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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.