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Dynamic magnetic resonance (MR) imaging has generated great researchinterest, as it can provide both spatial and temporal information for clinicaldiagnosis. However, slow imaging speed or long scanning time is still one ofthe challenges for dynamic MR imaging. Most existing methods reconstructDynamic MR images from incomplete k-space data under the guidance of compressedsensing (CS) or low rank theory, which suffer from long iterativereconstruction time. Recently, deep learning has shown great potential inaccelerating dynamic MR. Our previous work proposed a dynamic MR imaging methodwith both k-space and spatial prior knowledge integrated via multi-supervisednetwork training. Nevertheless, there was still a certain degree of smooth inthe reconstructed images at high acceleration factors. In this work, we proposecascaded residual dense networks for dynamic MR imaging with edge-enhance lossconstraint, dubbed as CRDN. Specifically, the cascaded residual dense networksfully exploit the hierarchical features from all the convolutional layers withboth local and global feature fusion. We further utilize the total variation(TV) loss function, which has the edge enhancement properties, for training thenetworks.

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