Abstract #3602

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

Xiangshun Liu^1,2, 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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