用物理模型统一恢复多剂量水平的PET探测数据，提升低剂量成像质量。

Low-dose positron emission tomography (LD-PET) reduces radiation exposure but leads to poor image quality and hinders diagnostic confidence. Existing supervised LD to standard-dose (SD) PET recovery methods often fail to generalise across dose variations, while dose-agnostic supervised methods require paired LD-SD data. Current self-supervised methods, although more flexible, typically produce inferior results, including loss of anatomical details and oversmoothing pathological features. These pose major limitations for practical applications. To overcome these limitations, we propose SinoDiff, a novel self-supervised, physics-consistent diffusion framework for the recovery of PET sinograms across multiple predefined dose levels. Unlike the noise simulation in traditional diffusion methods, SinoDiff integrates the PET acquisition model into the forward diffusion process via Poisson thinning, enabling physically consistent sampling/modelling of dose-dependent count statistics. During the reverse diffusion process, SinoDiff estimates the incremental change in PET signals from predefined dose levels. Therefore, SinoDiff is a single, unified model that requires no retraining across multiple dose levels. To consider the characteristics of PET sinogram, we incorporate a frequency-domain convolution to capture long-range dependencies across projection angles and detector bins. Experiments on [18F]-FDG and [18F]-FDOPA datasets demonstrate that SinoDiff achieves competitive performance against supervised and self-supervised baselines across multiple dose levels.