title: Prior Distributions of Material Parameters for Bayesian Calibration of Growth and Remodeling Computational Model of Abdominal Aortic Wall.
abstract: 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.
authors.forename: Sajjad
authors.initials: S
authors.lastname: Seyedsalehi
authors.forename: Liangliang
authors.initials: L
authors.lastname: Zhang
authors.forename: Jongeun
authors.initials: J
authors.lastname: Choi
authors.forename: Seungik
authors.initials: S
authors.lastname: Baek
mesh_terms: D000368:Aged; D001012:Aorta, Abdominal / Q000254:growth & development / Q000502:physiology*; D001499:Bayes Theorem; D001696:Biomechanical Phenomena; D002138:Calibration; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D066230:Patient-Specific Modeling; D066253:Vascular Remodeling; D055815:Young Adult