Submitted by webadmin on Mon, 10/18/2021 - 12:14
Toward Real-time Magnetic Resonance Imaging Using Machine Learning
Abstract

Magnetic Resonance Imaging (MRI) is a clinically used medical imaging modality to reveal the structure, metabolism, and function of internal organs of humans or any biological objects.  One of a major challenge in MRI is it long acquisition time, which limits dynamic and quantitative imaging of organs that change over time. To address the issue of acquisition speed, we have developed mathematical models and computational algorithms that allow the MR images to be reconstructed from data acquired far below the Nyquist rate.  This talk will start with compressed sensing, a strategy for signal recovery from sub-Nyquist sampled data. I will show how the machine-learning-based strategies can be incorporated into the underlying optimization framework. In particular, deep learning-based reconstruction approaches will be elaborated, including those unrolled from an iterative optimization to a deep neural network and those integrating both standard convolutional neural networks with the imaging physics. Results for some clinical applications such as cardiac imaging, diffusion imaging, and parameter mapping will be presented. The talk will conclude with future outlooks.
 

Speaker: Professor Leslie YING 
Date: 27 October 2021 (Wednesday)
Time: 9:00am – 10:00am
PosterClick here

Biography

Prof Leslie Ying is the Clifford C. Furnas Professor of Biomedical Engineering and Electrical Engineering at the University at Buffalo, the State University of New York (SUNY). Prof Ying received her B.E. in Electronics Engineering from Tsinghua University, China in 1997, and both her M.S. and Ph.D. in Electrical Engineering from the University of Illinois at Urbana - Champaign in 1999 and 2003, respectively. She was an Assistant and then an Associate Professor of Electrical Engineering and Computer Science at the University of Wisconsin from 2003 to 2011. She joined the University at Buffalo, SUNY in Spring 2012. Her research interests include magnetic resonance imaging, compressed sensing, image reconstruction, and machine learning. Prof Ying received a CAREER award from the National Science Foundation in 2009. She was elected as an Administrative Committee member of IEEE Engineering in Medicine and Biology Society in 2013 - 2015. She was an Associate Editor of IEEE Transactions on Biomedical Engineering, a Deputy Editor of Magnetic Resonance in Medicine, and an editorial board member of Scientific Reports. She has been the Editor-in-Chief of IEEE Transactions on Medical Imaging since 2019. She is a Fellow of AIMBE.