{"ID":2857323,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14995","arxiv_id":"2510.14995","title":"PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising","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.","short_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-N...","url_abs":"https://arxiv.org/abs/2510.14995","url_pdf":"https://arxiv.org/pdf/2510.14995v1","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\"]","published":"2025-10-10T04:26:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}