Self-Supervised Low-Rank Plus Sparse Network for Radial MRI Reconstruction

Abstract

In this work, we introduce a physics-guided self-supervised learning approach to reconstruct dynamic magnetic resonance images (MRI) from sparsely sampled radial data. The architecture incorporates a variable splitting scheme via a quadratic penalty approach consisting of iterative data consistency and denoiser step. To accommodate cardiac motion, the denoiser implements a learnable low-rank and sparse component instead of a conventional convolutional neural network. We compare the proposed model to iterative regularized MRI reconstruction techniques and to other deep neural network approaches adapted to radial data, both in supervised and self-supervised tasks. Our proposed method surpasses the performance of other techniques for a single heartbeat and four heartbeat MRI reconstruction. Furthermore, our approach outperforms other deep neural network reconstruction approaches in both supervision and self-supervision tasks.