  The deep learning methods have achieved attractive performance in dynamic MR
cine imaging. However, all of these methods are only driven by the sparse prior
of MR images, while the important low-rank (LR) prior of dynamic MR cine images
is not explored, which limits the further improvements on dynamic MR
reconstruction. In this paper, a learned singular value thresholding
(Learned-SVT) operation is proposed to explore deep low-rank prior in dynamic
MR imaging for obtaining improved reconstruction results. In particular, we
come up with two novel and distinct schemes to introduce the learnable low-rank
prior into deep network architectures in an unrolling manner and a
plug-and-play manner respectively. In the unrolling manner, we put forward a
model-based unrolling sparse and low-rank network for dynamic MR imaging,
dubbed SLR-Net. The SLR-Net is defined over a deep network flow graph, which is
unrolled from the iterative procedures in the Iterative Shrinkage-Thresholding
Algorithm (ISTA) for optimizing a sparse and low-rank based dynamic MRI model.
In the plug-and-play manner, we present a plug-and-play LR network module that
can be easily embedded into any other dynamic MR neural networks without
changing the network paradigm. Experimental results show that both schemes can
further improve the state-of-the-art CS methods, such as k-t SLR, and
sparsity-driven deep learning-based methods, such as DC-CNN and CRNN, both
qualitatively and quantitatively.
