PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917275178631168 |
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| author | Shi, Yang Wang, Jingchao Lu, Liangsi Huang, Mingxuan He, Ruixin Xie, Yifeng Liu, Hanqian Guo, Minzhe Liang, Yangyang Zhang, Weipeng Li, Zimeng Chen, Xuhang |
| author_facet | Shi, Yang Wang, Jingchao Lu, Liangsi Huang, Mingxuan He, Ruixin Xie, Yifeng Liu, Hanqian Guo, Minzhe Liang, Yangyang Zhang, Weipeng Li, Zimeng Chen, Xuhang |
| contents | Positron Emission Tomography (PET) is crucial in medicine, but its clinical use is limited due to high signal-to-noise ratio doses increasing radiation exposure. Lowering doses increases Poisson noise, which current denoising methods fail to handle, causing distortions and artifacts. We propose a Poisson Consistent U-Net (PC-UNet) model with a new Poisson Variance and Mean Consistency Loss (PVMC-Loss) that incorporates physical data to improve image fidelity. PVMC-Loss is statistically unbiased in variance and gradient adaptation, acting as a Generalized Method of Moments implementation, offering robustness to minor data mismatches. Tests on PET datasets show PC-UNet improves physical consistency and image fidelity, proving its ability to integrate physical information effectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising Shi, Yang Wang, Jingchao Lu, Liangsi Huang, Mingxuan He, Ruixin Xie, Yifeng Liu, Hanqian Guo, Minzhe Liang, Yangyang Zhang, Weipeng Li, Zimeng Chen, Xuhang Computer Vision and Pattern Recognition Artificial Intelligence Positron Emission Tomography (PET) is crucial in medicine, but its clinical use is limited due to high signal-to-noise ratio doses increasing radiation exposure. Lowering doses increases Poisson noise, which current denoising methods fail to handle, causing distortions and artifacts. We propose a Poisson Consistent U-Net (PC-UNet) model with a new Poisson Variance and Mean Consistency Loss (PVMC-Loss) that incorporates physical data to improve image fidelity. PVMC-Loss is statistically unbiased in variance and gradient adaptation, acting as a Generalized Method of Moments implementation, offering robustness to minor data mismatches. Tests on PET datasets show PC-UNet improves physical consistency and image fidelity, proving its ability to integrate physical information effectively. |
| title | PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2510.14995 |