Super-resolution MR Vessel Wall Images Using deep learning
Wenjing Xu1, Sen Jia1, Qing Zhu2, Yikang Li3, Hongying Zhang4, Shuai Shen1, Fuliang Lin1, Ye Li1, Dong Liang1, Xin Liu1, Hairong Zheng1, and Na Zhang1
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Faculty of Information Technology, Beijing University of Technology, Beijing, China, 3Department of computing，Imperial College London, London, United Kingdom, 4Department of Radiology, Northern Jiangsu People's Hospital, Jiangsu, China
To develop a super-resolution method based on the 3D high-resolution MR vessel wall images for generating high-resolution images from low-resolution, a 3D complex-valued super resolution (CVSR) neural network was proposed, which maintained complex algebraic structure of the original acquired images. CVSR was trained on 20 pairs of data sets and tested on 5 pairs. Ground truth with 0.44 mm were compared with Fourier interpolation method, EDSR with two real-valued channels and CVSR. Evaluations were performed using structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and error map quality metrics. The CVSR achieved the best performance when compared with the other methods.
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