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Abstract #3602

 Multi-contrast MR imaging with enhanced denoising autoencoder prior network learning

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

This paper proposes an enhanced denoising autoencoder prior (EDAEP) network learning method for multi-contrast MR reconstruction using deep learning. Specifically, a multi-model autoencoder with various noise levels was developed to capture different features from multi-contrast images. A weighted aggregation strategy was also adopted to balance the impact of multiple model outputs. These designs empower the network to explore the correlations and similarities among multi-contrast images, handle different acceleration trajectories and avoid a lot of cumbersome retraining. Experimental results demonstrate that our method can improve the quality of reconstructed images compared to other classical methods.

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