  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.
