Linking Aneurysmal Geometry and Hemodynamics Using Computational Fluid Dynamics

TL;DR

This study analyzes how aneurysm shape influences blood flow patterns using advanced CFD simulations on a large patient cohort, identifying geometric features as potential biomarkers for risk assessment.

Contribution

It introduces a comprehensive multiscale CFD framework linking aneurysm geometry with hemodynamics, providing large-scale quantitative analysis of AAA flow behavior.

Findings

Specific geometric features shape shear-stress patterns

Flow signatures could serve as biomarkers for risk assessment

Large CFD dataset enhances understanding of aneurysm hemodynamics

Abstract

The development and progression of abdominal aortic aneurysms (AAA) are related to complex flow patterns and wall-shear-driven mechanobiological stimuli, yet the quantitative relationship between aneurysmal geometry and hemodynamics remains poorly defined. In this study, we conducted a comprehensive hemodynamic analysis of 74 patient-specific abdominal aortas, representing one of the largest Computational Fluid Dynamics (CFD) cohorts reported to date. A multiscale framework coupling 0D-1D systemic circulation models with 3D stabilized finite-element simulations is used to generate physiologically consistent boundary conditions and high-fidelity flow fields. From each model, we extract Time Averaged Wall Shear Stress (TAWSS), Oscillatory Shear Index (OSI), Relative Residence Time (RRT) and Local Normalized Helicity (LNH) indicators alongside an extended set of geometric descriptors characterizing diameter, curvature and torsion. This study provides a clear and comprehensive view of how aneurysm shape influences blood-flow behavior, supported by one of the largest systematically analyzed CFD datasets of AAAs to date. Our results show that specific geometric features reliably shape shear-stress patterns, suggesting that these geometry-driven flow signatures could serve as valuable biomarkers for patient-specific risk assessment. Together, these insights highlight the potential of incorporating detailed geometric descriptors into future models that aim to predict AAA growth and rupture.