From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries

[2605.27578] From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computational Engineering, Finance, and Science arXiv:2605.27578 (cs) [Submitted on 26 May 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries Authors: Reza Akbarian Bafghi , Sukirt Thakur , Maziar Raissi View a PDF of the paper titled From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries, by Reza Akbarian Bafghi and 2 other authors View PDF HTML (experimental) 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 learned surrogate for fast prediction of CFD-simulated coronary hemodynamics from vessel centerline geometry. 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 neural-operator baselines. The single-vessel dataset is publicly available at this https URL Comments: Accepted by Transactions on Machine Learning Research (TMLR), 2026 Subjects: Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2605.27578 [cs.CE] (or arXiv:2605.27578v2 [cs.CE] for this version) https://doi.org/10.48550/arXiv.2605.27578 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Reza Akbarian Bafghi [ view email ] [v1] Tue, 26 May 2026 18:48:37 UTC (766 KB) [v2] Fri, 14 Aug 2026 23:59:17 UTC (958 KB) Full-text links: Access Paper: View a PDF of the paper titled From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries, by Reza Akbarian Bafghi and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CE < prev | next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• From Centerlines to Hemodynamics: Anisotropic RBF Decoders for Coronary Arteries#608886
• How does human blood rheology influence arterial and cardiovascular device hemodynamics? A comprehensive review, discussion, and future directions#1034832
• RHEOLOGICAL PROPERTIES OF BLOOD AND ITS BIOPHYSICAL ROLE IN HEMODYNAMIC PROCESSES#237775
• A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation#128278
• Effect of Evolocumab on Coronary Plaque Characteristics#966225