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arXiv · 2510.14995

PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising

Yang Shi·Jingchao Wang·Liangsi Lu·Mingxuan Huang·Ruixin He·Yifeng Xie·Hanqian Liu·Minzhe Guo·Yangyang Liang·Weipeng Zhang·Zimeng Li·Xuhang Chen
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Abstract

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.

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Yang Shi, Jingchao Wang, Liangsi Lu, Mingxuan Huang, Ruixin He, Yifeng Xie, Hanqian Liu, Minzhe Guo, Yangyang Liang, Weipeng Zhang, Zimeng Li, Xuhang Chen. 2025-10-10. PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising. https://doi.org/10.1109/bibm66473.2025.11356275

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