Machine Learning in MRI

From Methods to Clinical Translation Volume 13
Herausgeber: Kuestner, Ing Thomas; Payabavsh, Sam; F Baumgartner, Christian; Huang, Hao

• Broschiertes Buch

Produktbeschreibung

Machine Learning in MRI: From Methods to Clinical Translation 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, important contributions. Ideas and concepts of how processing could be harmonized and used to provide deployable frameworks that integrate into the clinical workflows are considered. Pitfalls and current limitations are discussed in the context of how they could be overcome to cater for clinical needs. Machine Learning in MRI: From Methods to Clinical Translation is 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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 375
• Erscheinungstermin: 1. September 2025
• Englisch
• ISBN-13: 9780443141096
• ISBN-10: 0443141096
• Artikelnr.: 70250843

Inhaltsangabe

1. Basics of machine learningTypes of learning: Supervised
self-supervised
semi-supervised
active learning
reinforcement learning 2. MR image acquisitionActive scanning
sequence parameter optimization 3. MR image reconstructionDL reconstruction 4. MR motion correctionPairwise image registration 5. MR image post-processingImage segmentation 6. Generalization and fairnessAI fairness and bias
domain adaptation 7. Publicly available codes
databases and challenges 8. Clinical translation/application(outcome
treatment prediction
patient monitoring
image quality