Three-dimensional physics-informed neural network simulation in coronary artery trees

Three-dimensional physics-informed neural network simulation in coronary artery trees

This study introduces a novel approach using 3D Physics-Informed Neural Networks (PINNs) for simulating blood flow in coronary arteries, integrating deep learning with fundamental physics principles. By merging physics-driven models with clinical datasets, our methodology accurately predicts fractio...

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Bibliographic Details
Main Authors: Alzhanov, Nursultan, Ng, Eddie Yin Kwee, Zhao, Yong
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2024
Subjects:
Engineering
Blood flow simulation
Coronal stenosis
Online Access:https://hdl.handle.net/10356/180591
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Institution: Nanyang Technological University
Language: English
Description
Summary:This study introduces a novel approach using 3D Physics-Informed Neural Networks (PINNs) for simulating blood flow in coronary arteries, integrating deep learning with fundamental physics principles. By merging physics-driven models with clinical datasets, our methodology accurately predicts fractional flow reserve (FFR), addressing challenges in noninvasive measurements. Validation against CFD simulations and invasive FFR methods demonstrates the model’s accuracy and efficiency. The mean value error compared to invasive FFR was approximately 1.2% for CT209, 2.3% for CHN13, and 2.8% for artery CHN03. Compared to traditional 3D methods that struggle with boundary conditions, our 3D PINN approach provides a flexible, efficient, and physiologically sound solution. These results suggest that the 3D PINN approach yields reasonably accurate outcomes, positioning it as a reliable tool for diagnosing coronary artery conditions and advancing cardiovascular simulations.

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