Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further exacerbated in limited-angle acquisitions. While deep learning methods demonstrate promising performance, their generalization to unseen clinical data distributions remains limited without extensive retraining. We propose PET-Adapter, a test-time domain adaptation framework for generative PET reconstruction models pretrained solely on phantom data. Our method enables adaptation to clinical datasets with varying anatomies, tracers, and scanner configurations without requiring paired ground truth. PET-Adapter introduces layer-wise low-rank anatomical conditioning during adaptation and Ordered Subset Expectation Maximization-based warm-starting that initializes the generation from physics-informed reconstructions, reducing diffusion steps from 50 to 2 without compromising quality. Experiments across multiple clinical datasets demonstrate superior 3D reconstruction performance in both full-angle and limited-angle settings, highlighting the clinical feasibility and computational efficiency of the proposed approach.

翻译：正电子发射断层扫描（PET）图像重建本质上受泊松噪声和物理退化因素的挑战，这些因素在有限角度采集中进一步加剧。尽管深度学习方法展现出良好的性能，但在未经大量重新训练的情况下，其对未见临床数据分布的泛化能力仍然有限。我们提出PET-Adapter，一种用于生成式PET重建模型的测试时域自适应框架，该模型仅基于体模数据进行预训练。我们的方法能够使模型适应不同解剖结构、示踪剂和扫描仪配置的临床数据集，而无需配对真值。PET-Adapter在自适应过程中引入了逐层低秩解剖条件调制，以及基于有序子集期望最大化的热启动策略，该策略从物理信息重建初始化生成过程，将扩散步数从50步减少到2步，且不牺牲重建质量。跨多个临床数据集的实验表明，该方法在全角度和有限角度设置下均展现出卓越的三维重建性能，凸显了所提方法的临床可行性和计算效率。