PyTorch implementation for 《TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration》

🚀🚀🚀Check our paper collection of recent Awesome-Medical-Image-Restoration

You can download the preprocessed datasets for MRI super-resolution, CT denoising, and PET synthesis from Baidu Netdisk or Google Drive.

The original dataset for MRI super-resolution and CT denoising are as follows:

• MRI super-resolution: IXI dataset

• CT denoising: AAPM dataset

You can use AMIDE to visualize the ".nii" file. Note that the color map for MRI and CT images is "black/white linear," while the color map for PET images is "white/black linear." Additionally, you need to rescale the PET image according to the voxel size specified in the paper.

If you find TAT useful in your research, please consider citing:

@inproceedings{yang2025tat,
  title={TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration},
  author={Yang, Zhiwen and Zhang, Jiaju and Yi, Yang and Liang, Jian and Wei, Bingzheng and Xu, Yan},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2025},
  organization={Springer}
}

The codebase is based on the awesome AMIR and Restore-RWKV repositories.