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

Although 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.