CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network

Xiongchao Chen, Bo Zhou, Luyao Shi, Hui Liu, Yulei Pang, Rui Wang, Edward J Miller, Albert J Sinusas, Chi Liu

Journal of Nuclear Cardiology. 2021 Jun 3:1-6.

[Paper]

This repository contains the PyTorch implementation of DuRDN.

Citation

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

    @article{chen2021ct,
      title={CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network},
      author={Chen, Xiongchao and Zhou, Bo and Shi, Luyao and Liu, Hui and Pang, Yulei and Wang, Rui and Miller, Edward J and Sinusas, Albert J and Liu, Chi},
      journal={Journal of Nuclear Cardiology},
      pages={1--16},
      year={2021},
      publisher={Springer}
    }

Environment and Dependencies

Requirements:

• Python 3.6.10
• Pytorch 1.2.0
• numpy 1.19.2
• scipy
• scikit-image
• h5py
• tqdm

Our code has been tested with Python 3.6.10, Pytorch 1.2.0, CUDA: 10.0.130 on Ubuntu 18.04.6.

Dataset Setup

.
Data
├── train                # contain training files
|   ├── data1.h5
|       ├── AC.mat  
|       ├── NC.mat
|       ├── SC.mat
|       ├── SC2.mat
|       ├── SC3.mat
|       ├── GD.mat
|       ├── BMI.mat
|       ├── STATE.mat
|   └── ...  
|
├── valid                # contain validation files
|   ├── data1.h5
|       ├── AC.mat  
|       ├── NC.mat
|       ├── SC.mat
|       ├── SC2.mat
|       ├── SC3.mat
|       ├── GD.mat
|       ├── BMI.mat
|       ├── STATE.mat
|   └── ... 
|
└── test                 # contain testing files
|   ├── data1.h5
|       ├── AC.mat  
|       ├── NC.mat
|       ├── SC.mat
|       ├── SC2.mat
|       ├── SC3.mat
|       ├── GD.mat
|       ├── BMI.mat
|       ├── STATE.mat
|   └── ... 
└── ... 

Each .mat should contain a H x W x H float value matrix.
AC: attenuation-corrected image.
NC: non-attenuation-corrected image.
SC: the 1st Scatter-window image.
SC2: the 2nd Scatter-window image.
SC3: the 3rd Scatter-window image.
GD: gender encoding volume (0/1).
BMI: BMI value volume.
State: stress/rest value volume (0/1).

To Run the Code

Sample training/testing scripts are provided at the root folder as train_script.sh and test_script.sh.

• Train the model

python train.py --experiment_name 'train_DuRDN' --model_type 'model_cnn' --data_root '../../Data/Dataset_filename/' --net_G 'DuRDN' --batch_size 2 --n_patch_train 1 --patch_size_train 32 32 32 --eval_epochs 10 --save_epochs 10 --patch_size_eval 32 32 32 --lr 5e-4 --norm 'BN' --norm_pred_AC --use_scatter --use_scatter2 --use_scatter3 --use_gender --use_bmi --use_state --gpu_ids 0

where
--experiment_name experiment name for the code, and save all the training results in this under this "experiment_name" folder.
--model_type: model type used (default convolutioanl neural networks -- "model_cnn").
--dataset: dataset type.
--data_root: path of the dataset.
--net_G: neural network model used (default: 'DuRDN').
--batch_size: training batch size.
--n_patch_train: number of patches extracted from each image volume.
--patch_size_train: training patch size.
--eval_epochs: number of epoches for periodic validation.
--save_epochs: number of epoches for saving trained model.
--patch_size_eval: validation patch size.
--lr: learning rate.
--norm: normalization of convolutioanl layers in the networks.
--norm_pred_AC: mean normalization of the predicted images before testing.
--use_scatter: adding the 1st scatter-window image to input.
--use_scatter2: adding the 2nd scatter-window image to input.
--use_scatter3: adding the 3rd scatter-window image to input.
--use_gender: adding gender volume to input.
--use_bmi: adding BMI volume to input .
--use_state: adding stress/rest volume to input.
--gpu_ids: GPU configuration.

• Test the model

python test.py --resume './outputs/train_DuRDN/checkpoints/model_999.pt' --resume_epoch 999 --experiment_name 'test_DuRDN_999' --model_type 'model_cnn' --data_root '../../Data/Dataset_filename' --net_G 'DuRDN' --patch_size_eval 32 32 32 --lr 5e-4 --norm 'BN' --norm_pred_AC --use_scatter --use_scatter2 --use_scatter3 --use_gender --use_bmi --use_state --gpu_ids 0

where
resume: path of the model to be tested.
resume_epoch: training epoch of the model to be tested.
--experiment_name: experiment name for the code, and save all the testing results in this under this "experiment_name" folder.
--patch_size_eval: testing patch size.

Contact

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

Xiongchao Chen: xiongchao.chen@yale.edu, cxiongchao9587@gmail.com