Abstract #4716

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

Synopsis

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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