Xiangshun Liu1,2, Minghui Zhang1, Qiegen Liu1, Leslie Ying3, Xin Liu2, Hairong Zheng2, and Shanshan Wang2
1Department of Electronic Information Engineering, Nanchang University, Nanchang, China, 2Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, China, 3Department of Biomedical Engineering and Electrical Engineering, The State University of New York, Buffalo, New York, NY, United States
1Department of Electronic Information Engineering, Nanchang University, Nanchang, China, 2Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, China, 3Department of Biomedical Engineering and Electrical Engineering, The State University of New York, Buffalo, New York, NY, United States
This paper proposes an enhanced denoising autoencoder prior (EDAEP) for multi-contrast MRI . We design a multi-noise model structure with weighted average strategy to capture different features from multi-contrast images, making the performance of proposed method more robust.
Fig. 1. The network architecture of denoising autoencoder. The arrow between input and output is residual connection.
Fig. 2. The flowchart of network training and multi-contrast MRI reconstruction. The green and red dashed boxes indicate two different noise level models.