Dual-domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-angle Reconstruction of Low-dose Cardiac SPECT

(MICCAI 2023 Workshop, MILLanD 2023)

Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Qiong Liu, Albert J. Sinusas, Chi Liu

[Paper Link]

This repository contains the PyTorch implementation of the Dual-Domain Iterative Network for joint denoising and few-angle reconstruction of low-dose cardiac SPECT.

Citation

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

@article{chen2023joint,
      title={Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency},
      author={Chen, Xiongchao and Zhou, Bo and Xie, Huidong and Guo, Xueqi and Liu, Qiong and Sinusas, Albert J and Liu, Chi},
      journal={arXiv preprint arXiv:2305.10328},
      year={2023}
    }

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
|       ├── Recon_FD_FA_EM.mat  
|       ├── Recon_LD_LA_EM.mat
|       ├── Proj_FD_FA_EM.mat  
|       ├── Proj_FD_LA_EM.mat
|       ├── Proj_LD_LA_EM.mat
|       ├── Mask_Proj.mat
|   └── ...  
| 
├── valid               # contain validation files
|   ├── data1.h5
|       ├── Recon_FD_FA_EM.mat  
|       ├── Recon_LD_LA_EM.mat
|       ├── Proj_FD_FA_EM.mat  
|       ├── Proj_FD_LA_EM.mat
|       ├── Proj_LD_LA_EM.mat
|       ├── Mask_Proj.mat
|   └── ...  
|
├── test                # contain testing files
|   ├── data1.h5
|       ├── Recon_FD_FA_EM.mat  
|       ├── Recon_LD_LA_EM.mat
|       ├── Proj_FD_FA_EM.mat  
|       ├── Proj_FD_LA_EM.mat
|       ├── Proj_LD_LA_EM.mat
|       ├── Mask_Proj.mat
|   └── ...  
└── ...  

where
Recon_FD_FA_EM: reconstructed full-dose and full-angle images with a size of 72 x 72 x 40.
Recon_LD_LA_EM: reconstructed lose-dose and limited-angle images with a size of 72 x 72 x 40.
Proj_FD_FA_EM: full-dose and full-angle projections with a size of 32 x 32 x 20.
Proj_FD_LA_EM: full-dose and limited-angle projections with a size of 32 x 32 x 20.
Proj_LD_LA_EM: lose-dose and limited-angle projections with a size of 32 x 32 x 20.
Mask_Proj: binary projection mask with a size of 32 x 32 x 20. 1 refers to the central limited-angle regions. \

To Run the Code

Sample training/testing scripts are provided at the root folder as train.sh and test.sh.

• Train the model

python train.py --experiment_name 'train_1' --model_type 'model_cnn' --data_root './' --norm 'BN' --net_filter 32 --n_denselayer 6 --growth_rate 32 --lr_1 1e-3 --lr_2 1e-3 --lr_3 1e-3 --step_size 1 --gamma 0.99 --n_epochs 200 --batch_size 1 --eval_epochs 5 --snapshot_epochs 5 --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 convolutional neural networks).
--data_root: the path of the dataset.
--norm: batch normalization in the CNN modules (default: 'BN').
--net_filter: num of filters in the densely connected layers (default: 32).
--n_denselayer: num of the densely connected layers (default: 6).
--growth_rate: growth rate of the densely connected layers (default: 32).
--lr_1: learning rate of projection-domain modules (default: 1e-3).
--lr_2: learning rate of image-domain modules (default: 1e-3).
--lr_3: learning rate of weight recalibrator modules (default: 1e-3).
--step_size: num of epoch for learning rate decay .
--gamma: learning decay rate.
--n_epochs: num of epochs of training.
--batch_size: training batch size.
--test_epochs: number of epochs for periodic validation.
--save_epochs: number of epochs for saving trained model.
--gpu_ids: GPU configuration.

• Test the model

python test.py --resume './outputs/train_1/checkpoints/model_xxx.pt' --experiment_name 'test_1_xxx' --model_type 'model_cnn' --data_root '../' --norm 'BN' --net_filter 32 --n_denselayer 6 --growth_rate 32 --batch_size 1 --gpu_ids 0

where
--resume: the path of the model to be tested.
--resume_epoch: training epoch of the model to be tested.

Data Availability

The original dataset in this study is available from the corresponding author ([email protected]) upon reasonable request and approval of Yale University.

Contact

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

Xiongchao Chen: [email protected], [email protected]