AnXplore: a comprehensive fluid-structure interaction study of 101 intracranial aneurysms

Abstract

Advances in computational fluid dynamics continuously extend the comprehension of aneurysm growth and rupture, intending to assist physicians in devising effective treatment strategies. While most studies have first modelled intracranial aneurysm walls as fully rigid with a focus on understanding blood flow characteristics, some researchers further introduced Fluid-Structure Interaction (FSI) and reported notable haemodynamic alterations for a few aneurysm cases when considering wall compliance. In this work, we explore further this research direction by studying 101 intracranial sidewall aneurysms, emphasizing the differences between rigid and deformable-wall simulations. The proposed dataset along with simulation parameters are shared for the sake of reproducibility. A wide range of haemodynamic patterns has been statistically analyzed with a particular focus on the impact of the wall modelling choice. Notable deviations in flow characteristics and commonly employed risk indicators are reported, particularly with near-dome blood recirculations being significantly impacted by the pulsating dynamics of the walls. This leads to substantial fluctuations in the sac-averaged oscillatory shear index, ranging from -36% to +674% of the standard rigid-wall value. Going a step further, haemodynamics obtained when simulating a flow-diverter stent modelled in conjunction with FSI are showcased for the first time, revealing a 73% increase in systolic sac-average velocity for the compliant-wall setting compared to its rigid counterpart. This last finding demonstrates the decisive impact that FSI modelling can have in predicting treatment outcomes.

Keywords: arterial wall tissue modelling; fluid-structure interaction; haemodynamics; intracranial aneurysm; open-source dataset.

FIGURE 1

2D cut of the basic geometry used for building the dataset. Dimensions are given in mm.

FIGURE 2

AnXplore generation pipeline. (A) The bulge is extracted from the original IntrA (Yang et al., 2020) mesh. (B) A PCA of the neck points is implemented and (C) the bulge is rotated/scaled to mount it on the toroidal artery. After Nearest Neighbors (N.N.) nodes are connected, the surface is remeshed (D) to obtain a homogeneous mesh size. (E) Lastly, volume meshes are generated.

FIGURE 3

Overview of the applied boundary conditions. The inflow waveform has been adapted from Ford et al. (2005). The red region corresponds to the interchangeable bulge area changing between cases.

FIGURE 4

The AnXplore dataset. Cases are indexed from 0 to 100. The dataset is available on GitHub.

FIGURE 5

IntrA →  AnXplore. Transformation examples following the pipeline described in Figure 2.

FIGURE 6

Geometrical descriptors of the investigated bulge geometries. Aneurysm diameter is computed from the bulge volume-equivalent sphere. The angle α is formed by the mean aneurysm dome direction with the vertical axis. The aspect ratio and Non-Sphericity Index (NSI) are computed as defined in the work of Dhar et al. (2008).

FIGURE 7

Overview of the simulated haemodynamics for three different aneurysms using rigid walls. (A) Systolic velocity streamlines colour-coded with the vertical component (B) TAWSS distribution with emphasis on extreme values as reported by Malek (1999); Meng et al. (2014) (C) OSI distribution.

FIGURE 8

(A) Evolution of the surface-averaged TAWSS and OSI plotted as a density map. Indices are displayed, with red ones corresponding to cases detailed in other figures. Note that the scope of the plot excludes some points for the sake of readability. (B) Evolution of several haemodynamic metrics. Variations are expressed as relative changes as defined by Eq. 6. Overlined indicators are spatially averaged over the bulge surface. The velocity v is averaged in space over the bulge volume and in time over the second cardiac cycle. The remaining quantities of interest are the KER: Kinetic Energy Ratio, VDR: Viscous Dissipation Ratio, (H/L)SA: High/Low Shear Area, ICI: Inflow Concentration Index, and SCI: Stress Concentration Index. These indicators are also averaged in time. Definitions for them can be found in Mut et al. (2011). (C) Spearman correlation matrix of the flow alteration with geometrical descriptors of the aneurysms as defined in Figure 6.

FIGURE 9

Evolution of haemodynamics moving from rigid-wall modelling to complete FSI simulations. Velocity streamlines are displayed both using rigid-wall CFD and the proposed FSI framework. High OSI regions are flagged at the surface of the bulge. Case indices are provided in the bottom row.

FIGURE 10

View of the mesh for case 71 with an implanted flow diverter. Low-opacity wires are not considered in the simulations. A zoom on the stent is displayed on the right. The mesh refinement at the wires goes down to h =15 μm.

FIGURE 11

Intra-saccular haemodynamics for case 71 treated with a flow diverter. Velocity streamlines are displayed at diastole (t ₆=1.8 s, (A) rigid, (B) compliant) and systole (t ₂=1.09 s, (C) rigid, (D) compliant), revealing two different flow patterns. Then, the change in OSI between the rigid (E) and deformable-wall (F) configurations is displayed. Lastly, (G) shows the systolic displacement of the fluid-solid interface.

FIGURE 12

Haemodynamic alteration in case 71 treated with a flow diverter when moving from rigid to compliant wall modelling. The upward velocity component is shown at the slice defined by y =10 mm. t ₂ corresponds to peak systole.