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.
F. Baumgartner, Christian: - "Dr. Christian Baumgartner is currently heading the Machine Learning for Medical Image Analysis Group which is part of the Cluster of Excellence: Machine Learning - New Perspectives for Science, at the University of Tübingen. Before joining the University of Tübingen, Christian was working in a senior research engineering role at PTC Vuforia, where he focused on research and development of machine learning technology for augmented reality applications. Prior to this, he was a Post-doc at the Biomedical Image Computing Group at ETH Zürich, and before in the Biomedical Image Analysis Lab at Imperial College London. Christian completed his PhD in 2016 under the joint supervision of Prof. Andy King and Prof. Daniel Rueckert at King's College London in the School of Biomedical Engineering & Imaging Sciences. He obtained his Master's degree in Biomedical Engineering and my Bachelor's degree in Information Technology and Electrical Engineering from ETH Zürich."
Huang, Hao: - Dr. Hao Huang is a Professor of Radiology in the Perelman School of Medicine at the University of Pennsylvania and Faculty Director of Small Animal Imaging Facility at Children's Hospital of Philadelphia. He obtained his PhD in Biomedical Engineering from Johns Hopkins University School of Medicine in 2005. By pushing technical boundaries in advanced neural MRI acquisition and analysis, his works provide new knowledge on understanding circuits and functions of brain in health and disease. He has published more than 150 peer-reviewed articles and is one of the top scientists in neuroimaging and neurobiological sciences with cutting-edge techniques in diffusion, perfusion and functional MRI as well as artificial intelligence algorithms. He is on the Editorial Board of NeuroImage. He has served in a number of leadership positions in international committees. He has been recognized as the Distinguished Investigator of the Academy for Radiology and Biomedical Imaging Research in 2019. He has been elected as the Fellow of American Institute of Medical and Biological Engineering (AIMBE) in 2021. He has been elected as the Fellow of International Society of Magnetic Resonance in Medicine (ISMRM) in 2022.
Kuestner, Thomas: - Prof. Dr.-Ing. Thomas Küstner (Member, IEEE; Junior Fellow, ISMRM) is the chair of medical imaging and data analysis (MIDAS.lab) at the University Hospital of Tübingen, Germany. He received his PhD from the University of Stuttgart, Germany, in 2017. From 2018 to 2020 he was with the School of Biomedical Engineering and Imaging Sciences at King's College London, United Kingdom. Since 2020 he co-leads the MIDAS.lab and in 2022 got appointed a professorship at the University Hospital of Tübingen, Germany about data engineering and advanced processing for medical imaging modalities. He is the spokesperson of the cross-section area for artificial intelligence-based infrastructure, data and methods in the clinic. His research group is working on artificial intelligence-enabled multi-parametric and multi-modality medical imaging methods in acquisition and reconstruction, and the automated analysis of clinical and epidemiological studies. He is particularly focused on MR-based motion imaging, correction and reconstruction, and the advents of artificial intelligence in MRI.
Payabvash, Sam: - Dr. Sam Payabvash, MD is an assistant professor of radiology at Yale University. He joined Yale in 2018 after completing fellowship and working as clinical instructor at UCSF. As a neuroimaging clinician scientist and neuroradiologist Dr. Payabvash and his lab apply advanced neuroimaging techniques and analysis to drive innovation and improve the lives of patients. His research is focused on the translation of novel neuroimaging modalities, quantitative analysis, and machine intelligence to clinical practice for informed treatment planning, personalized patient care, and clinical trial design. Through multidisciplinary collaboration with clinicians, scientists, and patient advocates, his team aims to translate emerging technologies into day-to-day clinical practice with focus on brain, head, and neck tumors.
