Cross-Domain Iterative Network (CDI-Net) for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Cardiac SPECT

(MICCAI 2023 Workshop, MLMI 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 CDI-Net for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Cardiac SPECT.

Citation

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

@article{chen2023cross,
  title={Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT},
  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:2305.10326},
  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
|       ├── Amap.mat  
|       ├── Recon_LD_LA_EM.mat
|       ├── Proj_FD_FA_EM.mat  
|       ├── Proj_LD_LA_EM.mat
|       ├── Mask_Proj.mat
|   └── ...  
| 
├── valid               # contain validation files
|   ├── data1.h5
|       ├── Amap.mat  
|       ├── Recon_LD_LA_EM.mat
|       ├── Proj_FD_FA_EM.mat  
|       ├── Proj_LD_LA_EM.mat
|       ├── Mask_Proj.mat
|   └── ...  
|
├── test                # contain testing files
|   ├── data1.h5
|       ├── Amap.mat  
|       ├── Recon_LD_LA_EM.mat
|       ├── Proj_FD_FA_EM.mat  
|       ├── Proj_LD_LA_EM.mat
|       ├── Mask_Proj.mat
|   └── ...  
└── ...  

where
Amap: CT-derived attenuation maps 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_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. \

• 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_G1 1e-3 --lr_G2 1e-4 --step_size 1 --gamma 0.99 --n_epochs 400 --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_G1: learning rate of Proj-Nets (default: 1e-3).
--lr_G2: learning rate of Img-Nets (default: 1e-4).
--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/checkpoints/model_xxx.pt' --experiment_name 'test_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 (chi.liu@yale.edu) 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: xiongchao.chen@yale.edu, cxiongchao9587@gmail.com