DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT

(Medical Physics 2022)

Xiongchao Chen, Bo Zhou, Huidong Xie, Tianshun Miao, Hui Liu, Wolfgang Holler, MingDe Lin, Edward J. Miller, Richard E. Carson, Albert J. Sinusas, Chi Liu

[Paper Link]

This repository contains the PyTorch implementation of DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT

Citation

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

@article{chen2023dudoss,
  title={DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT},
  author={Chen, Xiongchao and Zhou, Bo and Xie, Huidong and Miao, Tianshun and Liu, Hui and Holler, Wolfgang and Lin, MingDe and Miller, Edward J and Carson, Richard E and Sinusas, Albert J and others},
  journal={Medical Physics},
  volume={50},
  number={1},
  pages={89--103},
  year={2023},
  publisher={Wiley Online Library}
}

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.

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, xiongchao220@outlook.com