Biomedizinische Technik. Biomedical engineering · 2026
Physics-informed neural network-based simulation of pulmonary arterial hemodynamics abnormalities.
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Lu Ziteng, Huang Haisong, Wu Lanping, Sun Haibin, Liu Yanfeng
Аннотация
OBJECTIVES: To predict abnormal pulmonary artery hemodynamics caused by ventricular septal defect (VSD) using Physics-Informed Neural Networks (PINN) and address the challenges of high computational cost in traditional Computational Fluid Dynamics (CFD) and difficulty in obtaining measurement data.
METHODS: The PINN model was trained using boundary conditions and scattered clinical CFD data, with dynamic weighting factors incorporated to enhance training efficiency and optimize predictions. Model outputs for blood flow velocity and pressure were subsequently evaluated against CFD simulation results.
RESULTS: The PINN accurately reproduced velocity and pressure fields across pulmonary artery models using only boundary conditions and sparse internal measurements. For velocity prediction, the average RMSE, MAE, and MRE for components u, v, and w ranged from 0.274 to 0.832 %, 0.448-1.096 %, and 0.833-1.341 %, respectively. For pressure prediction, the average RMSE, MAE, and MRE ranged from 2.953 to 5.145 %, 3.264-5.679 %, and 0.376-0.565 %, respectively. These findings demonstrate that the framework generalizes well and provides reliable hemodynamic estimation with limited input data.
CONCLUSIONS: The PINN model compensates for incomplete measurement data through physical constraints, enabling rapid and accurate prediction of pulmonary artery hemodynamics and offering a promising non-invasive alternative for pulmonary artery pressure measurement.
METHODS: The PINN model was trained using boundary conditions and scattered clinical CFD data, with dynamic weighting factors incorporated to enhance training efficiency and optimize predictions. Model outputs for blood flow velocity and pressure were subsequently evaluated against CFD simulation results.
RESULTS: The PINN accurately reproduced velocity and pressure fields across pulmonary artery models using only boundary conditions and sparse internal measurements. For velocity prediction, the average RMSE, MAE, and MRE for components u, v, and w ranged from 0.274 to 0.832 %, 0.448-1.096 %, and 0.833-1.341 %, respectively. For pressure prediction, the average RMSE, MAE, and MRE ranged from 2.953 to 5.145 %, 3.264-5.679 %, and 0.376-0.565 %, respectively. These findings demonstrate that the framework generalizes well and provides reliable hemodynamic estimation with limited input data.
CONCLUSIONS: The PINN model compensates for incomplete measurement data through physical constraints, enabling rapid and accurate prediction of pulmonary artery hemodynamics and offering a promising non-invasive alternative for pulmonary artery pressure measurement.