基于泊松统计的PET图像去噪模型，有效抑制低剂量下的噪声失真。
PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
- 引入泊松均值与方差一致性损失，融合物理先验知识
- 在低剂量下保持图像保真度，提升信噪比达18.7%
- 适合需要降低辐射剂量的临床PET成像应用
正电子发射断层扫描（PET）在医学中至关重要，但高剂量导致的辐射暴露限制了其临床应用。降低剂量会加剧泊松噪声，现有去噪方法难以应对，造成图像畸变和伪影。本文提出泊松一致性U-Net（PC-UNet），采用新的泊松方差与均值一致性损失（PVMC-Loss），融入物理数据以提升图像保真度。该损失在方差和梯度上具有统计无偏性，具备广义矩估计特性，对数据微小偏差具鲁棒性。在多个PET数据集上的测试表明，PC-UNet显著提升物理一致性与图像质量，验证了其有效整合物理信息的能力。
原文摘要 · Abstract (English)
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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