FEAorta: A Fully Automated Framework for Finite Element Analysis of the Aorta From 3D CT Images

2025-10-08

AI Summary

To address two major bottlenecks in thoracic aortic aneurysm (TAA) rupture risk assessment—time-consuming manual segmentation and computationally expensive finite element analysis (FEA)—this work proposes the first end-to-end deep neural network framework that directly generates patient-specific aortic finite element meshes from 3D CT volumes, enabling fully automated biomechanical modeling. The method integrates a PyTorch-based FEA library with static equilibrium constraints to fully automate FEA pre-processing and incorporates a physics-guided stress computation acceleration mechanism. Experimental results demonstrate that mesh generation and stress simulation are completed within seconds—achieving a 100× to 1,000× speedup over conventional workflows—thereby markedly improving clinical timeliness and scalability. This study establishes, for the first time, a fully automated, real-time closed-loop pipeline from CT imaging to biomechanical risk assessment, offering a novel paradigm for large-scale TAA screening and personalized intervention.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Segmentation
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the perspective of biomechanics, rupture occurs when the stress acting on the aortic wall exceeds the wall strength. Wall stress distribution can be obtained by computational biomechanical analyses, especially structural Finite Element Analysis. For risk assessment, probabilistic rupture risk of TAA can be calculated by comparing stress with material strength using a material failure model. Although these engineering tools are currently available for TAA rupture risk assessment on patient specific level, clinical adoption has been limited due to two major barriers: labor intensive 3D reconstruction current patient specific anatomical modeling still relies on manual segmentation, making it time consuming and difficult to scale to a large patient population, and computational burden traditional FEA simulations are resource intensive and incompatible with time sensitive clinical workflows. The second barrier was successfully overcome by our team through the development of the PyTorch FEA library and the FEA DNN integration framework. By incorporating the FEA functionalities within PyTorch FEA and applying the principle of static determinacy, we reduced the FEA based stress computation time to approximately three minutes per case. Moreover, by integrating DNN and FEA through the PyTorch FEA library, our approach further decreases the computation time to only a few seconds per case. This work focuses on overcoming the first barrier through the development of an end to end deep neural network capable of generating patient specific finite element meshes of the aorta directly from 3D CT images.

Problem

• Automating aortic finite element analysis from CT images
• Overcoming manual segmentation for patient-specific modeling
• Reducing computational burden in clinical aneurysm assessment

Innovation

• Automated aorta mesh generation from 3D CT
• Deep neural network enables end-to-end processing
• PyTorch integration accelerates finite element analysis