Beyond the Patterns 27 - Luis Pineda - Active MR k-space Sampling with Reinforcement Learning

Our invited speaker in this video is Luis Pineda from Facebook AI Research! Abstract: Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this paper, we focus on learning acquisition trajectories given a fixed image reconstruction model. We formulate the problem as a sequential decision process and propose the use of reinforcement learning to solve it. Experiments on a large scale public MRI dataset of knees show that our proposed models significantly outperform the state-of-the-art in active MRI acquisition, over a large range of acceleration factors. Bio: Luis Pineda is a researcher at Facebook AI Research in Montreal. He obtained his PhD from University of Massachusetts Amherst in 2018, advised by Prof. Shlomo Zilberstein; during his PhD, he focused on deve