Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction

• URL: http://arxiv.org/abs/2410.03802v1
• Date: Fri, 4 Oct 2024 09:39:15 GMT
• Title: Mesh-Informed Reduced Order Models for Aneurysm Rupture Risk Prediction
• Authors: Giuseppe Alessio D'Inverno, Saeid Moradizadeh, Sajad Salavatidezfouli, Pasquale Claudio Africa, Gianluigi Rozza,
• Abstract summary: Graph Neural Networks (GNNs) exploit the natural graph structure of the mesh obtained by the Finite Volume (FV) discretization. Our experimental validation framework yields promising results, confirming our method as a valid alternative that overcomes the curse of dimensionality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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, 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 abdominal 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.

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