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HAI Weekly Seminar with Akshay Chaudhari

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

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. 

Akshay Chaudhari

Akshay Chaudhari

Assistant Professor of Radiology (Integrative Biomedical Imaging Informatics) and, by courtesy, of Biomedical Data Science, Stanford University

 

Dr. Chaudhari is an Assistant Professor in the Integrative Biomedical Imaging Informatics at Stanford (IBIIS) section in the Department of Radiology and is the Associate Director of Research and Education at the Stanford AIMI Center. He is interested in the application of artificial intelligence tec ...