Bo Zhou, S. Kevin Zhou
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
[Paper]
This repository contains the PyTorch implementation of DuDoRNet.
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}
}
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.
.
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.
- 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.
If you have any question, please file an issue or contact the author:
Bo Zhou: [email protected]
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