Diffusion probabilistic priors for zero‐shot low‐dose CT image denoising Article Swipe
Xuan Liu
,
Yaoqin Xie
,
Chenbin Liu
,
Jun Cheng
,
Songhui Diao
,
Shan Tan
,
Xiaokun Liang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/mp.17431
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/mp.17431
Background Denoising low‐dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning‐based approaches have made significant advancements in this area in recent years. However, these methods typically require pairs of low‐dose and normal‐dose CT images for training, which are challenging to obtain in clinical settings. Existing unsupervised deep learning‐based methods often require training with a large number of low‐dose CT images or rely on specially designed data acquisition processes to obtain training data. Purpose To address these limitations, we propose a novel unsupervised method that only utilizes normal‐dose CT images during training, enabling zero‐shot denoising of low‐dose CT images. Methods Our method leverages the diffusion model, a powerful generative model. We begin by training a cascaded unconditional diffusion model capable of generating high‐quality normal‐dose CT images from low‐resolution to high‐resolution. The cascaded architecture makes the training of high‐resolution diffusion models more feasible. Subsequently, we introduce low‐dose CT images into the reverse process of the diffusion model as likelihood, combined with the priors provided by the diffusion model and iteratively solve multiple maximum a posteriori (MAP) problems to achieve denoising. Additionally, we propose methods to adaptively adjust the coefficients that balance the likelihood and prior in MAP estimations, allowing for adaptation to different noise levels in low‐dose CT images. Results We test our method on low‐dose CT datasets of different regions with varying dose levels. The results demonstrate that our method outperforms the state‐of‐the‐art unsupervised method and surpasses several supervised deep learning‐based methods. Our method achieves PSNR of 45.02 and 35.35 dB on the abdomen CT dataset and the chest CT dataset, respectively, surpassing the best unsupervised algorithm Noise2Sim in the comparative methods by 0.39 and 0.85 dB, respectively. Conclusions 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 .
Related Topics
Concepts
Artificial intelligence
Maximum a posteriori estimation
Noise reduction
Prior probability
Computer science
Medical imaging
Noise (video)
Pattern recognition (psychology)
Computer vision
Iterative reconstruction
Image quality
Image (mathematics)
Mathematics
Bayesian probability
Statistics
Maximum likelihood
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/mp.17431
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17431
- OA Status
- bronze
- Cited By
- 25
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403464026
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403464026Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/mp.17431Digital Object Identifier
- Title
-
Diffusion probabilistic priors for zero‐shot low‐dose CT image denoisingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-16Full publication date if available
- Authors
-
Xuan Liu, Yaoqin Xie, Chenbin Liu, Jun Cheng, Songhui Diao, Shan Tan, Xiaokun LiangList of authors in order
- Landing page
-
https://doi.org/10.1002/mp.17431Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17431Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17431Direct OA link when available
- Concepts
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Artificial intelligence, Maximum a posteriori estimation, Noise reduction, Prior probability, Computer science, Medical imaging, Noise (video), Pattern recognition (psychology), Computer vision, Iterative reconstruction, Image quality, Image (mathematics), Mathematics, Bayesian probability, Statistics, Maximum likelihoodTop concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
- Citations by year (recent)
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2025: 18, 2024: 7Per-year citation counts (last 5 years)
- References (count)
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47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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