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 developing heuristic search algorithms for probabilistic planning and their applications to robotics problems. At FAIR, his focus has been on studying deep reinforcement learning and its applications. His recent work includes exploring the use of deep RL for active MRI acquisition, and developing novel RL-based methods for multi-agent collaboration in Hanabi.
Pineda, Luis, Sumana Basu, Adriana Romero, Roberto Calandra, and Michal Drozdzal. "Active MR k-space sampling with reinforcement learning." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 23-33. Springer, Cham, 2020.
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