抄録
The present study presents a data-driven generation of synthetic cerebral aneurysm geometries for systematic computational hemodynamic simulations. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component analysis (PCA) was then applied to the aligned point-cloud data to quantify morphological variability and parameterize shape deformation. The first four principal components captured over 90% of the total variance; however, higher-order components were required to capture the detailed geometrical features of the original geometries. Computational fluid dynamic (CFD) simulations were performed on the PCA-based synthetic geometries under a cavity-flow-type velocity boundary condition to investigate the influence of shape variations on intra-aneurysmal flow patterns, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). Independent variation of first and second principal components (PCS1 and PCS2) indicated that PCS1 primarily modulates global aneurysm aspect ratios (depth-width-height), whereas PCS2 mainly controls local surface morphology, including wrinkles and bump-like features. CFD results showed that the circulation pattern was dominated by PCS1, with higher PCS1 leading to increased TAWSS and a non-monotonic change in mean OSI that reached a minimum for near-isotropic geometries, while higher PCS2 tended to locally elevate OSI in wrinkled/bump regions. Collectively, these findings indicate that PCA-based shape parameterization provides a practical approach for generating synthetic aneurysm datasets and systematically assessing how specific morphological features govern hemodynamic behavior. The proposed approach is expected to contribute to the future development of surrogate modeling and data-driven hemodynamic prediction.
