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

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.

💡 Research Summary

Thoracic aortic aneurysm (TAA) remains a leading cause of mortality because rupture occurs when the aortic wall can no longer withstand hemodynamic pressure. While three‑dimensional computed tomography (3D CT) is the clinical gold standard for detecting TAA, imaging alone does not provide the mechanical stresses that ultimately dictate rupture risk. This paper presents a fully integrated, patient‑specific computational pipeline that transforms raw CT data into high‑fidelity finite‑element (FE) simulations, thereby enabling quantitative risk assessment.

The pipeline begins with automated image segmentation. The authors trained a 3‑dimensional U‑Net variant on a curated dataset of annotated CT scans, incorporating Hounsfield‑unit normalization, multi‑scale feature fusion, and extensive data augmentation. The resulting voxel‑wise masks for the aortic lumen, wall, and surrounding structures achieve a Dice coefficient of 0.94, even in regions of subtle pathology.

Next, the voxel masks are converted into a structured hexahedral mesh rather than the more common triangular surface mesh. Using an octree‑based adaptive refinement strategy, the authors generate fine elements (≈ 0.3 mm) in the aneurysmal region and coarser elements (≈ 0.8 mm) elsewhere. This approach reduces total element count by roughly 30 % while maintaining mesh quality metrics (aspect ratio < 1.5, skewness < 0.2). Hexahedral elements are chosen because they provide superior numerical stability for large‑deformation, thin‑wall problems typical of the aorta.

The FE analysis employs the Holzapfel‑Gasser‑Ogden (HGO) anisotropic hyperelastic model to capture the layered, fiber‑reinforced nature of the aortic wall. Patient‑specific systolic and diastolic blood pressures are imposed as cyclic internal pressure loads, and shear stresses derived from computational fluid dynamics (CFD) simulations are mapped onto the wall as additional surface tractions. A non‑linear static‑dynamic solver iterates until the energy residual falls below 10⁻⁶, delivering distributions of circumferential stress, shear stress, and strain‑energy density.

Risk quantification is performed by correlating these mechanical fields with clinical outcomes. In a cohort of 20 patients (including 7 documented ruptures), the authors built a logistic regression model using peak wall stress, maximum strain‑energy density, and wall thickness as predictors. Compared with the conventional diameter threshold (> 5.5 cm), the stress‑based model achieved an area under the ROC curve of 0.92, sensitivity of 0.88, and specificity of 0.85, demonstrating markedly improved discriminative power.

A key contribution of the work is the end‑to‑end automation of the entire workflow. Image preprocessing, deep‑learning segmentation, mesh generation, quality verification, FE solving, and risk metric extraction are orchestrated by a series of scripts, reducing the total processing time per patient to under three hours. This speed, combined with the high fidelity of the mechanical simulation, makes the pipeline feasible for clinical decision support, where rapid, patient‑specific risk estimates are essential for planning surgical or endovascular interventions.

In summary, the authors deliver a comprehensive computational framework that bridges medical imaging and biomechanical simulation for TAA. By integrating state‑of‑the‑art deep‑learning segmentation, adaptive hexahedral meshing, and anisotropic hyperelastic FE analysis, the pipeline provides quantitative, patient‑specific rupture risk metrics that outperform traditional diameter‑based assessments. The study lays the groundwork for larger multi‑center validations and for incorporation of additional physiological factors (e.g., pulsatile flow, wall remodeling) to further enhance personalized treatment planning.