Hemodynamic Simulation on Patient-Specific Intracranial Aneurysms Using Physics-Informed Neural Network
10.16156/j.1004-7220.2025.03.030
- VernacularTitle:基于物理信息神经网络的颅内动脉瘤血流动力学模拟
- Author:
Wen ZHANG
1
;
Tianxin SHI
;
Shiyao CHEN
;
Yunzhang CHENG
;
Nan LÜ
;
Mingwei ZHANG
Author Information
1. 上海理工大学健康科学与工程学院,上海 200093
- Publication Type:Journal Article
- Keywords:
physics-informed neural network;
computational fluid dynamics;
intracranial aneurysm;
hemodynamics
- From:
Journal of Medical Biomechanics
2025;40(3):741-748
- CountryChina
- Language:Chinese
-
Abstract:
Objective To use a physics-informed neural network(PINN)-based model to predict hemodynamics in intracranial aneurysms and address the problems of long simulation time and high computational cost in traditional computational fluid dynamics(CFD)simulations.Methods The PINN model was trained using only the computational domain coordinates and sparse velocity measurement points from CFD data of clinical patients.The predicted blood flow velocity,pressure,and wall shear stress(WSS)from the PINN model were compared with CFD simulation results.Results The proposed method was used to test and validate data from four different patients.For velocity prediction,the average mean absolute error(MAE),average mean relative error(MRE),average mean squared error(MSE)was 4.60％,6.61％,and 0.229％,respectively.For WSS prediction,the average MAE,MRE and MSE was 5.54％,8.58％,and 0.510％,respectively.The PINN model demonstrated a good generalization capability across different aneurysm models and could reduce the computation time of hemodynamics from several hours to just a few seconds.Conclusions The PINN model can effectively compensate for incomplete measurement data through physical constraints,even when boundary conditions are unknown and measurement data are sparse.It can rapidly and accurately simulate the hemodynamics of intracranial aneurysms.This method has the potential to provide effective support for clinical risk prediction in intracranial aneurysms.
