chunmeifeng / T2Net

【MICCAI 2021】Task Transformer Network for Joint MRI Reconstruction and Super-Resolution

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T2Net

[Paper][Code]

Dependencies

  • numpy==1.18.5
  • scikit_image==0.16.2
  • torchvision==0.8.1
  • torch==1.7.0
  • runstats==1.8.0
  • pytorch_lightning==0.8.1
  • h5py==2.10.0
  • PyYAML==5.4

Data Prepare

  1. Download and decompress data from the link https://pan.baidu.com/s/1OdIoBwJy3GZB979JPBJS6w Password: qrlt

  2. Transform .h5 format to .mat format "python convertH5tomat.py --data_dir XXX/T2Net/h5"

  3. You can get the dir of as following:

  • h5
    • train
    • val
    • test
  • mat
    • train
    • val
    • test
  1. Set data_dir = 'XXX/T2Net/h5' at the line 4 of ixi_config.yaml

[Training code --> T2Net]

git clone https://github.com/chunmeifeng/T2Net.git

Train

single gpu train

python ixi_train_t2net.py

multi gpu train you can change the 65th line in ixi_tain_t2net.py , set num_gpus = gpu number, then run

python ixi_train_t2net.py

🔥 NEWS 🔥

  • We have upload the mask file.
  • Before our project, you need to transform the .nii file to .mat file at first.
  • We have provided the code of converting the .nii file to .mat file as well as the .mat data.

Citation

@inproceedings{feng2021T2Net,
  title={Task Transformer Network for Joint MRI Reconstruction and Super-Resolution},
  author={Feng, Chun-Mei and Yan, Yunlu and Fu, Huazhu and Chen, Li and Xu, Yong},
  booktitle={International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)},
  year={2021}
}

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【MICCAI 2021】Task Transformer Network for Joint MRI Reconstruction and Super-Resolution


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