Abstract
Computational fluid dynamics models are increasingly proposed for assisting the diagnosis and management of vascular diseases. Ideally, patient-specific flow measurements are used to impose flow boundary conditions. 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 variabilities are large. Consequently, neither one-shot flow measurements nor mean values across a cohort are truly indicative of the flow regime in a given person. We develop models for both the between-subjects and within-subject variability of internal carotid flow. A log-linear mixed effects model is combined with a Gaussian process to model the between-subjects flow variability, while a lumped parameter model of cerebral autoregulation is used to model the within-subject flow variability in response to heart rate and blood pressure changes. The model parameters are identified from carotid ultrasound measurements in a cohort of 103 elderly volunteers. We use the models to study intracranial aneurysm flow in 54 subjects under rest and exercise and conclude that OSI, a common wall shear-stress derived quantity in vascular CFD studies, may be too sensitive to flow fluctuations to be a reliable biomarker.
Original language | English |
---|---|
Article number | e3271 |
Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | International Journal for Numerical Methods in Biomedical Engineering |
Volume | 36 |
Issue number | 1 |
Early online date | 5 Nov 2019 |
DOIs | |
Publication status | Published - 27 Jan 2020 |
Keywords
- cerebrovascular disease
- computational fluid dynamics
- Gaussian process models
- patient-specific models
- uncertainty quantification