REINFORCEMENT LEARNING

COMPUTER VISION

Active MR k-space Sampling with Reinforcement Learning

October 10, 2020

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.

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AUTHORS

Written by

Luis Pineda

Sumana Basu

Adriana Romero

Roberto Calandra

Michal Drozdzal

Publisher

International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI 2020

Research Topics

Computer Vision

Reinforcement Learning

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