Background: Hemodynamics during the growth process of cerebral aneurysms are incompletely understood. We developed a novel fluid–structure interaction analysis method for the identification of relevant scenarios of aneurysm onset. Method: This method integrates both fluid dynamics and structural mechanics, as well as their mutual interaction, for a comprehensive analysis. Patients with a single unruptured cerebral aneurysm were included. Results: Overall, three scenarios were identified. In scenario A, wall shear stress (WSS) was low, and the oscillatory shear index (OSI) was high in large areas within the region of aneurysm onset (RAO). In scenario B, the quantities indicated a reversed behavior, where WSS was high and OSI was low. In the last scenario C, a behavior in-between was found, with scenarios A and B coexisting simultaneously in the RAO. Structural mechanics demonstrated a similar but independent trend. Further, we analyzed the change in hemodynamics between the onset and a fully developed aneurysm. While scenarios A and C remained unchanged during aneurysm growth, 47% of aneurysms in scenario B changed into scenario A and 20% into scenario C. Conclusions: In conclusion, these findings suggest that WSS and the OSI are reciprocally regulated, and both low and high WSS/OSI conditions can lead to aneurysm onset.

This study investigates morphological, hemodynamic and structural mechanical factors influencing aneurysm rupture behavior, specifically focusing on Middle Cerebral Artery (MCA) and Anterior Communicating Artery (ACOMM) aneurysms, using computational fluid dynamics (CFD) and fluid-structure interaction (FSI) analyses. Data from 116 patients with aneurysms located in the MCA and ACOMM, including 81 ruptured and 35 stable aneurysms. Aneurysms were modeled with FSI methods to examine key parameters: aneurysm angle, wall shear stress, and equivalent Mises stress as well as additional parameters. Statistical analyses were conducted using the Mann-Whitney U-test to identify significant differences in these parameters between the two locations in stable and ruptured aneurysms. In a further step univariate and multivariate regression analysis was performed. Results indicated no significant differences in the assessed parameters between ruptured MCA and ACOMM aneurysms. However, stable ACOMM aneurysms exhibited a larger aneurysm angle, lower wall equivalent stress as well as lower ratio of maximum WSS in the aneurysms and the parent vessel when compared to MCA aneurysms, indicating that ACOMM aneurysms may rupture at lower stresses possibly due to their higher aneurysm angle. Regression analysis underlines the statistical power of aneurysm angle. These findings highlight morphological and hemodynamic differences in stable aneurysms across MCA and ACOMM locations. Stable MCA aneurysms appear to tolerate higher wall stress values compared to ACOMM aneurysms, leading to greater susceptibility to rupture in ACOMM aneurysms at stress levels where MCA aneurysms can remain stable. This study underscores the importance of considering morphological, hemodynamic and structural mechanical factors as well as the distinction between stable and ruptured aneurysms, to improve the accuracy of rupture risk.

Volume 10, published for JBEB Journal.

Introduction: The Anterior Communicating Artery complex (AComA) is one of the most common intracranial aneurysms locations. Accurate rupture risk assessment in patients with cerebral aneurysms is essential for optimizing treatment decisions. Computational fluid dynamics has significantly advanced insight into aneurysmal hemodynamics. Many studies concentrate predominantly on blood flow patterns, frequently neglecting the biomechanical properties of the aneurysm wall. Fluid-structure interaction analysis combines hemodynamic behavior with wall mechanics, potentially facilitating a more thorough evaluation of rupture risk assessment. Methods: In this study, we employed advanced techniques to investigate several single and composite parameters to predict the rupture risk of AComA aneurysms in a cohort of 150 patients treated at the Kepler University Hospital in Linz, Austria. For this reason, clinical, morphological, hemodynamic, and structural mechanical parameters were assessed. Results: A subsequent workflow analysis, consisting of comparative analysis, collinearity analysis, predictive modeling, composite parameter, performance evaluation, and internal threshold validation, revealed the Gaussian curvature GLN (AUC = 0.91) with a sensitivity of 0.93 and specificity of 0.83 as a best-performing single parameter for aneurysm rupture prediction. Composite parameters like WGD (combination of wall shear stress, Gaussian curvature, and wall displacement) achieved an AUC of 0.89, and WG (combination of wall shear stress and Gaussian curvature) an AUC of 0.88. An internal validation with 25 independent ruptured aneurysms was performed, and the previous results were confirmed with very high sensitivity values of 0.92 for GLN. Conclusion: Our findings indicate that the investigated morphological, hemodynamic, and structural, mechanical parameters could provide a potential tool for evaluating rupture risk for AComA aneurysms. The single morphological parameter GLN offers, followed by composite parameters WGD and WG, excellent prediction power for the aneurysm rupture state, as confirmed by internal validation. Further studies are warranted to evaluate the prospective clinical application of these parameters.

Patient‐specific modeling of vessel wall thickness remains a key challenge in Fluid–Structure Interaction (FSI) simulations of cerebral aneurysms. To address this gap, the authors introduce the “aneurysm‐OSI‐band” (AOB)—derived from the Oscillatory Shear Index (OSI)—as a marker of localized wall thickness. Moreover, this study provides a direct comparison of OSI calculations against intraoperative imaging, offering confirmation of the AOB approach. In addition, FSI convergence is improved with an optimized residual method. A total of 25 Middle Cerebral Artery (MCA) aneurysms treated at a single institution between 2016 and 2020 were retrospectively analyzed. Digital subtraction angiography (DSA) data were used for FSI simulations and OSI calculation. The resulting OSI maps were systematically compared with intraoperative images, wherein thin‐ and thick‐walled regions were identified macroscopically. This correlation enabled statistical comparison and assessment of the accuracy of the AOB in localizing distinct wall‐thickness profiles. Defining the AOB based on OSI values revealed a statistically significant distinction between thin‐ and thick‐walled aneurysm segments. Quantitative analysis demonstrated robust concordance between simulation‐derived OSI patterns and intraoperative observations, highlighting OSI value differences between thin and thick-walled areas. The introduction of the AOB provides a method for assessing patient‐specific wall thickness and determining rupture status. These findings underscore the potential of advanced computational modeling to improve personalized aneurysm management and enhance clinical outcomes.