translationPart Five: Generalization and Fairness16. The cause and effect of an MR image: Robustness and generalizability17. Scale it up: Large-scale MR data processing18. Human in the loop: integration of experts to MR Data ProcessingPart Six: Clinical Application19. Clinical Applications of machine learning in brain, neck and spine MRI20. Clinical Applications of machine learning in cardiac MRI21. Clinical Applications of machine learning in body MRI22. Clinical Applications of machine learning in breast MRI23. Clinical Applications of Machine Learning in musculoskeletal MRIPart Seven: Reproducibility24. Let's share: Open-Source frameworks and public databases25. System under test: challenges for algorithm benchmarkingPart Eight: Conclusion26. Future Challenges and Directions
-
- 洋書電子書籍
- Control and Trackin…
-
- 和書
- イーグル・シューター
