Back to Search
Start Over
PS-Net: Learned Partially Separable Model for Dynamic MR Imaging
- Publication Year :
- 2022
-
Abstract
- 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.<br />Comment: journal
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2205.04073
- Document Type :
- Working Paper