Journal
JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
Volume 137, Issue 10, Pages -Publisher
ASME
DOI: 10.1115/1.4031116
Keywords
abdominal aortic aneurysm; model parameters; G&R model; correlation; age
Categories
Funding
- National Heart, Lung, and Blood Institute of the National Institutes of Health [R01HL115185, R21HL113857]
- National Science Foundation CAREER award [CMMI-1150376]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1150376] Funding Source: National Science Foundation
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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 c(e), c(k1), c(k2), c(m2), G(h)(c), and phi(e) are correlated with age, and Group-II in which the parameters c(m1), G(h)(m), G(1)(e), G(2)(e), and alpha 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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