All-In-One Medical Image Restoration via Task-Adaptive Routing (AMIR)

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

Network Architecture

Dataset

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

Visualization

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.

Citation

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}
}