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 systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for haemodynamical flow models.
journal_name
R Soc Open Scijournal_title
Royal Society open scienceauthors
Larson K,Bowman C,Papadimitriou C,Koumoutsakos P,Matzavinos Adoi
10.1098/rsos.182229subject
Has Abstractpub_date
2019-10-16 00:00:00pages
182229issue
10issn
2054-5703pii
rsos182229journal_volume
6pub_type
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