CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution

• URL: http://arxiv.org/abs/2603.13901v1
• Date: Sat, 14 Mar 2026 11:28:23 GMT
• Title: CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution
• Abstract summary: We formulate PET super-resolution as posterior inference under heterogeneous system configurations.<n>We propose a CT-conditioned diffusion framework with physics-constrained sampling.<n>Under both standard and OOD settings, the proposed method consistently improves experimental metrics and lesion-level clinical relevance indicators.
• Score: 25.790321547743066
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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