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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