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

Andrei Mancu, Wenqi Huang, Gastao Lima da Cruz, Daniel Rueckert, Kerstin Hammernik

Keywords: Inverse Problems, MRI Reconstruction, Deep learning, Low-rank, Cardiac MRI, Radial sampling

TL;DR: The paper presents a physics-guided self-supervised deep neural network technique for radial magnetic resonance imaging that leverages the intrinsic low-rank and sparse prior of moving organ’s images, improving cardiac image 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.

Submission Number: 6