Machine Learning in MRI
From Methods to Clinical Translation
- 1st Edition, Volume 13 - December 19, 2025
- Latest edition
- Editors: Thomas Kuestner, Hao Huang, Christian F Baumgartner, Sam Payabvash
- Language: English
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 mac… Read more
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Description
Description
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.
Key features
Key features
- 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
Readership
Readership
Magnetic resonance imaging researchers and radiologists with backgrounds either in MR physics, biomedical engineering, mathematics and radiology
Table of contents
Table of contents
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
Product details
Product details
- Edition: 1
- Latest edition
- Volume: 13
- Published: December 23, 2025
- Language: English
About the editors
About the editors
TK
Thomas Kuestner
Prof. Dr.-Ing. Thomas Küstner (Member, IEEE; Junior Fellow, ISMRM) is the chair of medical imaging and data analysis (MIDAS.lab) at the University Hospital of Tübingen, Germany. He received his PhD from the University of Stuttgart, Germany, in 2017. From 2018 to 2020 he was with the School of Biomedical Engineering and Imaging Sciences at King’s College London, United Kingdom. Since 2020 he co-leads the MIDAS.lab and in 2022 got appointed a professorship at the University Hospital of Tübingen, Germany about data engineering and advanced processing for medical imaging modalities. He is the spokesperson of the cross-section area for artificial intelligence-based infrastructure, data and methods in the clinic. His research group is working on artificial intelligence-enabled multi-parametric and multi-modality medical imaging methods in acquisition and reconstruction, and the automated analysis of clinical and epidemiological studies. He is particularly focused on MR-based motion imaging, correction and reconstruction, and the advents of artificial intelligence in MRI.
Affiliations and expertise
Chair, Medical Imaging and Data Analysis (MIDAS.lab), Diagnostic and Interventional Radiology, University Hospital of Tuebingen, GermanyHH
Hao Huang
Dr. Hao Huang is a Professor of Radiology in the Perelman School of Medicine at the University of Pennsylvania and Faculty Director of Small Animal Imaging Facility at Children’s Hospital of Philadelphia. He obtained his PhD in Biomedical Engineering from Johns Hopkins University School of Medicine in 2005. By pushing technical boundaries in advanced neural MRI acquisition and analysis, his works provide new knowledge on understanding circuits and functions of brain in health and disease. He has published more than 150 peer-reviewed articles and is one of the top scientists in neuroimaging and neurobiological sciences with cutting-edge techniques in diffusion, perfusion and functional MRI as well as artificial intelligence algorithms. He is on the Editorial Board of NeuroImage. He has served in a number of leadership positions in international committees. He has been recognized as the Distinguished Investigator of the Academy for Radiology and Biomedical Imaging Research in 2019. He has been elected as the Fellow of American Institute of Medical and Biological Engineering (AIMBE) in 2021. He has been elected as the Fellow of International Society of Magnetic Resonance in Medicine (ISMRM) in 2022.
Affiliations and expertise
Professor, Radiology in the Perelman School of Medicine, University of Pennsylvania, Pennsylvania, USA.
Faculty Director, Small Animal Imaging Facility, Children’s Hospital of Philadelphia, Pennsylvania, USA.CF
Christian F Baumgartner
Dr. Christian Baumgartner is currently heading the Machine Learning for Medical Image Analysis Group which is part of the Cluster of Excellence: Machine Learning - New Perspectives for Science, at the University of Tübingen. Before joining the University of Tübingen, Christian was working in a senior research engineering role at PTC Vuforia, where he focused on research and development of machine learning technology for augmented reality applications. Prior to this, he was a Post-doc at the Biomedical Image Computing Group at ETH Zürich, and before in the Biomedical Image Analysis Lab at Imperial College London. Christian completed his PhD in 2016 under the joint supervision of Prof. Andy King and Prof. Daniel Rueckert at King’s College London in the School of Biomedical Engineering & Imaging Sciences. He obtained his Master’s degree in Biomedical Engineering and my Bachelor’s degree in Information Technology and Electrical Engineering from ETH Zürich.
Affiliations and expertise
Head, Machine Learning for Medical Image Analysis Group, University of Tübingen, GermanySP
Sam Payabvash
Dr. Sam Payabvash, MD is an assistant professor of radiology at Yale University. He joined Yale in 2018 after completing fellowship and working as clinical instructor at UCSF. As a neuroimaging clinician scientist and neuroradiologist Dr. Payabvash and his lab apply advanced neuroimaging techniques and analysis to drive innovation and improve the lives of patients. His research is focused on the translation of novel neuroimaging modalities, quantitative analysis, and machine intelligence to clinical practice for informed treatment planning, personalized patient care, and clinical trial design. Through multidisciplinary collaboration with clinicians, scientists, and patient advocates, his team aims to translate emerging technologies into day-to-day clinical practice with focus on brain, head, and neck tumors.
Affiliations and expertise
Assistant professor of radiology, Yale University, New Haven, Connecticut, USAView book on ScienceDirect
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