From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries

TMLR Paper9098 Authors

Abstract: Accurate and rapid estimation of hemodynamic metrics, such as pressure and wall shear stress (WSS), is important for assessing the severity of Coronary Artery Disease (CAD). Existing approaches, including invasive Fractional Flow Reserve (FFR) measurements and computationally expensive Computational Fluid Dynamics (CFD) simulations, face challenges in invasiveness, cost, and speed. We present a framework for fast, non-invasive coronary hemodynamics prediction. The model encodes 1D vessel centerlines together with inlet flow rate using a transformer-based encoder, and predicts continuous wall-based fields via an anisotropic Radial Basis Function (RBF) decoder aligned with vessel morphology. To support training and evaluation, we introduce two datasets with paired steady-state OpenFOAM simulations: (i) a synthetic benchmark of $4{,}200$ single-vessel geometries with controlled anatomical variations, and (ii) a multi-vessel dataset derived from ImageCAS including $4{,}800$ cases spanning both right and left coronary arteries, generated by randomly introducing stenoses and varying physiologically plausible flow rates. Across both datasets, our method achieves lower pressure and WSS errors than strong neural-operator baselines (GNOT, Transolver, and ONO) at a fraction of the computational cost of CFD. On the multi-vessel dataset, using $1{,}024$ anisotropic RBF centers our model reduces the mean relative $\ell_2$ error by $52\%$ compared to the best neural-operator baseline, while at $128$ centers it requires $13.8\times$ fewer FLOPs than GNOT and still outperforms all baselines. The single-vessel dataset is publicly available.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Uri_Shaham1

Submission Number: 9098