Deep unrolling Shrinkage Network for Dynamic MR imaging
Yinghao Zhang Xiaodi Li Weihang Li Yue Hu
Deep unrolling networks that utilize sparsity priors have achieved great
success in dynamic magnetic resonance (MR) imaging. The convolutional neural
network (CNN) is usually utilized to extract the transformed domain, and then
the soft thresholding (ST) operator is applied to the CNN-transformed data to
enforce the sparsity priors. However, the ST operator is usually constrained to
be the same across all channels of the CNN-transformed data. In this paper, we
propose a novel operator, called soft thresholding with channel attention
(AST), that learns the threshold for each channel. In particular, we put
forward a novel deep unrolling shrinkage network (DUS-Net) by unrolling the
alternating direction method of multipliers (ADMM) for optimizing the
transformed $l_1$ norm dynamic MR reconstruction model. Experimental results on
an open-access dynamic cine MR dataset demonstrate that the proposed DUS-Net
outperforms the state-of-the-art methods. The source code is available at
\url{https://github.com/yhao-z/DUS-Net}.