Geometry-Guided Graph Encoding Network (G³e-Net) for Patient-Specific Prediction of Vascular Haemodynamics

Patient-specific haemodynamic assessment is essential for clarifying vascular pathophysiology and guiding clinical decision-making. While computational fluid dynamics offers high-fidelity flow quantification, its routine clinical use remains limited by the time-consuming iterative solution process required for patient-specific simulations. To tackle these issues, we developed a geometry-guided graph encoding network (G³e-Net), a CFD-supervised image-derived prediction framework that learns anatomically informed representations from CTA-derived vascular geometries. G³e-Net coupled centerline-guided spectral encoding to resolve global topology with multi-scale curvature descriptors capturing local boundary nuances. An attention mechanism then combined these different spatial representations, allowing the network to dynamically balance global connectivity with local geometry in complicated vascular structures. By learning a direct mapping from CTA-derived point clouds to three-dimensional haemodynamic fields, G³e-Net enables rapid functional estimation without requiring volumetric meshing or iterative numerical optimization during inference. Extensive validation on thoracic aortas and carotid arteries from multiple hospitals confirmed that G³e-Net outperformed state-of-the-art baselines, reducing prediction errors for velocity, pressure, and WSS by 10-20%. Notably, inference on high-resolution aortic geometries comprising approximately 200,000 nodes was completed in less than 4 seconds, providing substantial acceleration compared with conventional CFD. These results support the potential of anatomically informed graph representation learning for rapid image-derived haemodynamic analysis and patient-specific functional vascular assessment in thoracic aortic and carotid artery applications.