Generalizable MRI Motion Correction via Compressed Sensing Equivariant Imaging Prior

Existing deep learning (DL)-based magnetic resonance imaging (MRI) retrospective motion correction (MoCo) models are typically task-specific, which makes them challenging to generalize to different scenarios w.r.t motions, modalities, planes, and scanner centers. This limitation occurs since the motions of each patient vary, and collecting diverse paired/unpaired motion data is generally costly and infeasible. To deal with this problem, we propose the Equivariant Imaging Prior (EIP) framework to generalize the MoCo tasks toward various scenarios.In this paper, the traditional MRI MoCo tasks, specifically for the multi-scenarios, can be treated as a mask-varying compressed sensing self-supervised problem for MRI reconstruction with corrupted k-space data.To the best of our knowledge, this framework is the first attempt to handle multiple MRI MoCo scenarios with one single DL model. Specifically, stochastic subsampling and modality augmentation are employed for data preparation. Then, a domain generalization-friendly net is carefully designed and an equivariant imaging task is leveraged to learn the mapping from corrupted data to clean images. The experimental results show that the proposed EIP framework achieves impressive adaptability across generalizable MoCo tasks, including but not limited to multi-motion, multi-modality, multi-center, and multi-plane. Furthermore, our EIP demonstrates similar or superior performance to several state-of-the-art models trained in a supervised manner, extending to even motion estimation on the multi-coil raw data. The code is available: https://github.com/wangzhiwen-scu/EIP4MoCo.