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Patient-specific computational modeling is a popular, non-invasive method toanswer medical questions. Medical images are used to extract geometric domainsnecessary to create these models, providing a predictive tool for clinicians.However, in vivo imaging is subject to uncertainty, impacting vessel dimensionsessential to the mathematical modeling process. While there are numerousprograms available to provide information about vessel length, radii, andposition, there is currently no exact way to determine and calibrate thesefeatures. This raises the question, if we are building patient-specific modelsbased on uncertain measurements, how accurate are the geometries we extract andhow can we best represent a patient's vasculature? In this study, we develop anovel framework to determine vessel dimensions using change points. We explorethe impact of uncertainty in the network extraction process on hemodynamics byvarying vessel dimensions and segmenting the same images multiple times. Ouranalyses reveal that image segmentation, network size, and minor changes inradius and length have significant impacts on pressure and flow dynamics inrapidly branching structures and tapering vessels. Accordingly, we concludethat it is critical to understand how uncertainty in network geometrypropagates to fluid dynamics, especially in clinical applications.

Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images