Active MR k-space Sampling with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method for optimizing MRI acquisition trajectories, significantly improving image acquisition efficiency over existing methods on knee MRI datasets.

Contribution

It presents a novel reinforcement learning framework for trajectory optimization in MRI, addressing a gap in prior work that focused mainly on reconstruction models.

Findings

Outperforms state-of-the-art active MRI acquisition methods

Effective across various acceleration factors

Demonstrates significant improvements on knee MRI datasets

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.