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Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Hao Zheng · Faming Fang · Guixu Zhang

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #132

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.

Author Information

Hao Zheng (East China Normal University)
Faming Fang (East China Normal University)
Guixu Zhang (East China Normal University)