Deep unrolling Shrinkage Network for Dynamic MR imaging

TL;DR

This paper introduces DUS-Net, a deep unrolling network with channel-specific soft thresholding for improved dynamic MR imaging reconstruction, outperforming existing methods.

Contribution

It proposes a novel deep unrolling shrinkage network with channel attention-based thresholding, unrolling ADMM for dynamic MR image reconstruction.

Findings

DUS-Net outperforms state-of-the-art methods on dynamic cine MR data.

The channel attention mechanism improves sparsity enforcement.

The method is validated on open-access datasets.

Abstract

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}.