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

• URL: http://arxiv.org/abs/2510.14995v1
• Date: Fri, 10 Oct 2025 04:26:26 GMT
• Title: PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
• Authors: Yang Shi, Jingchao Wang, Liangsi Lu, Mingxuan Huang, Ruixin He, Yifeng Xie, Hanqian Liu, Minzhe Guo, Yangyang Liang, Weipeng Zhang, Zimeng Li, Xuhang Chen,
• Abstract summary: Positron Emission Tomography (PET) is crucial in medicine, but its clinical use is limited due to high signal-to-noise ratio doses.<n>We propose a Poisson Consistent U-Net (PC-UNet) model with a new Poisson Variance and Mean Consistency Loss (PVMC-Loss) to improve image fidelity.
• Score: 11.375263699816339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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