Detection of arterial wall abnormalities via Bayesian model selection.

ABSTRACT: Patient-specific modelling of haemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and

SUBMITTER: Larson K 

PROVIDER: S-EPMC6837237 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature