Active MR k-space Sampling with Reinforcement Learning
This work addresses the challenge of accelerating MRI acquisition for medical imaging, representing an incremental advance by focusing on trajectory optimization rather than reconstruction models.
The paper tackles the problem of optimizing MRI acquisition trajectories using reinforcement learning, achieving significant performance improvements over state-of-the-art methods across various acceleration factors.
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.