Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910913248886784 |
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| author | D'Inverno, Giuseppe Alessio Moradizadeh, Saeid Salavatidezfouli, Sajad Africa, Pasquale Claudio Rozza, Gianluigi |
| author_facet | D'Inverno, Giuseppe Alessio Moradizadeh, Saeid Salavatidezfouli, Sajad Africa, Pasquale Claudio Rozza, Gianluigi |
| contents | The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysics numerical models are crucial. On one side, Full Order Models (FOMs) deliver accurate hemodynamic assessments, but their high computational demands hinder their real-time clinical application. In contrast, Reduced Order Models (ROMs) provide more efficient yet accurate solutions, essential for personalized healthcare and timely clinical decision-making. In this work, we explore the application of computational fluid dynamics (CFD) in cardiovascular medicine by integrating FOMs with ROMs for predicting the risk of aortic aneurysm growth and rupture. Wall Shear Stress (WSS) and the Oscillatory Shear Index (OSI), sampled at different growth stages of the thoracic aortic aneurysm, are predicted by means of Graph Neural Networks (GNNs). GNNs exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization, taking into account the spatial local information, regardless of the dimension of the input graph. Our experimental validation framework yields promising results, confirming our method as a valid alternative that overcomes the curse of dimensionality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03802 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction D'Inverno, Giuseppe Alessio Moradizadeh, Saeid Salavatidezfouli, Sajad Africa, Pasquale Claudio Rozza, Gianluigi Medical Physics Machine Learning Numerical Analysis 65M08, 68T07, 92B20, 76-10, 76Z05, 92C50 The complexity of the cardiovascular system needs to be accurately reproduced in order to promptly acknowledge health conditions; to this aim, advanced multifidelity and multiphysics numerical models are crucial. On one side, Full Order Models (FOMs) deliver accurate hemodynamic assessments, but their high computational demands hinder their real-time clinical application. In contrast, Reduced Order Models (ROMs) provide more efficient yet accurate solutions, essential for personalized healthcare and timely clinical decision-making. In this work, we explore the application of computational fluid dynamics (CFD) in cardiovascular medicine by integrating FOMs with ROMs for predicting the risk of aortic aneurysm growth and rupture. Wall Shear Stress (WSS) and the Oscillatory Shear Index (OSI), sampled at different growth stages of the thoracic aortic aneurysm, are predicted by means of Graph Neural Networks (GNNs). GNNs exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization, taking into account the spatial local information, regardless of the dimension of the input graph. Our experimental validation framework yields promising results, confirming our method as a valid alternative that overcomes the curse of dimensionality. |
| title | Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction |
| topic | Medical Physics Machine Learning Numerical Analysis 65M08, 68T07, 92B20, 76-10, 76Z05, 92C50 |
| url | https://arxiv.org/abs/2410.03802 |