Diffusion probabilistic priors for zero‐shot low‐dose CT image denoising
We propose a novel low-dose CT image denoising method based on diffusion model. Our proposed method only requires normal-dose CT images as training data, greatly alleviating the data scarcity issue faced by most deep learning-based methods. At the same time, as an unsupervised algorithm, our method achieves very good qualitative and quantitative results. The Codes are available in https://github.com/DeepXuan/Dn-Dp.
Authors
- Xuan Liu (ORCID: https://orcid.org/0000-0001-5370-937X)
- Chenbin Liu (ORCID: https://orcid.org/0000-0002-2037-5630)
- Xiaokun Liang (ORCID: https://orcid.org/0000-0002-1207-5726)
- Jun Cheng (ORCID: https://orcid.org/0000-0002-7771-2132)
- Songhui Diao (ORCID: https://orcid.org/0000-0002-0971-8591)
- Shan Tan (ORCID: https://orcid.org/0000-0001-9350-5128)
- Yaoqin Xie (ORCID: https://orcid.org/0000-0002-1412-2354)
Institutions
- Chinese Academy of Sciences (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1002/mp.17431
- Citations
- 32
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 12.94
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China