DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior

Bo Zhou, S. Kevin Zhou

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020

[Paper]

This repository contains the PyTorch implementation of DuDoRNet.

Citation

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

@inproceedings{zhou2020dudornet,
  title={DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior},
  author={Zhou, Bo and Zhou, S Kevin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4273--4282},
  year={2020}
}

Environment and Dependencies

Requirements:

• Python 3.7
• Pytorch 0.4.1
• scipy
• scikit-image
• opencv-python
• tqdm

Our code has been tested with Python 3.7, Pytorch 0.4.1, CUDA 10.0 on Ubuntu 18.04.

Dataset Setup

.
Data
├── TRAIN                   # contain training files
│   ├── T1
│   │   ├── kspace
│   │   │   ├── train_1.mat         
│   │   │   ├── train_2.mat 
│   │   │   ├── ...         
│   │   │   └── train_N.mat 
│   │   └── ...
│   │   
│   ├── T2
│   │   ├── kspace
│   │   │   ├── train_1.mat          
│   │   │   ├── train_2.mat 
│   │   │   ├── ...         
│   │   │   └── train_N.mat 
│   │   └── ...
│   │   
│   ├── FLAIR
│   │   ├── kspace
│   │   │   ├── train_1.mat          
│   │   │   ├── train_2.mat 
│   │   │   ├── ...         
│   │   │   └── train_N.mat 
│   │   └── ...
│   └── ...
│
├── VALI                    # contain validation files
│   ├── T1
│   │   ├── kspace
│   │   │   ├── vali_1.mat          
│   │   │   ├── vali_2.mat 
│   │   │   ├── ...         
│   │   │   └── vali_M.mat 
│   │   └── ...
│   │   
│   ├── T2
│   │   ├── kspace
│   │   │   ├── vali_1.mat          
│   │   │   ├── vali_2.mat 
│   │   │   ├── ...         
│   │   │   └── vali_M.mat 
│   │   └── ...
│   │   
│   ├── FLAIR
│   │   ├── kspace
│   │   │   ├── vali_1.mat          
│   │   │   ├── vali_2.mat 
│   │   │   ├── ...         
│   │   │   └── vali_M.mat 
│   │   └── ...
│   └── ...
│
├── TEST                    # contain test files
│   ├── T1
│   │   ├── kspace
│   │   │   ├── test_1.mat          
│   │   │   ├── test_2.mat 
│   │   │   ├── ...         
│   │   │   └── test_K.mat 
│   │   └── ...
│   │   
│   ├── T2
│   │   ├── kspace
│   │   │   ├── test_1.mat          
│   │   │   ├── test_2.mat 
│   │   │   ├── ...         
│   │   │   └── test_K.mat 
│   │   └── ...
│   │   
│   ├── FLAIR
│   │   ├── kspace
│   │   │   ├── test_1.mat          
│   │   │   ├── test_2.mat 
│   │   │   ├── ...         
│   │   │   └── test_K.mat 
│   │   └── ...
│   └── ...
│            
└── ...

Each .mat should contain a W x W complex value matrix with kspace data in it, where W x W is the kspace size. Please note the variable name should be set as 'kspace_py'. Then, please add the data directory './Data/' after --data_root in the code or scripts.

To Run Our Code

• Train the model

python train.py --experiment_name 'train_DuDoRN_R4_pT1' --data_root './Data/' --dataset 'Cartesian' --netG 'DRDN' --n_recurrent 4 --use_prior --protocol_ref 'T1' --protocol_tag 'T2'

where
--experiment_name provides the experiment name for the current run, and save all the corresponding results under the experiment_name's folder.
--data_root provides the data folder directory (with structure illustrated above).
--n_recurrent defines number of recurrent blocks in the DuDoRNet.
--protocol_tag defines target modality to be reconstruct, e.g. T2 or FLAIR.
--protocol_ref defines modality to be used as prior, e.g. T1.
--use_prior defines whether to use prior as indicated by protocol_ref.
Other hyperparameters can be adjusted in the code as well.

• Test the model

python test.py --experiment_name 'test_DuDoRN_R4_pT1' --accelerations 5 --resume './outputs/train_DuDoRN_R4_pT1/checkpoints/model_259.pt' --data_root './Data/' --dataset 'Cartesian' --netG 'DRDN' --n_recurrent 4 --use_prior --protocol_ref 'T1' --protocol_tag 'T2'

where
--accelerations defines the acceleration factor, e.g. 5 for 5 fold accelerations.
--resume defines which checkpoint for testing and evaluation.
The test will output an eval.mat containing model's input, reconstruction prediction, and ground-truth for evaluation.

Sample training/test scripts are provided under './scripts/' and can be directly executed.

Contact

If you have any question, please file an issue or contact the author:

Bo Zhou: bo.zhou@yale.edu