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.

• 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
• Application chapters show how the methods can translate into medical practice