O1504 Modelling the effect of flow variability on intracranial aneurysms with Gaussian processes and lumped parameter models
Mr. Ali Sarrami-Foroushani, Dr. Toni Lassila, Prof. Alejandro F. Frangi
Centre for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, United Kingdom


Computational fluid dynamics models are increasingly used to simulate vascular flow. Patient-specific flow measurements are typically used to set flow boundary conditions that drive the simulations. When patient-specific flow measurements are unavailable, mean values of flow measurements across small cohorts are used as normative values. In reality, both the between-subjects and within-subject flow variability is large, and mean values across a cohort might not be indicative of flow in a particular individual. We highlight the importance of quantifying the uncertainty in CFD-based indicators, such as wall shear stress (WSS), due to the physiological variability of blood flow.

We develop data-driven statistical models for the between-subjects variability of internal carotid flow (Lassila et al. 2018). A log-linear mixed effects model describes the variability in the time-averaged flow rate, while a Gaussian process model describes the variability of the time-dependent flow fluctuations. The model parameters are identified from carotid ultrasound measurements in a cohort of 104 elderly volunteers. To model within-subject variability, we use a cerebral autoregulation model (Mader et al. 2015) to model the response of internal carotid flow to changes in heart rate and blood pressure. Our models provide: (i) cohort-specific boundary conditions to vascular CFD simulations in the case that patient-specific flow measurements are not available, and (ii) estimates of within-subject flow variability in cases where patient-specific flow measurements are available only as spot measurements.

The flow variability models are applied to study the effects of flow variability on WSS patterns in intracranial aneurysms. Different WSS-related indicators have been proposed for identifying aneurysms with high rupture risk. We have previously shown that two commonly used WSS-based indicators, oscillatory shear index (OSI) and transverse WSS (TransWSS), are sensitive to physiological flow variability (Sarrami-Foroushani et al. 2016), which may confound their interpretation. We further study the effects of exercise-induced variability of ICA flow on WSS-based indicators. While the cerebral autoregulation system holds the time-averaged flow rate relatively stable even under strenuous exercise, changes in the flow waveform are more dramatic and can lead to large uncertainty in WSS-based indicators.

Lassila T, et al. Population-specific flow modelling: between/within-subject variability in the internal carotid arteries of elderly volunteers. Ann. Biomed. Eng, submitted, 2018
Mader G, et al. Modeling cerebral blood flow velocity during orthostatic stress. Ann. Biomed. Eng. 43(8):1748-1758, 2015
Sarrami-Foroushani A, et al. Uncertainty quantification of wall shear stress in intracranial aneurysms using a data-driven statistical model of systemic blood flow variability. J. Biomech. 49 (16), 3815-3823, 2016