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Abstract #1329

Super-resolution and distortion-corrected diffusion-weighted imaging using 2D super-resolution generative adversarial network

Pu-Yeh Wu1, Weiqiang Dou1, Hongyuan Ding2, Jiulou Zhang3, Yong Shen1, Guangnan Quan1, Zhangxuan Hu1, and Bing Wu1
1GE Healthcare, Beijing, China, 2Radiology Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 3Artificial Intelligence Imaging Laboratory, School of Medical Imaging, Nanjing Medical University, Nanjing, China

We proposed a deep learning-based method for super-resolution and distortion-corrected DWI reconstruction with a visual perception-sensitive super-resolution network SRGAN and multi-shot DWI as target. Our preliminary results demonstrated that the proposed model could produce satisfactory reconstruction of super-resolution diffusion images at b = 0 and 1000 s/mm2, and the geometric distortions in prefrontal cortex and temporal pole were well corrected. Furthermore, SRGAN reconstructed images provide comparable texture details to that of multi-shot DWI. With these findings, this developed model may be considered an effective tool for detecting subtle alterations of diffusion properties with only regular T2WI and DWI as inputs.

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