针对低剂量脑PET噪声特性，提出新型去噪扩散模型以提升定量重建精度。
HDDPM: Heteroscedastic Denoising Diffusion Probabilistic Model for Quantitative Low-Count Brain PET Recovery
- 设计强度感知的异方差噪声机制，模拟真实PET噪声的空间依赖性。
- 在1%低剂量下显著降低高低活性区域的测量误差，优于传统模型。
- 适合追求高精度低剂量PET重建的医学影像研究人员使用。
正电子发射断层扫描（PET）需在诊断质量与辐射剂量间取得平衡。低计数PET噪声具有非高斯、非平稳及空间依赖性，其强度与局部活性成正比，受迭代重建和物理修正影响。标准去噪扩散概率模型（DDPM）忽略这些特性，其前向过程添加各向同性、同方差高斯噪声，无法反映成像系统产生的真实退化。为此，本文提出异方差残差扩散模型（HDDPM），其前向污染过程具备强度自适应能力。设计固定泊松基方差模块生成体素级噪声图，使低活性区域承受更强噪声扰动；同时网络在显式剂量分数条件下预测低计数至标准计数的残差。在三种不同扫描仪上，使用内部及外部数据集，在1%至50%模拟剂量水平下评估。HDDPM与各向同性DDPM整体图像质量相当，但在最低剂量（1%）外部扫描中表现更优，显著降低高低活性区域的测量误差。结果表明，所提异方差噪声机制可行，为定量低计数PET重建提供了物理驱动的归纳偏置。
原文摘要 · Abstract (English)
Positron emission tomography (PET) seeks to balance diagnostic quality with ra-diation dose. Low-count PET noise is non-Gaussian, non-stationary, and spatial-ly dependent. It scales directly with local activity and is shaped by iterative recon-struction and physical corrections. Standard denoising diffusion probabilistic models (DDPMs) ignore these PET properties. Their forward process adds iso-tropic, homoscedastic Gaussian noise to the target. Such an approach fails to cap-ture the realistic physical degradation generated by the imaging system. To ad-dress the above limitations, this study introduces a heteroscedastic residual diffu-sion model (HDDPM) for low-count brain PET recovery in which the forward corruption is itself intensity-aware. We designed a fixed, Poisson-based variance module to generate voxel-wise noise maps. These maps naturally place stronger noise perturbation on low-activity regions than high-activity ones, meanwhile the network predicts the low-to-standard-count residual under explicit dose-fraction conditioning. We evaluated our proposed model (HDDPM) alongside generative frameworks across three different scanners, using both internal and external da-tasets at various simulated dose levels (1% to 50%). HDDPM and isotropic DDPM showed comparable overall image quality, but HDDPM stood out in the lowest-dose (1%) external scans. It is highly reliable and significantly reduces measurement errors in both high- and low-activity regions, compared to the standard model. These results support that heteroscedastic noising with the pro-posed HDDPM is feasible, and it provides a physically motivated inductive bias for quantitative low-count PET recovery by reflecting the activity-dependent noise structure of PET.
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