Linking Aneurysmal Geometry and Hemodynamics Using Computational Fluid Dynamics

TL;DR

The study addresses the link between abdominal aortic aneurysm geometry and hemodynamics by applying a large-scale multiscale CFD workflow (0D-1D coupling with 3D simulations) to 74 patient-specific AAAs. It quantifies how geometric descriptors (diameter, curvature, torsion) relate to hemodynamic biomarkers (TAWSS, OSI, RRT) and helicity (LNH), uncovering strong geometry–flow associations, especially in the iliac region. Proximal neck size and sac diameter emerge as key determinants of disturbed flow and wall-stress patterns, with downstream iliac geometry significantly amplifying these effects. The findings support incorporating detailed geometric descriptors into rupture-growth risk assessment and highlight the potential of geometry-driven biomarkers for personalized AAA management.

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.

Figures (11)

• Figure 1: Schematic representation of the waveform, extracted from 0D-1D model, at the inlet and the $3$-element Windkessel RCR conditions at the outlet.
• Figure 2: Workflow for acquiring iliac RCR outlet parameters from flow waveform and pressure data.
• Figure 3: Velocity magnitude distributions in the six aortic aneurysm models (VAID3, VAID7, VAID53, T1-P8, T2-P4, and T2-P17) at three cardiac phases: T1: peak systole, T2: late systole, and T3: late diastole. The flow slices demonstrate the hemodynamic changes, highlighting high-velocity inflow jets at peak systole, progressively altering velocity distribution during late systole, and reduced flow behavior approaching at late diastole.
• Figure 4: Streamlines for the six aortic aneurysm models (VAID3, VAID7, VAID53, T1-P8, T2-P4, and T2-P17) at three cardiac phases: T1: peak systole, T2: late systole, and T3: late diastole. The streamlines demonstrate the hemodynamic changes, highlighting the recirculation regions and the creation of vortices inside the AAAs.
• Figure 5: Velocity slices for six selected AAA models from the dataset, at three cardiac-cycle times ($T1$: peak systole, $T2$: late systole, $T3$: late diastole). The slices were selected at the proximal neck $Y1$, the aneurysm sac $Y2$ and the distal aneurysmal neck $Y3$. The velocity units are in $cm / s$.