Abstract #2947

Image distortion correction for diffusion MRI using U-Net and Transformer

Tsuyoshi Ueyama^1,2,3, Erika Takahashi¹, Naoto Fujita¹, Yuichi Suzuki², Hideyuki Iwanaga², Osamu Abe⁴, and Yasuhiko Terada¹

¹Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan, ²Radiology center, The University of Tokyo Hospital, Tokyo, Japan, ³School of Medicine, Stanford University, Palo Alto, CA, United States, ⁴Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan

Synopsis

Keywords: Data Processing, Machine Learning/Artificial Intelligence, Diffusion weighted image/Diffusion tensor imageAlthough several end-to-end deep neural networks have been proposed to correct image distortion directly from distorted images, no study has verified the distortion correction performance for high b-values diffusion-weighted image (DWI) and diffusion tensor image (DTI) parameters. For example, the U-Net-based Synb0-DisCo was only validated for distortion correction of b0 images. Here, we used two networks, U-Net and Trans-DisCo, to verify distortion correction performance for DWIs and DTI parameter images. Trans-DisCo is our proposed model that replaces the convolutional neural network in U-Net with Swin Transformer, and we have shown that it outperforms U-Net.

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.