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

Thoracic aortic aneurysm (TAA) represents a critical cardiovascular challenge, characterized by the silent, progressive dilation of the aorta that can lead to catastrophic rupture or dissection. Despite the significant clinical burden of TAA, current clinical risk stratification relies heavily on maximum diameter measurements derived from three-dimensional computed tomography (3D CT), a metric that often fails to capture the complex biomechanical environment precipitating failure. To address this, we developed a comprehensive computational pipeline for patient-specific modeling of thoracic aortic aneurysms, combining segmentation, mesh generation, and finite element simulation. A key innovation of this pipeline is the open-arch mesh with node and element correspondence among different patients, enabled by the template-fitting based meshing method, which preserves anatomical accuracy, enables statistical shape modeling, and facilitates FE simulations with all-hexahedral elements in PyTorch-FEA -capabilities not addressed in previous studies. Our pipeline captures clinically relevant biomechanical differences, showing that aneurysmal patients exhibit significantly higher maximum absolute principal stress compared to non-aneurysmal patients. This result identifies a quantitative biomechanical marker that extends beyond conventional geometric measurements and potentially aids in TAA diagnosis. Moreover, the pipeline quantifies stress distribution across different regions of the aorta, enabling more detailed biomechanical assessment and supporting personalized treatment planning. By streamlining the transition from routine imaging to advanced stress analysis, this pipeline facilitates large-scale population studies and paves the way for the integration of patient-specific biomechanical parameters into routine clinical decision-making.