Prior Distributions of Material Parameters for Bayesian Calibration of Growth and Remodeling Computational Model of Abdominal Aortic Wall

For the accurate prediction of the vascular disease progression, there is a crucial need for developing a systematic tool aimed toward patient-specific modeling. Considering the interpatient variations, a prior distribution of model parameters has a strong influence on computational results for arte...

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Vydáno v:Journal of biomechanical engineering Ročník 137; číslo 10; s. 101001
Hlavní autoři: Seyedsalehi, Sajjad, Zhang, Liangliang, Choi, Jongeun, Baek, Seungik
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.10.2015
Témata:
Aged
Aorta, Abdominal - growth & development
Aorta, Abdominal - physiology
Bayes Theorem
Biomechanical Phenomena
Calibration
Female
Humans
Male
Middle Aged
Multivariate Analysis
Patient-Specific Modeling
Vascular Remodeling
Young Adult
ISSN:1528-8951, 1528-8951
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Shrnutí:For the accurate prediction of the vascular disease progression, there is a crucial need for developing a systematic tool aimed toward patient-specific modeling. Considering the interpatient variations, a prior distribution of model parameters has a strong influence on computational results for arterial mechanics. One crucial step toward patient-specific computational modeling is to identify parameters of prior distributions that reflect existing knowledge. In this paper, we present a new systematic method to estimate the prior distribution for the parameters of a constrained mixture model using previous biaxial tests of healthy abdominal aortas (AAs). We investigate the correlation between the estimated parameters for each constituent and the patient's age and gender; however, the results indicate that the parameters are correlated with age only. The parameters are classified into two groups: Group-I in which the parameters ce, ck1, ck2, cm2,Ghc, and ϕe are correlated with age, and Group-II in which the parameters cm1, Ghm, G1e, G2e, and α are not correlated with age. For the parameters in Group-I, we used regression associated with age via linear or inverse relations, in which their prior distributions provide conditional distributions with confidence intervals. For Group-II, the parameter estimated values were subjected to multiple transformations and chosen if the transformed data had a better fit to the normal distribution than the original. This information improves the prior distribution of a subject-specific model by specifying parameters that are correlated with age and their transformed distributions. Therefore, this study is a necessary first step in our group's approach toward a Bayesian calibration of an aortic model. The results from this study will be used as the prior information necessary for the initialization of Bayesian calibration of a computational model for future applications.
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ISSN:1528-8951
1528-8951
DOI:10.1115/1.4031116