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

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