Name: Machine Learning in MRI. From Methods to Clinical Translation
SKU: 64847172
Price: 2529.00 ZAR
Availability: InStock
Author: Christian F Baumgartner;Thomas , Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Germany) Kuestn;Sam, MD Payabvash;Hao Huang
ISBN: 9780443141096

portada Machine Learning in MRI. From Methods to Clinical Translation

Machine Learning in MRI. From Methods to Clinical Translation

Type

Physical Book

Publisher

Academic Press Inc

Author

Christian F Baumgartner;Thomas , Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Germany) Kuestn;Sam, MD Payabvash;Hao Huang

Collection

Advances in Magnetic Resonance Technology and Applications

Year

2026

Pages

375

Format

Paperback

Dimensions

23.5x19.1 cm

ISBN13

9780443141096

Machine Learning in MRI. From Methods to Clinical Translation

Christian F Baumgartner;Thomas , Diagnostic And Interventional Radiology, University Hospital Of Tuebingen, Germany) Kuestn;Sam, Md Payabvash;Hao Huang (Author) · Academic Press Inc · Paperback

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Synopsis "Machine Learning in MRI. From Methods to Clinical Translation"

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 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.