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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sg-ntu-dr.10356-1805912024-10-19T16:48:50Z Three-dimensional physics-informed neural network simulation in coronary artery trees Alzhanov, Nursultan Ng, Eddie Yin Kwee Zhao, Yong School of Mechanical and Aerospace Engineering Engineering Blood flow simulation Coronal stenosis 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. Published version The research was supported by the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant #1 number AP19678197 and grant #2 number OPCRP2022006. 2024-10-14T05:40:24Z 2024-10-14T05:40:24Z 2024 Journal Article Alzhanov, N., Ng, E. Y. K. & Zhao, Y. (2024). Three-dimensional physics-informed neural network simulation in coronary artery trees. Fluids, 9(7), 153-. https://dx.doi.org/10.3390/fluids9070153 2311-5521 https://hdl.handle.net/10356/180591 10.3390/fluids9070153 2-s2.0-85199873116 7 9 153 en Fluids © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
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Engineering Blood flow simulation Coronal stenosis |
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Engineering Blood flow simulation Coronal stenosis Alzhanov, Nursultan Ng, Eddie Yin Kwee Zhao, Yong Three-dimensional physics-informed neural network simulation in coronary artery trees |
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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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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Alzhanov, Nursultan Ng, Eddie Yin Kwee Zhao, Yong |
format |
Article |
author |
Alzhanov, Nursultan Ng, Eddie Yin Kwee Zhao, Yong |
author_sort |
Alzhanov, Nursultan |
title |
Three-dimensional physics-informed neural network simulation in coronary artery trees |
title_short |
Three-dimensional physics-informed neural network simulation in coronary artery trees |
title_full |
Three-dimensional physics-informed neural network simulation in coronary artery trees |
title_fullStr |
Three-dimensional physics-informed neural network simulation in coronary artery trees |
title_full_unstemmed |
Three-dimensional physics-informed neural network simulation in coronary artery trees |
title_sort |
three-dimensional physics-informed neural network simulation in coronary artery trees |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/180591 |
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1814777708835504128 |