CBR/EE: Talk by Prof. Mathews Jacob

Talk on Model based deep Learning for inverse problems in MRI: Beyond Algorithm Unrolling

by Prof. Mathews Jacob, University of Iowa, USA.

on July 30th, from 11.30 AM to 12.30 PM.

Venue: CBR Auditorium, CBR, IISc.

Host: Prof. Chandra Sekhar Seelamantula, IISc

Abstract: The reconstruction of MR images from highly undersampled Fourier measurements is a problem that has received a lot of attention in the past decade. Compressed sensing algorithms have been extensively employed in MRI to overcome the challenges associated with the slow nature of MRI acquisition. These methods offer guaranteed uniqueness, fast convergence, and stability properties. Model-based deep learning methods that combine imaging physics with learned regularization priors have emerged as more powerful alternatives for MR image recovery in recent years. The talk will introduce different flavors of physics-based deep learning methods and discuss the unique challenges associated with these schemes in high-dimensional settings. Novel memory efficient iterative algorithms that possess guarantees similar to compressive sensing, while offering improved performance will be introduced. Energy models that allow sampling from the posterior distribution will also be discussed. The talk will draw upon our recent work, available at https://cbig.iibi.uiowa.edu/publications

Biography of the speaker: Mathews Jacob will be starting as a Professor in the Department of Electrical and Computer Engineering at the University of Virginia, starting August 2024. He is currently a professor in the Department of Electrical and Computer Engineering and is heading the Computational Biomedical Imaging Group (CBIG) at the University of Iowa.  He obtained his B.Tech in Electronics and Communication Engineering from National Institute of Technology, Calicut, Kerala, and his M.E in signal processing from the Indian Institute of Science, Bangalore. He received his Ph.D. degree from the Biomedical Imaging Group at the Swiss Federal Institute of Technology. He was a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign.

Dr. Jacob is the recipient of the CAREER award from the National Science Foundation in 2009, the Research Scholar Award from American Cancer Society in 2011, and the Faculty Excellence Award for Research from University of Iowa in 2021. He is currently the associate editor of the IEEE Transactions on Medical Imaging and has served as the associate editor of IEEE Transactions on Computational Imaging from 2016-20. He was the senior author on two best paper awards (2015 & 2021) and one best machine learning paper award (2019) from IEEE ISBI. He was the general chair of IEEE International Symposium on Biomedical Imaging, 2020. He was elected as a Fellow of the IEEE (2022) for contributions to computational biomedical imaging.