Machine Learning in MRI
Machine Learning in MRI
From Methods to Clinical Translation
Payabvash, Sam; F Baumgartner, Christian; Kuestner, Thomas; Huang, Hao
Elsevier Science Publishing Co Inc
12/2025
742
Mole
Inglês
9780443141096
15 a 20 dias
Descrição não disponível.
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
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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MRI; Artificial Intelligence; machine learning; MR data acquisition; MR image reconstruction; MR image post-processing; MR image analysis; MRI clinical translation
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.