PyTorch implementation for All-In-One Medical Image Restoration via Task-Adaptive Routing (AMIR) (MICCAI 2024).

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

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 AMIR useful in your research, please consider citing:

@misc{yang2024allinone,
      title={All-In-One Medical Image Restoration via Task-Adaptive Routing}, 
      author={Zhiwen Yang and Haowei Chen and Ziniu Qian and Yang Yi and Hui Zhang and Dan Zhao and Bingzheng Wei and Yan Xu},
      year={2024},
      eprint={2405.19769},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}