Uncertainty quantification of wall shear stress in intracranial aneurysms using a data-driven statistical model of systemic blood flow variability

Adverse wall shear stress (WSS) patterns are known to play a key role in the localisation, formation, and progression of intracranial aneurysms (IAs). Com- plex region-specific and time-varying aneurysmal WSS patterns depend both on vascular morphology as well as on variable systemic ow conditions. Com- putational uid dynamics (CFD) has been proposed for characterising WSS patterns in IAs; however, CFD simulations often rely on deterministic bound- ary conditions that are not representative of the actual variations in blood ow. We develop a data-driven statistical model of internal carotid artery (ICA) ow, which is used to generate a virtual population of waveforms used as inlet bound- ary conditions in CFD simulations. This allows the statistics of the resulting aneurysmal WSS distributions to be computed. It is observed that ICA wave- form variations have limited in uence on the time-averaged WSS (TAWSS) on the IA surface. In contrast, in regions where the ow is locally highly multidi- rectional, WSS directionality and harmonic content are strongly affected by the ICA ow waveform. As a consequence, we argue that the effect of blood ow variability should be explicitly considered in CFD-based IA rupture assessment to prevent confounding the conclusions.