Papers › Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Hao Zheng, Faming Fang, Guixu Zhang
Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging. Inspired by recent deep learning methods, we propose a Cascaded Dilated Dense Network (CDDN) for MRI reconstruction. Dense blocks with residual connection are used to restore clear images step by step and dilated convolution is introduced for expanding receptive field without taking more network parameters. After each sub-network, we use a novel two-step Data Consistency (DC) operation in k-space. We convert the complex result from first DC operation to real-valued images and applied another sampled \emph{k}-space data replacement. Extensive experiments demonstrate that the proposed CDDN with two-step DC achieves state-of-art result.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections