Machine Learning in MRI: From Methods to Clinical Translation, Volume Thirteen in the
Advances 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.
Part One: Basics of Machine Learning and Magnetic Resonance Imaging
1. The statistics behind Machine Learning
2. The Ingredients for Machine Learning
3. Introduction to the Physics behind MR
Part Two: MR Image Acquisition
4. Adjust to your imaging scenario: learning and optimizing MR sampling
5. MR Imaging in the low field: Leveraging the power of machine learning
6. The Smart spin: Machine learning for magnetic resonance spectroscopy
Part Three: MR Image Reconstruction
7. Get the Image: Machine Learning for MR image reconstruction
8. Enhance the Image: Super resolution in MRI
9. Freeze the motion: Machine Learning for motion correction
10. Map the Image: Machine learning for quantitative MR Mapping
11. Am (A)I hallucinating: Robustness of MR Image reconstruction
Part Four: MR image Post-Processing
12. Cut it here: Image Segmentation for MRI
13. Quality Matters: Automated MR Image Quality control
14. What is beyond the image? Machine Learning for MR Image Analysis
15. Give me that other image: machine learning for image-to-image translation
Part Five: Generalization and Fairness
16. The cause and effect of an MR image: Robustness and generalizability
17. Scale it up: Large-scale MR data processing
18. Human in the loop: integration of experts to MR Data Processing
Part Six: Clinical Application
19. Clinical Applications of machine learning in brain, neck and spine MRI
20. Clinical Applications of machine learning in cardiac MRI
21. Clinical Applications of machine learning in body MRI
22. Clinical Applications of machine learning in breast MRI
23. Clinical Applications of Machine Learning in musculoskeletal MRI
Part Seven: Reproducibility
24. Let’s share: Open-Source frameworks and public databases
25. System under test: challenges for algorithm benchmarking
Part Eight: Conclusion
26. Future Challenges and Directions
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