Uncertainty quantification in subject‐specific estimation of local vessel mechanical properties

Quantitative estimation of local mechanical properties remains critically important in the ongoing effort to elucidate how blood vessels establish, maintain, or lose mechanical homeostasis. Recent advances based on panoramic digital image correlation (pDIC) have made high‐fidelity 3D reconstructions...

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Bibliographic Details
Published in:International journal for numerical methods in biomedical engineering Vol. 37; no. 12; pp. e3535 - n/a
Main Authors: Rego, Bruno V., Weiss, Dar, Bersi, Matthew R., Humphrey, Jay D.
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2021
Wiley Subscription Services, Inc
Subjects:
Animal models
Animals
Aorta
Bayes Theorem
Bayesian analysis
Biomechanical Phenomena
Biomechanics
Blood vessels
Constitutive models
digital image correlation
Digital imaging
Empirical analysis
Homeostasis
image‐based modeling
Mathematical models
Mechanical properties
Mice
Modelling
Parameter uncertainty
Statistical analysis
Stiffness
Stress, Mechanical
subject‐specific model
Thorax
Uncertainty
uncertainty quantification
Workflow
uncertainty quantification
subject-specific model
image-based modeling
digital image correlation
ISSN:2040-7939, 2040-7947, 2040-7947
Online Access:Get full text
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Summary:Quantitative estimation of local mechanical properties remains critically important in the ongoing effort to elucidate how blood vessels establish, maintain, or lose mechanical homeostasis. Recent advances based on panoramic digital image correlation (pDIC) have made high‐fidelity 3D reconstructions of small‐animal (e.g., murine) vessels possible when imaged in a variety of quasi‐statically loaded configurations. While we have previously developed and validated inverse modeling approaches to translate pDIC‐measured surface deformations into biomechanical metrics of interest, our workflow did not heretofore include a methodology to quantify uncertainties associated with local point estimates of mechanical properties. This limitation has compromised our ability to infer biomechanical properties on a subject‐specific basis, such as whether stiffness differs significantly between multiple material locations on the same vessel or whether stiffness differs significantly between multiple vessels at a corresponding material location. In the present study, we have integrated a novel uncertainty quantification and propagation pipeline within our inverse modeling approach, relying on empirical and analytic Bayesian techniques. To demonstrate the approach, we present illustrative results for the ascending thoracic aorta from three mouse models, quantifying uncertainties in constitutive model parameters as well as circumferential and axial tangent stiffness. Our extended workflow not only allows parameter uncertainties to be systematically reported, but also facilitates both subject‐specific and group‐level statistical analyses of the mechanics of the vessel wall. Quantitative estimation of local mechanical properties remains critically important in the ongoing effort to elucidate how blood vessels establish, maintain, or lose mechanical homeostasis. While we have previously developed inverse modeling approaches to translate image‐derived surface deformations into biomechanical metrics of interest, our workflow did not heretofore include a methodology to quantify uncertainties in estimated local mechanical properties. In the present study, we have integrated a novel uncertainty quantification and propagation pipeline within our inverse modeling approach.
Bibliography:Funding information
National Institutes of Health, Grant/Award Number: U01‐HL142518
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ISSN:2040-7939
2040-7947
2040-7947
DOI:10.1002/cnm.3535