Diffusion models have recently enabled state-of-the-art reconstruction of positron emission tomography (PET) images while requiring only image training data. However, domain shift remains a key concern for clinical adoption: priors trained on images from one anatomy, acquisition protocol or pathology may produce artefacts on out-of-distribution data. We propose integrating steerable conditional diffusion (SCD) with our previously-introduced likelihood-scheduled diffusion (PET-LiSch) framework to improve the alignment of the diffusion model's prior to the target subject. At reconstruction time, for each diffusion step, we use low-rank adaptation (LoRA) to align the diffusion model prior with the target domain on the fly. Experiments on realistic synthetic 2D brain phantoms demonstrate that our approach suppresses hallucinated artefacts under domain shift, i.e. when our diffusion model is trained on perturbed images and tested on normal anatomy, our approach suppresses the hallucinated structure, outperforming both OSEM and diffusion model baselines qualitatively and quantitatively. These results provide a proof-of-concept that steerable priors can mitigate domain shift in diffusion-based PET reconstruction and motivate future evaluation on real data.

翻译：扩散模型最近实现了正电子发射断层扫描（PET）图像的最先进重建，且仅需图像训练数据。然而，领域偏移仍是临床采用的关键问题：基于特定解剖结构、采集协议或病理图像训练的先验模型可能在分布外数据上产生伪影。我们提出将可调控条件扩散（SCD）与我们先前提出的似然调度扩散（PET-LiSch）框架相结合，以改善扩散模型先验与目标对象的对齐。在重建过程中，针对每个扩散步骤，我们使用低秩自适应（LoRA）技术动态调整扩散模型先验以匹配目标域。基于真实合成二维脑体模的实验表明，我们的方法能有效抑制领域偏移下的幻觉伪影——即当扩散模型在扰动图像上训练并在正常解剖结构上测试时，我们的方法可抑制幻觉结构生成，在定性和定量评估上均优于OSEM及扩散模型基线方法。这些结果为可调控先验能够缓解基于扩散的PET重建中的领域偏移提供了概念验证，并为未来在真实数据上的评估奠定了基础。