Prior distributions of material parameters for Bayesian calibration of growth and remodeling computational model of abdominal aortic wall

Sajjad Seyedsalehi, Liangliang Zhang, Jongeun Choi, Seungik Baek

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Article number101001
JournalJournal of Biomechanical Engineering
Issue number10
Publication statusPublished - 2015 Oct 1

Bibliographical note

Publisher Copyright:
© 2015 by ASME.

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Physiology (medical)


Dive into the research topics of 'Prior distributions of material parameters for Bayesian calibration of growth and remodeling computational model of abdominal aortic wall'. Together they form a unique fingerprint.

Cite this