zoonono / K-Space-Transformer

K-Space Transformer for Undersampled MRI Reconstruction

This repository contains the PyTorch implementation of K-Space Transformer: https://arxiv.org/abs/2206.06947v2.

Citation

If you use this code for your research or project, please cite:

@inproceedings{zhao2022kspacetransformer,
  title={K-Space Transformer for Undersampled MRI Reconstruction},
  author={Ziheng Zhao, Tianjiao Zhang, Weidi Xie, Yanfeng Wang and Ya Zhang},
  booktitle={British Machine Vision Conference (BMVC)},
  year={2022}
}

Environment and Dependencies

Requirements:

• Python 3.7
• Pytorch 1.10.0
• torchvision 0.11.1
• numpy 1.21.6
• scikit-image 0.18.1
• matplotlib 3.4.2
• tqdm

To Run Our Code

• OASIS brain MRI dataset : https://www.oasis-brains.org

• fastMRI knee MRI dataset : https://fastmri.med.nyu.edu

• Our Pre-trained Model : https://drive.google.com/drive/folders/1YvkykYh5yoxLd_nuNgKfUXly_BCX-5RO?usp=sharing

• Train the model

python main.py --output_dir 'Log_Path' \
--train_hr_data_path 'xxx/xxx.npy' \
--train_lr_data_path 'xxx/xxx.npy' \
--train_mask_path 'xxx/xxx.npy' \
--valid_hr_data_path 'xxx/xxx.npy' \
--valid_lr_data_path 'xxx/xxx.npy' \
--valid_mask_path 'xxx/xxx.npy'

Here, hr and lr refers to the original resolution and downsampled groundtruth k-space MRI data.

• Test the model

python test.py --output_dir 'Test_Record_Path' \
--modelPath 'xxx/checkpoint.pth' \
--data_path 'xxx/xxx.npy' \
--mask_path 'xxx/xxx.npy'

Acknowledgement

To generate sampling masks, we use the code provided in https://github.com/rmsouza01/CD-Deep-Cascade-MR-Reconstruction.