A physics-informed neural network for improving surface reconstruction of intracranial saccular aneurysms via variational membrane equilibrium

Abstract

Intracranial saccular aneurysms (ISAs) pose severe health risks, yet conventional population-based risk stratification scores (PHASES, UIATS, and ELAPSS) offer limited capacity for patient-specific rupture risk assessment. Image-based computational approaches have gained prominence, but traditional surface reconstruction relies on mathematical smoothing (e.g., L-curve criteria) that indiscriminately suppresses both imaging artifacts and genuine pathological features such as rupture-prone blebs. Although Laplace's membrane equilibrium ($\kappa_1 T_1 + \kappa_2 T_2 = P$) has long governed aneurysm wall mechanics (Humphrey and Kyriacou [Neurol. Res., 18 (1996)]), its integration into geometric reconstruction pipelines remains unexplored. This work introduces a physics-informed neural network (PINN) framework with B-spline representations, whose key contributions are: (i) embedding the variational equilibrium condition ($\delta \Pi = 0$) as a physics-informed loss that replaces the mathematical L-curve criterion with a biomechanically grounded artifact discrimination, (ii) developing a Manifold-Consistent CNN ansatz that preserves the closed-surface topology of vascular geometries, and (iii) establishing a Laplace equilibrium-driven reconstruction that filters imaging noise while preserving diagnostically critical high-curvature features. Application to patient-specific clinical datasets demonstrates that the framework eliminates non-physical concave artifacts without compromising genuine geometric anomalies. Clinical evaluation by a practicing neurosurgeon confirms that the resulting risk map---with rupture risk concentrated at the dome apex---is consistent with intraoperative observations, establishing a computational biomarker foundation for patient-specific rupture risk assessment of intracranial saccular aneurysms.