Speaker: Jon Tamir, PhD | Assistant Professor, Chandra Family Department of Electrical and Computer Engineering, University of Texas – Austin
Date: Tuesday, January 28th, 2025
Time: 11:00 AM Central Time
Location: Zoom
Title: “Deep Generative Physical Modeling for MRI Reconstruction”
Abstract: Recently, deep learning techniques have been used as powerful data-driven reconstruction methods for inverse problems, and in particular have led to reduced scan times in magnetic resonance imaging (MRI). Typically, these methods are implemented using end-to-end supervised learning based on idealized imaging conditions. While promising, reconstruction quality is known to degrade when applied to natural measurement and anatomy perturbations. In this talk we present an alternative approach to deep learning reconstruction based on distribution learning, in which we train a deep generative model to learn image priors without reference to the measurement process. We show that decoupling the measurement and statistical models provides a powerful framework for MRI reconstruction. We leverage recent advances in diffusion probabilistic models to learn the prior distribution and we pose the image reconstruction task as posterior sampling. We show that this approach is competitive with end-to-end methods when applied to in-distribution data, and we demonstrate theoretical and empirical robustness to various out-of-distribution shifts. In cases where the distribution shift is large, we empirically show a small amount of training data is sufficient to recover the performance. We show how to train the models when data come from noisy or incomplete measurements, and we show how our framework can be used in the presence of motion or other unknown corruptions in measurement process.
Bio: Jon Tamir is an Assistant Professor in the Chandra Family Department of Electrical and Computer Engineering at UT Austin. He received his PhD in EECS from UC Berkeley. His research focus spans computational medical imaging, signal processing, and machine learning, with specific emphasis on magnetic resonance imaging. He is a Fellow of the Jack Kilby/Texas Instruments Endowed Faculty Fellowship in Computer Engineering and a recipient of the inaugural Oracle for Research Fellowship and the Google Research Scholar Award. He received the NSF CAREER Award in 2023.