Self-Supervised Low-rank plus Sparse Network for Radial MRI Reconstruction

Motivation: Physics-guided self-supervised approaches have proven to be useful in MR image reconstruction from limited Cartesian measurements. However, the potential of radially-sampled k-space data remains largely unexplored. Goal(s): In this context, we introduce a self-supervised learning approach to reconstruct dynamic images from sparsely-sampled radial cardiac data. Approach: The proposed model integrates a novel low-rank and sparse regularizer in its iterative framework to better exploit the characteristics of dynamic images. Results: Our method is compared to iterative reconstruction techniques and other deep neural network approaches in supervised and self-supervised tasks, where the proposed model achieves the best performance for a single and four heartbeat reconstruction. Impact: Self-supervised models for radially sampled cardiac measurements can now be efficiently trained on limited amounts of data to reliably reconstruct high-contrast and low artifact dynamic MR images, even at high acceleration rates for faster acquisition speed.