HAI Seminar: Beyond Image Interpretation in Radiology: Data-Efficient AI for Accelerating MRI Acquisition,

HAI Seminar

Title: Beyond Image Interpretation in Radiology: Data-Efficient AI for Accelerating MRI Acquisition
Speaker: Akshay Chaudhari
Date: November 3
RSVP Here: https://stanford.zoom.us/webinar/register/WN_PljGDyMLRTGwoeqvm8C_rw

Abstract:
Recent applications of artificial intelligence (AI) in radiology have focused on image interpretation tasks such as image classification, segmentation, or detection. However, a fundamental challenge in radiology is to acquire these medical images in a safe and efficient manner. New AI techniques have been proposed to solve the inverse problem of image reconstruction wherein only a limited set of measurements are used to reconstruct medical images with high diagnostic quality. Specifically, in this talk, I will describe how physics-guided AI is currently being used to improve the speed of magnetic resonance imaging (MRI). I will further describe how we may eschew requiring large extents of paired datasets required for supervised model training by using novel unsupervised and semi-supervised approaches for accelerated MRI. Beyond data efficiency, these approaches can help mitigate the challenge of distribution shifts for trained models. I will conclude by describing a 1.5TB dataset that we have made publicly available to help evaluate MRI reconstructions with clinically-relevant metrics.

Bio:
Dr. Chaudhari is an Assistant Professor of research in the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) section in the Department of Radiology. His primary research interests lie at the intersection of artificial intelligence and medical imaging. Dr. Chaudhari graduated from UCSD with a B.S. in Bioengineering in 2012. He completed his Ph.D. from Stanford University’s Department of Bioengineering in 2017, focusing on novel MRI methods for musculoskeletal imaging; supported through the National Science Foundation Graduate Research Fellowship, the Whitaker Fellowship, and the Siebel Fellowship. Dr. Chaudhari trained as a postdoctoral fellow in Radiology at Stanford University, where he combined machine learning with medical imaging acquisition and analysis. Dr. Chaudhari has won many awards, including the W.S. Moore Young Investigator Award, the Junior Fellow Award, and an Outstanding Teacher Award from the ISMRM. He has 6 additional young investigator awards for his work on advanced medical imaging acquisition and analysis techniques. Dr. Chaudhari is the Associate Director of Research and Education at the Stanford AIMI Center and is an internal advisory board member of the Precision Health and Integrated Diagnostics Center.

Date: 
Wednesday, November 3, 2021 - 10:00am to 11:00am