MR Fingerprinting is emerging as a clinically feasible and versatile tool for quantitative MR imaging. Magnetic Resonance Fingerprinting for Quantitative MRI focuses on all aspects of MR Fingerprinting techniques and applications. Presenting a detailed and clear description of the technology, implementation, clinical translation issues and applications, it emphasizes different perspectives of MR signals and their utilization, with the goal of inspiring readers to explore new MR Fingerprinting designs. The book starts each section with a discussion on rethinking the MR acquisition and the opportunities provided by, for example, different MR contrast mechanisms, signal modeling, post-processing and clinical applications.
Magnetic Resonance Fingerprinting for Quantitative MRI is a complete reference for magnetic resonance imaging researchers and radiologists.
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Table of Contents
Preface
1. Quantitative MRI and Contrast Mechanisms
2. Overview of Quantitative Imaging Techniques
3. Fundamentals of MR Fingerprinting
4. Sequence Optimization and Derivatives in MRF
5. Technical Considerations for MRF Implementation
6. Multi-Parametric MRF Designs
7. MRF at Ultra-High and Ultra-Low Magnetic Fields
8. Technical development towards efficiency, high-resolution, and automated MRF.
9. Preclinical MRF
10. Vendor-Neutral, Cloud Computing, Large-Scale Dissemination
11. Image Post-Processing and Analysis Techniques
12. The Roles of Artificial Intelligence in MRF
13. Standardization and Reproducibility
14. Addressing Motion Artifacts in MRF
15. Confounding Factors in Image Analysis
16. Opportunities and Challenges in Clinical Translation
17. MRF in Neuroimaging
18. MRF in Cardiac Imaging
19. MRF in Abdominal Imaging
20. MRF in Musculoskeletal Imaging
21. MRF in Pediatric Imaging
Authors
Dan Ma Assistant Professor, Department of Biomedical Engineering, School of Medicine, Case Western Reserve University, and Member, Cancer Imaging Program, Case Comprehensive Cancer Center.Dan Ma, PhD, is an Associate Professor of Neurosurgery and Biomedical Engineering at Duke University. She is the co-inventor of MR Fingerprinting and has led its technical development and clinical translation for over ten years. Her research focuses on quantitative MRI across acquisition, reconstruction, and analysis, with clinical applications in neurological diseases, pediatric imaging, breast cancer, and prostate cancer. She is a Senior Member of the National Academy of Inventors, a Junior Fellow of the International Society for Magnetic Resonance in Medicine (ISMRM) , and a recipient of the ISMRM I. I. Rabi Young Investigator Award.
Mark Griswold Professor of Radiology and Biomedical Engineering at Case Western Reserve University and a member of the Case Comprehensive Cancer Center..Dr. Mark A. Griswold is a world-renowned MRI scientist, a Professor of Radiology and Biomedical Engineering at Case Western Reserve University (CWRU) and a member of the Case Comprehensive Cancer Center. His research spans fast and quantitative MRI methods, with contributions to parallel imaging, MR fingerprinting, and MRI hardware innovation. At CWRU, he has served as Director of MRI Research and as Director of the Interactive Commons, fostering cross-disciplinary collaboration and new approaches to visualization and education. Recognized for impactful invention and translation, he was elected a Fellow of the National Academy of Inventors and a Fellow of AIMBE. He also served as President of the International Society for Magnetic Resonance in Medicine (ISMRM).
Claudia Prieto Reader, School of Biomedical Engineering and Imaging Sciences, UK.Claudia Prieto, PhD, is a Professor at the Faculty of Engineering at the Pontificia Universidad Cat�lica de Chile. She is an internationally recognized expert in cardiac magnetic resonance imaging (MRI), with more than 15 years of experience in MRI research. Her work focuses on developing and evaluating novel MRI techniques for improved assessment of cardiovascular disease, including acquisition, reconstruction, motion estimation and correction, quantitative MRI, and AI-based solutions for different stages of the imaging pipeline.
