Deep unrolling Shrinkage Network for Dynamic MR imaging
This work addresses dynamic MR reconstruction for medical imaging, offering an incremental improvement by enhancing sparsity priors with channel-specific thresholds.
The paper tackled dynamic MR imaging by proposing a deep unrolling shrinkage network (DUS-Net) that introduces a channel-attention soft thresholding operator, which outperformed state-of-the-art methods on an open-access dataset.
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}.