Poster Q3 - Characterizing Local Hemodynamics and Wall Features in Intracranial Aneurysms Walls

Introduction: Intracranial aneurysms (IAs) pose a critical dilemma in neurovascular medicine, compelling clinicians and patients to make complex decisions about treating or monitoring these conditions. While various demographic, and patient-specific risk factors guide these decisions, the detailed biological and mechanical processes driving changes in aneurysm walls, such as remodeling and degradation, are not fully understood. Current research has extensively investigated global hemodynamics, particularly wall shear stress (WSS), yet the specific impact of localized hemodynamic conditions on wall features—like “blebs”, “growth (enlargement)”, and “rupture”—remains elusive. This gap underscores the need for deeper exploration into how localized flow dynamics interact with biological mechanisms to influence aneurysm progression and rupture.

Our research introduces a novel methodology that characterizes local hemodynamics within specific subregions of the aneurysm wall. By systematically analyzing the distribution of critical wall features and correlating them with localized flow dynamics, we aim to elucidate the flow-induced mechanisms that drive aneurysm behavior. While the analysis was implemented in all available IA data, the results of the presented work specifically examine anterior communicating artery (ACom) and middle cerebral artery (MCA) aneurysms, which differ significantly in stability and rupture risk. ACom aneurysms are generally smaller, less stable, and more prone to rupture, possibly due to complex flow dynamics at the junction of communicating arteries. In contrast, MCA aneurysms are influenced by the stresses at bifurcation points. We hypothesize this detailed mapping and analysis will establish a benchmark for understanding the interplay between hemodynamic forces and aneurysm wall responses, ultimately guiding more tailored intervention strategies.

Materials and

Methods: Our study integrated data from one IA surgical database of 359 IAs treated with clipping and a longitudinal database tracking 67 growing IAs. Aneurysms were reconstructed into 3D patient-specific vascular models from 3D rotational angiography (3DRA) and CT angiography (CTA) images for computational fluid dynamics (CFD) simulations to assess hemodynamics within the aneurysm wall [1]. Using these models and intra-operative videos from the databases, we identified wall features’ regions by aligning follow-up models with baseline scans to pinpoint 88 growth regions (88), identified by displacements exceeding 0.5 mm; and interactively marked blebs (31 thin and 28 thick), and rupture sites (31) (Fig 1A-F).

To analyze and compare local flow environments, we implemented novel methods where the aneurysm surface was subdivided into "saccular," "streamwise," and "topographical" regions. Each subdivision involved detailed classifications based on anatomical and flow-related characteristics, such as geodesic distance from the aneurysm neck to dome or flow direction along streamlines and topographical identifiers like blebs or parent aneurysm segments (Fig 2A-C). The hemodynamic environment at each aneurysm wall segment of 359 IAs was characterized by computing variables like WSS metrics, oscillatory shear index (OSI), and relative residence time (RRT). We also identified critical points in the WSS vector field, tracking their influence over the cardiac cycle using a critical point influence field calculated by Gaussian convolution (Fig 2D-F). We analyzed distributions of hemodynamics and wall features in specifically ACom and MCA aneurysm wall subdivisions using the Kruskal-Wallis and Mann-Whitney tests for statistical significance (p < 0.05).

Results, Conclusions, and Discussions: Flow and Wall Features Distribution in Subregions:

Dome region exhibits lower WSS and longer RRT, particularly in MCA. Body region shows low WSS and gradients but higher OSI, indicating oscillatory flow. Necks faces highest WSS and gradients with significant impingement. Thin blebs are mostly in the body and dome for ACom and MCA. Thick blebs are consistent across subregions. AComs primarily grow in the body; MCAs in the dome and body. Ruptures mainly occur at the dome, with more body ruptures in AComs. Central region has slow flows, lowest WSS and gradients, highest OSI, longest RRT, and critical vortex points. The inflow region sees the highest WSS and gradients, with many saddle points showing shear flow. Outflow region has milder flow with notable positive WSS divergence, indicating secondary impingement. Both thin and thick blebs dominate the central region, with thin more frequent in inflow. AComs grow mainly in inflow and central regions, minimally in outflow. MCAs grow more centrally, less in inflow and outflow. AComs have balanced rupture distribution between inflow and central; MCAs favor central then inflow. Bleb regions show higher WSS and WSS gradients, hosting many critical points, including vortex ends; these are common rupture sites, particularly in MCA aneurysms. Transition zones feature high OSI, significant WSS divergence, and prolonged RRT, with common rupture sites, especially in ACom aneurysms.

Implications and

Conclusion:

Study findings suggest two distinct pathways for aneurysm evolution influenced by localized hemodynamics. Low WSS and oscillatory flow primarily contribute to blebs and wall remodeling, common in the MCA body and central regions. Conversely, high WSS and flow impingement promote wall thinning and rupture, notably in ACom inflow regions. These mechanisms—vascular remodeling in low-flow areas and structural degradation in high-flow zones—highlight divergent growth and rupture risks in ACom and MCA aneurysms. However, the absence of direct causal links between specific hemodynamic conditions and aneurysm response emphasizes the need for further research. This study underscores the complexity of aneurysm pathology, advocating for patient-specific diagnostic and therapeutic strategies that consider unique hemodynamic profiles and anatomical features.

References:
[1] J. R. Cebral et al., Ann Biomed Eng, Dec. 2016.

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