PS-Net: Learned Partially Separable Model for Dynamic MR Imaging
This work addresses dynamic MR imaging for medical diagnostics, offering incremental improvements over existing methods.
The authors tackled the problem of dynamic MR imaging by proposing a learned low-rank method that adaptively characterizes low-rank priors, outperforming state-of-the-art compressed sensing and deep learning methods on a cardiac cine dataset.
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.