基于变分膜平衡的物理信息神经网络用于改善颅内囊状动脉瘤的表面重建
A physics-informed neural network for improving surface reconstruction of intracranial saccular aneurysms via variational membrane equilibrium
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中文总结 AI 辅助
研究针对颅内囊状动脉瘤传统风险评估不足及表面重建问题，引入基于B样条表示的物理信息神经网络框架，通过嵌入变分平衡条件、开发流形一致CNN假设、建立拉普拉斯平衡驱动重建，为患者特定破裂风险评估建立计算生物标志物基础。
中文摘要 AI 辅助
颅内囊状动脉瘤（ISAs）带来严重健康风险，传统基于人群的风险分层评分对患者特定破裂风险评估能力有限。基于图像的计算方法虽受关注，但传统表面重建依赖数学平滑，会抑制成像伪影和真正的病理特征。拉普拉斯膜平衡长期用于动脉瘤壁力学，但未融入几何重建流程。本文引入具有B样条表示的物理信息神经网络（PINN）框架，其贡献包括：将变分平衡条件作为物理信息损失嵌入，取代数学L曲线准则；开发保持血管几何闭合表面拓扑的流形一致CNN假设；建立拉普拉斯平衡驱动的重建以过滤成像噪声并保留关键高曲率特征。应用于患者特定临床数据集表明该框架可消除非物理凹伪影且不影响真实几何异常。神经外科医生的临床评估证实所得风险图与术中观察一致，为颅内囊状动脉瘤患者特定破裂风险评估建立了计算生物标志物基础。
英文摘要
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 ($κ_1 T_1 + κ_2 T_2 = P$) 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 ($δΠ= 0$) 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.
