  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.
