SwinMR

This is the official implementation of our proposed SwinMR:

Swin Transformer for Fast MRI

Please cite:

@article{huang2022swin,
      title={Swin Transformer for Fast MRI}, 
      author={Jiahao Huang and Yingying Fang and Yinzhe Wu and Huanjun Wu and Zhifan Gao and Yang Li and Javier Del Ser and Jun Xia and Guang Yang},
      year={2022},
      eprint={2201.03230},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Highlight

• A novel Swin transformer-based model for fast MRI reconstruction was proposed.
• A multi-channel loss with sensitivity maps was proposed for reserving more details.
• Comparison studies were performed to validate the robustness of our SwinMR.
• A pre-trained segmentation network was used to validate the reconstruction quality.

Requirements

matplotlib==3.3.4

opencv-python==4.5.3.56

Pillow==8.3.2

pytorch-fid==0.2.0

scikit-image==0.17.2

scipy==1.5.4

tensorboardX==2.4

timm==0.4.12

torch==1.9.0

torchvision==0.10.0

Training and Testing

Use different options (json files) to train different networks.

Calgary Campinas multi-channel dataset (CC)

To train SwinMR (PI) on CC:

python main_train_swinmr.py --opt ./options/train_swinmr_pi.json

To test SwinMR (PI) on CC:

python main_train_swinmr.py --opt ./options/train_swinmr_npi.json

To train SwinMR (nPI) on CC:

python main_test_swinmr.py --opt ./options/test/test_swinmr_pi.json

To test SwinMR (nPI) on CC:

python main_test_swinmr.py --opt ./options/test/test_swinmr_npi.json

Multi-modal Brain Tumour Segmentation Challenge 2017 (BraTS17)

To train SwinMR (nPI) on BraTS17:

python main_train_swinmr.py --opt ./options/train_swinmr_brats17.json

To test SwinMR (nPI) on BraTS17:

python main_test_swinmr_BraTS17.py --opt ./options/test/test_swinmr_brats17.json

This repository is based on:

SwinIR: Image Restoration Using Swin Transformer (code and paper);

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (code and paper).