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 patientspecific 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 patientspecific 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: GroupI in which the parameters ce,آ ck1,آ ck2,آ cm2,Ghc, and د•e are correlated with age, and GroupII in which the parameters cm1,آ Ghm,آ G1e,آ G2e, and خ± are not correlated with age. For the parameters in GroupI, we used regression associated with age via linear or inverse relations, in which their prior distributions provide conditional distributions with confidence intervals. For GroupII, 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 subjectspecific 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.