用CT引导扩散模型，结合物理模型提升PET图像超分辨率。
CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution
- 以CT为条件，通过扩散模型学习高质PET/CT配对数据的先验。
- 引入扫描仪物理模型与梯度优化，保持图像与真实测量一致。
- 在真实和分布外场景中均减少幻觉，提升病灶识别准确性。
PET超分辨率因缺乏同一受试者的多分辨率配对扫描而高度欠约束，且有效分辨率受扫描仪物理特性（如点扩散函数、探测器几何结构和采集参数）决定。这限制了监督端到端训练，导致纯图像域生成修复在解剖与物理约束弱时易产生幻觉结构。本文将PET超分辨率建模为异构系统配置下的后验推断，提出一种基于CT条件的扩散先验与物理约束采样框架。训练阶段，利用高质量PET/CT配对数据学习条件扩散先验，通过交叉注意力实现解剖引导，无需配对低分辨-高分辨PET数据。推理阶段，通过包含显式点扩散函数效应的扫描仪感知前向模型强制测量一致性，并采用基于梯度的数据一致性精炼。在标准及分布外设置下，该方法在实验指标和病灶级临床相关性指标上持续优于强基线，同时减少幻觉伪影并提升结构保真度。
原文摘要 · Abstract (English)
PET super-resolution is highly under-constrained because paired multi-resolution scans from the same subject are rarely available, and effective resolution is determined by scanner-specific physics (e.g., PSF, detector geometry, and acquisition settings). This limits supervised end-to-end training and makes purely image-domain generative restoration prone to hallucinated structures when anatomical and physical constraints are weak. We formulate PET super-resolution as posterior inference under heterogeneous system configurations and propose a CT-conditioned diffusion framework with physics-constrained sampling. During training, a conditional diffusion prior is learned from high-quality PET/CT pairs using cross-attention for anatomical guidance, without requiring paired LR--HR PET data. During inference, measurement consistency is enforced through a scanner-aware forward model with explicit PSF effects and gradient-based data-consistency refinement. Under both standard and OOD settings, the proposed method consistently improves experimental metrics and lesion-level clinical relevance indicators over strong baselines, while reducing hallucination artifacts and improving structural fidelity.
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