Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911310768242688 |
|---|---|
| author | Garnier, Paul Jeken-Rico, Pablo Lannelongue, Vincent Faitini, Chiara Goetz, Aurèle Chanvillard, Lea Nemer, Ramy Viquerat, Jonathan Pelissier, Ugo Meliga, Philippe Sédat, Jacques Liebig, Thomas Chau, Yves Hachem, Elie |
| author_facet | Garnier, Paul Jeken-Rico, Pablo Lannelongue, Vincent Faitini, Chiara Goetz, Aurèle Chanvillard, Lea Nemer, Ramy Viquerat, Jonathan Pelissier, Ugo Meliga, Philippe Sédat, Jacques Liebig, Thomas Chau, Yves Hachem, Elie |
| contents | Intracranial aneurysms remain a major cause of neurological morbidity and mortality worldwide, where rupture risk is tightly coupled to local hemodynamics particularly wall shear stress and oscillatory shear index. Conventional computational fluid dynamics simulations provide accurate insights but are prohibitively slow and require specialized expertise. Clinical imaging alternatives such as 4D Flow MRI offer direct in-vivo measurements, yet their spatial resolution remains insufficient to capture the fine-scale shear patterns that drive endothelial remodeling and rupture risk while being extremely impractical and expensive.
We present a graph neural network surrogate model that bridges this gap by reproducing full-field hemodynamics directly from vascular geometries in less than one minute per cardiac cycle. Trained on a comprehensive dataset of high-fidelity simulations of patient-specific aneurysms, our architecture combines graph transformers with autoregressive predictions to accurately simulate blood flow, wall shear stress, and oscillatory shear index. The model generalizes across unseen patient geometries and inflow conditions without mesh-specific calibration. Beyond accelerating simulation, our framework establishes the foundation for clinically interpretable hemodynamic prediction. By enabling near real-time inference integrated with existing imaging pipelines, it allows direct comparison with hospital phase-diagram assessments and extends them with physically grounded, high-resolution flow fields.
This work transforms high-fidelity simulations from an expert-only research tool into a deployable, data-driven decision support system. Our full pipeline delivers high-resolution hemodynamic predictions within minutes of patient imaging, without requiring computational specialists, marking a step-change toward real-time, bedside aneurysm analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment Garnier, Paul Jeken-Rico, Pablo Lannelongue, Vincent Faitini, Chiara Goetz, Aurèle Chanvillard, Lea Nemer, Ramy Viquerat, Jonathan Pelissier, Ugo Meliga, Philippe Sédat, Jacques Liebig, Thomas Chau, Yves Hachem, Elie Machine Learning Fluid Dynamics Intracranial aneurysms remain a major cause of neurological morbidity and mortality worldwide, where rupture risk is tightly coupled to local hemodynamics particularly wall shear stress and oscillatory shear index. Conventional computational fluid dynamics simulations provide accurate insights but are prohibitively slow and require specialized expertise. Clinical imaging alternatives such as 4D Flow MRI offer direct in-vivo measurements, yet their spatial resolution remains insufficient to capture the fine-scale shear patterns that drive endothelial remodeling and rupture risk while being extremely impractical and expensive. We present a graph neural network surrogate model that bridges this gap by reproducing full-field hemodynamics directly from vascular geometries in less than one minute per cardiac cycle. Trained on a comprehensive dataset of high-fidelity simulations of patient-specific aneurysms, our architecture combines graph transformers with autoregressive predictions to accurately simulate blood flow, wall shear stress, and oscillatory shear index. The model generalizes across unseen patient geometries and inflow conditions without mesh-specific calibration. Beyond accelerating simulation, our framework establishes the foundation for clinically interpretable hemodynamic prediction. By enabling near real-time inference integrated with existing imaging pipelines, it allows direct comparison with hospital phase-diagram assessments and extends them with physically grounded, high-resolution flow fields. This work transforms high-fidelity simulations from an expert-only research tool into a deployable, data-driven decision support system. Our full pipeline delivers high-resolution hemodynamic predictions within minutes of patient imaging, without requiring computational specialists, marking a step-change toward real-time, bedside aneurysm analysis. |
| title | Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment |
| topic | Machine Learning Fluid Dynamics |
| url | https://arxiv.org/abs/2512.09013 |