Muzi Guo1,2, Yuanyuan Liu1, Yuxin Yang1, Dong Liang1, Hairong Zheng1, and Yanjie Zhu1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2University of Chinese Academy of Sciences, Bejing, China
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2University of Chinese Academy of Sciences, Bejing, China
A self-supervised convolution neural network for quantitative magnetic resonance (MR) images super-resolution is proposed, which could recover the high-resolution weighted-MR images and estimated map efficiently and accurately.
Fig. 1. The
framework of the proposed algorithm.
Fig. 2. Illustration
of SR results for simulated data with different methods. The
second row shows the zoom-up views of selected regions in the first row.
