标题： 基于深度扩散图像先验的PET图像重建 显示英文标题

标题： PET Image Reconstruction Using Deep Diffusion Image Prior

摘要： 扩散模型在医学图像去噪和重建方面表现出巨大的潜力，但由于示踪剂特异性对比度的变化和高计算需求，其在正电子发射断层扫描（PET）成像中的应用仍然有限。 在本工作中，我们提出了一种基于扩散模型的解剖先验引导的PET图像重建方法，该方法受到深度扩散图像先验（DDIP）框架的启发。 所提出的方法在扩散采样和由PET sinogram引导的模型微调之间交替进行，使得可以使用另一个示踪剂数据集预训练的得分函数从各种PET示踪剂中重建高质量图像。 为了提高计算效率，采用了半二次分裂（HQS）算法，将网络优化与迭代PET重建解耦。 所提出的方法使用了一个模拟数据集和两个临床数据集进行了评估。 对于模拟研究，一个在[$^{18}$F]FDG数据上预训练的模型被测试在淀粉样蛋白阴性PET数据上，以评估分布外（OOD）性能。 对于临床数据验证，十个低剂量[$^{18}$F]FDG数据集和一个[$^{18}$F]Florbetapir数据集被测试在一个基于其他示踪剂数据预训练的模型上。 实验结果表明，所提出的PET重建方法可以在示踪剂分布和扫描仪类型之间稳健地泛化，为低剂量PET成像提供了一种高效且通用的重建框架。 显示英文摘要

摘要： 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 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.