Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-Enhanced Loss Constraint

Abstract

Dynamic magnetic resonance (MR) imaging has generated great research interest, because it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging speed or long scanning time is still a challenge 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 some smoothing needed in the reconstructed images at high acceleration. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhanced loss constraint, dubbed cascaded residual dense networks (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 use the higher-degree total variation loss function, which has the edge enhancement properties, for training the networks.