Active MR k-space Sampling with Reinforcement Learning

November 04, 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

Adriana Romero Soriano

Michal Drozdzal

Roberto Calandra

Sumana Basu

Publisher

MICCAI

Research Topics

Reinforcement Learning

Computer Vision

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