Learning MRI k-Space Subsampling Pattern Using Progressive Weight Pruning

Magnetic resonance (MR) imaging is widely used in clinical scenarios, while the long acquisition time is still one of its major limitations. An efficient way to accelerate the imaging process is to subsample the k-space, where MR signal is physically acquired, and then estimate the fully-sampled MR image from subsampled signal with a learned reconstruction model. In this work, we are inspired from the idea of neural network pruning and propose a novel strategy to learn the k-space subsampling pattern and the reconstruction model alternately in a data-driven fashion. More specifically, in each iteration of learning, we first greedily eliminate a few phases that are considered less important in the k-space according to their assigned weights, and then fine-tune the reconstruction model. In our pilot study, experiments demonstrated the robustness and superiority of our proposed method in both single- and multi-modal MRI settings.