ArticleQuantitative imaging in medicine and surgery2026
Local pressure as a dominant hemodynamic driver of wall enhancement in anterior communicating artery aneurysms: a facet-level computational fluid dynamics and vessel wall imaging analysis.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Size-based risk stratification often overlooks small but unstable intracranial aneurysms (IAs). Aneurysm wall enhancement (AWE) on vessel wall imaging (VWI) is a validated marker of wall instability, yet the local hemodynamic drivers of this pathology, particularly in complex anterior communicating artery (ACoA) aneurysms, remain incompletely characterized. This study leverages a combined computational fluid dynamics (CFD)-VWI approach to characterize the mechanobiological coupling between local hemodynamics and quantitative wall remodeling in ACoA aneurysms. Methods: We retrospectively analyzed 24 patients harboring 25 ACoA aneurysms. A Vector-Integrated Surface Parametrization (VISP) pipeline achieved sub-voxel sampling density [through adaptive interpolation rather than imaging resolution beyond the native 0.6 mm magnetic resonance imaging (MRI) voxel] for co-registration of CFD and 3T-VWI, with wall enhancement defined at a contrast ratio (CR) ≥0.6. To identify hemodynamic drivers of enhanced wall thickness (EWT) while explicitly accounting for within-patient hierarchical clustering, four complementary analytical frameworks were applied in parallel: (I) intra-patient paired bootstrap tests (2,000 resamples) comparing enhanced and non-enhanced wall segments within each of the 13 AWE-positive patients; (II) a multivariate linear mixed model (LMM) with patient-level random intercepts for EWT severity (n=12,473 enhanced segments); (III) generalized estimating equations (GEEs) with cluster-robust variance for AWE presence (n=157,284 segments); and (IV) ensemble machine-learning models (Random Forest and XGBoost) interpreted via Shapley Additive exPlanations (SHAP) values across segment-level, patient-centered, and patient-level GroupKFold cross-validation (CV). Cross-patient generalization of EWT prediction was disclosed separately as an out-of-sample analysis. Results: Focal AWE was identified in 14 of 25 aneurysms (13 patients), spatially coinciding with hemodynamic stagnation zones. Enhanced segments exhibited significantly lower local Pressure Conclusions: We present a facet-level CFD-VWI pipeline that achieves sub-voxel sampling density for spatially mapping local hemodynamics onto quantitative wall remodeling in ACoA aneurysms on a clinical 3T platform. Across four independent hierarchical analyses, local Pressure
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