Assistant Professor of Radiology (Integrative Biomedical Imaging Informatics); Faculty Affiliate, Stanford Institute for Human-Centered Artificial Intelligence (HAI), Wu Tsai Neurosciences Institute
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, Akshay Chaudhari will describe how physics-guided AI is currently being used to improve the speed of magnetic resonance imaging (MRI). Chaudhari 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. Chaudhari will conclude by describing a 1.5TB dataset that we have made publicly available to help evaluate MRI reconstructions with clinically-relevant metrics.