CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint

TL;DR

This paper introduces CRDN, a deep learning method using cascaded residual dense networks and edge-enhanced loss for faster, high-quality dynamic MR image reconstruction from incomplete data.

Contribution

It proposes a novel cascaded residual dense network architecture combined with TV loss to improve image quality and reconstruction speed in dynamic MR imaging.

Findings

Enhanced image sharpness with edge preservation.

Reduced reconstruction time compared to iterative methods.

Improved performance at high acceleration factors.

Abstract

Dynamic magnetic resonance (MR) imaging has generated great research interest, as it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging speed or long scanning time is still one of the challenges for dynamic MR imaging. Most existing methods reconstruct Dynamic MR images from incomplete k-space data under the guidance of compressed sensing (CS) or low rank theory, which suffer from long iterative reconstruction time. Recently, deep learning has shown great potential in accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training. Nevertheless, there was still a certain degree of smooth in the reconstructed images at high acceleration factors. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN. Specifically, the cascaded residual dense networks fully exploit the hierarchical features from all the convolutional layers with both local and global feature fusion. We further utilize the total variation (TV) loss function, which has the edge enhancement properties, for training the networks.