Diffusion models have shown great promise in medical image denoising and reconstruction, but their application to Positron Emission Tomography (PET) imaging remains limited by tracer-specific contrast variability and high computational demands. In this work, we proposed an anatomical prior-guided PET image reconstruction method based on diffusion models, inspired by the deep diffusion image prior (DDIP) framework. The proposed method alternated between diffusion sampling and model fine-tuning guided by the PET sinogram, enabling the reconstruction of high-quality images from various PET tracers using a score function pretrained on a dataset of another tracer. To improve computational efficiency, the half-quadratic splitting (HQS) algorithm was adopted to decouple network optimization from iterative PET reconstruction. The proposed method was evaluated using one simulation and two clinical datasets. For the simulation study, a model pretrained on [$^{18}$F]FDG data was tested on [$^{18}$F]FDG data and amyloid-negative PET data to assess out-of-distribution (OOD) performance. For the clinical-data validation, ten low-dose [$^{18}$F]FDG datasets and one [$^{18}$F]Florbetapir dataset were tested on a model pretrained on data from another tracer. Experiment results show that the proposed PET reconstruction method can generalize robustly across tracer distributions and scanner types, providing an efficient and versatile reconstruction framework for low-dose PET imaging.

翻译：扩散模型在医学图像去噪与重建中展现出巨大潜力，但其在正电子发射断层扫描（PET）成像中的应用仍受限于示踪剂特异性对比度变异性和高计算需求。本研究受深度扩散图像先验（DDIP）框架启发，提出了一种基于扩散模型的解剖先验引导PET图像重建方法。该方法在扩散采样与PET正弦图引导的模型微调之间交替进行，能够利用在一种示踪剂数据集上预训练的评分函数，重建多种PET示踪剂的高质量图像。为提升计算效率，采用半二次分裂（HQS）算法将网络优化与迭代PET重建解耦。所提方法通过一个仿真数据集和两个临床数据集进行评估。仿真研究中，在[$^{18}$F]FDG数据上预训练的模型分别于[$^{18}$F]FDG数据和淀粉样蛋白阴性PET数据上进行测试，以评估分布外（OOD）性能。临床数据验证中，十个低剂量[$^{18}$F]FDG数据集和一个[$^{18}$F]Florbetapir数据集在另一种示踪剂预训练的模型上测试。实验结果表明，所提出的PET重建方法能够稳健地跨示踪剂分布和扫描仪类型泛化，为低剂量PET成像提供高效且通用的重建框架。