Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. However, most of these methods represent low-rank prior by hand-crafted nuclear norm, which cannot accurately approximate the low-rank prior over the entire dataset through a fixed regularization parameter. In this paper, we propose a learned low-rank method for dynamic MR imaging. In particular, we unrolled the semi-quadratic splitting method (HQS) algorithm for the partially separable (PS) model to a network, in which the low-rank is adaptively characterized by a learnable null-space transform. Experiments on the cardiac cine dataset show that the proposed model outperforms the state-of-the-art compressed sensing (CS) methods and existing deep learning methods both quantitatively and qualitatively.
@article{arxiv.2205.04073,
title = {PS-Net: Learned Partially Separable Model for Dynamic MR Imaging},
author = {Chentao Cao and Zhuo-Xu Cui and Qingyong Zhu and Congcong Liu and Dong Liang and Yanjie Zhu},
journal= {arXiv preprint arXiv:2205.04073},
year = {2022}
}