CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912966113230848 |
|---|---|
| author | Yang, Liutao Wang, Zi Jing, Peiyuan Wang, Xiaowen Montoya-Zegarra, Javier A. Shi, Kuangyu Zhang, Daoqiang Yang, Guang |
| author_facet | Yang, Liutao Wang, Zi Jing, Peiyuan Wang, Xiaowen Montoya-Zegarra, Javier A. Shi, Kuangyu Zhang, Daoqiang Yang, Guang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13901 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution Yang, Liutao Wang, Zi Jing, Peiyuan Wang, Xiaowen Montoya-Zegarra, Javier A. Shi, Kuangyu Zhang, Daoqiang Yang, Guang Computer Vision and Pattern Recognition 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. |
| title | CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.13901 |