Task Transformer Network for Joint MRI Reconstruction and Super-Resolution (MICCAI 2021)

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Deep Learning T2Net
Overview

T2Net

Task Transformer Network for Joint MRI Reconstruction and Super-Resolution (MICCAI 2021)

[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}
}
Comments
  • cannot import name 'NumpyMetric' from 'pytorch_lightning.metrics.metric'

    cannot import name 'NumpyMetric' from 'pytorch_lightning.metrics.metric'

    I am running this on google collab, in the TPU runtime. I have already installed all the dependecies.

    Running python ixi_train_t2net.py throws an exception, the stack trace of which is as follows:

    WARNING:root:TPU has started up successfully with version pytorch-1.7
Traceback (most recent call last):
  File "ixi_train_t2net.py", line 17, in <module>
    from ixi_module_t2net import UnetModule  # experimental.unet.unet_module
  File "/content/gdrive/My Drive/T2Net/ixi_module_t2net.py", line 14, in <module>
    from fastmri.mri_ixi_module_t2net import MriModule
  File "/content/gdrive/My Drive/T2Net/fastmri/mri_ixi_module_t2net.py", line 19, in <module>
    from fastmri import evaluate
  File "/content/gdrive/My Drive/T2Net/fastmri/evaluate.py", line 14, in <module>
    from pytorch_lightning.metrics.metric import NumpyMetric, TensorMetric
ImportError: cannot import name 'NumpyMetric' from 'pytorch_lightning.metrics.metric' (/usr/local/lib/python3.7/dist-packages/pytorch_lightning/metrics/metric.py)

    Am I missing something?

    opened by AmitSharma1127 7
  • 关于UNET

    关于UNET

    作者您好，冒昧打扰了! 我是一名正在进行MRI reconstruction项目的学生，想请问一下，您这里是否有在IXI数据集上训练好的UNET代码呢，因为看到您有拿这个作为对比方法，但是在网络上没有找到比较合适的完全使用UNET用于MRI reconstruction的代码，所以请求您的帮助，希望能够提供一份来源！谢谢您！

    opened by WuJiaMian 0
  • How to generate the file '1D-Cartesian_6X_128128.mat'?

    How to generate the file '1D-Cartesian_6X_128128.mat'?

    Thanks for sharing this fantastic work. I wonder how you could generate the file '1D-Cartesian_6X_128128.mat'. I'd really appreciate it if you could share the code of generating it.

    opened by ElliotQi 2
  • incompatibility in code and article

    incompatibility in code and article

    In your article after concatenating Q and output of the transfer attention layer, we see two convolution layers as illustrated in Fig 2 ,however, In code you applied one Conv why do not they match? T=self.conv1[i](T)

    opened by maralzar 4
  • normalization

    normalization

    作者你好，我注意到实验数据采用mean —stddev归一化的方法，该归一化方法会产生负值。图片数据在经历傅立叶和逆傅里叶以及绝对值的操作(np.abs(np.ifft.fft2(np.fft2.fft(image))))，会存在负值消失的问题，这对最后重建有负面影响吗？这与归一化到0～1的操作是否存在较大的区别？

    opened by Hao-Xin-Shen 0
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