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Unsupervised physics-informed deep learning (N=1) for solving inverse qMRI problems – Relaxometry and field mapping from multi-echo data

Author:Ilyes Benslimane  Thomas Jochmann  Robert Zivadinov  Ferdinand Schweser  

Session Type:Oral  

Session Date:Tuesday, 18 May 2021  

Topic:Machine Learning for Quantitative Imaging  

Session Name:Machine Learning for Quantitative Imaging  

Program Number:0330  

Room Session:Concurrent 1  

Institution:Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States  Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany  Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, USA