Temporal Diffusion Bridge Model for Vessel Extraction Using Physics-informed Synthetic Data
Accurate artery extraction from X-ray coronary angiography (XCA) is essential for coronary artery disease diagnosis and intervention. However, low vessel visibility, respiratory and cardiac motion, and limited annotations challenge existing methods. Moreover, most deep learning approaches produce binary vessel masks without preserving clinically relevant intra-arterial contrast information. We propose a Temporal Diffusion Bridge Model (TDBM) that formulates vessel extraction as image translation and learns from physics-informed synthetic data without manual vessel annotations. A physics-based XCA synthesis engine generates diverse, perfectly aligned pairs of contrast-enhanced and contrast...
| Authors | Jinkui Hao, Donald R. Cantrell, Ramez Abdalla et al. |
|---|---|
| Primary category | eess.IV |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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