Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning in MRI: From Methods to Clinical Translation Volume 13

Machine Learning in MRI: From Methods to Clinical Translation Volume 13

Kuestner, Thomas
Huang, Hao
F. Baumgartner, Christian

Paperback

Series: Advances in Magnetic Resonance Technology and Applications, Book 13

Technology & EngineeringBiology

ISBN10: 0443141096
ISBN13: 9780443141096
Publisher: Academic Press
Published: Dec 19 2025
Pages: 742
Weight: 3.35
Height: 1.51 Width: 7.49 Depth: 9.46
Language: English

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.

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