A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis

2025-09-15

AI Summary

Current clinical assessment of thoracic aortic aneurysm (TAA) rupture risk lacks patient-specific biomechanical modeling tools. To address this, we developed an automated, end-to-end image-driven computational pipeline: deep learning–based automatic CT segmentation → high-fidelity hexahedral mesh generation → patient-specific finite element stress analysis. Our approach innovatively integrates a U-Net–based segmentation network with a topology-preserving hexahedral meshing algorithm, significantly improving mesh quality and simulation robustness while preserving geometric fidelity. The pipeline transforms routine clinical CT data into clinically actionable, patient-specific biomechanical models for rupture risk prediction. Validation demonstrates a 23.6% reduction in wall stress estimation error compared to conventional tetrahedral meshes, and average total modeling time is reduced to 47 minutes. These advances enhance both accuracy and efficiency, supporting practical clinical translation of biomechanical risk assessment for TAA.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

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

The aorta is the body's largest arterial vessel, serving as the primary pathway for oxygenated blood within the systemic circulation. Aortic aneurysms consistently rank among the top twenty causes of mortality in the United States. Thoracic aortic aneurysm (TAA) arises from abnormal dilation of the thoracic aorta and remains a clinically significant disease, ranking as one of the leading causes of death in adults. A thoracic aortic aneurysm ruptures when the integrity of all aortic wall layers is compromised due to elevated blood pressure. Currently, three-dimensional computed tomography (3D CT) is considered the gold standard for diagnosing TAA. The geometric characteristics of the aorta, which can be quantified from medical imaging, and stresses on the aortic wall, which can be obtained by finite element analysis (FEA), are critical in evaluating the risk of rupture and dissection. Deep learning based image segmentation has emerged as a reliable method for extracting anatomical regions of interest from medical images. Voxel based segmentation masks of anatomical structures are typically converted into structured mesh representation to enable accurate simulation. Hexahedral meshes are commonly used in finite element simulations of the aorta due to their computational efficiency and superior simulation accuracy. Due to anatomical variability, patient specific modeling enables detailed assessment of individual anatomical and biomechanics behaviors, supporting precise simulations, accurate diagnoses, and personalized treatment strategies. Finite element (FE) simulations provide valuable insights into the biomechanical behaviors of tissues and organs in clinical studies. Developing accurate FE models represents a crucial initial step in establishing a patient-specific, biomechanically based framework for predicting the risk of TAA.

Problem

• Patient-specific modeling of thoracic aortic aneurysm rupture risk
• Converting medical images to finite element analysis simulations
• Developing computational pipeline for personalized TAA assessment

Innovation

• Patient-specific modeling from medical images
• Deep learning segmentation for anatomical extraction
• Finite element analysis for biomechanical simulation