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.
@article{arxiv.2007.10469,
title = {Active MR k-space Sampling with Reinforcement Learning},
author = {Luis Pineda and Sumana Basu and Adriana Romero and Roberto Calandra and Michal Drozdzal},
journal= {arXiv preprint arXiv:2007.10469},
year = {2020}
}
Comments
Presented at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020