ArticleJournal of biomechanical engineering2015
Prior Distributions of Material Parameters for Bayesian Calibration of Growth and Remodeling Computational Model of Abdominal Aortic Wall.
Article in Journal of biomechanical engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 37 citations in OpenAlex.
- A microstructurally motivated framework to study autoregulation in the coronary circulation.The Journal of physiology · 2026Article
- Physics-Based Growth and Remodeling Modeling for Virtual Abdominal Aortic Aneurysm Evolution and Growth Prediction.medRxiv : the preprint server for health sciences · 2026Article
- A Microstructurally-Motivated Framework to Study Autoregulation in the Coronary Circulation.bioRxiv : the preprint server for biology · 2025Article
- Variations in stiffness and structure of the human aorta along its length.Scientific reports · 2025Article
- Effect of aging on the biaxial mechanical behavior of human descending thoracic aorta: Experiments and constitutive modeling considering collagen crosslinking.Journal of the mechanical behavior of biomedical materials · 2023Article
- A Biochemomechanical Model of Collagen Turnover in Arterial Adaptations to Hemodynamic Loading.Research square · 2023Article
- Computational modelling of multi-temporal ventricular-vascular interactions during the progression of pulmonary arterial hypertension.Journal of the Royal Society, Interface · 2022Article
- Region-dependent mechanical characterization of porcine thoracic aorta with a one-to-many correspondence method to create virtual datasets using uniaxial tensile tests.Frontiers in bioengineering and biotechnology · 2022Article
- Inverse modeling framework for characterizing patient-specific microstructural changes in the pulmonary arteries.Journal of the mechanical behavior of biomedical materials · 2021Article
- Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications.Computers in biology and medicine · 2021Article
- Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration.IEEE journal of biomedical and health informatics · 2019Article
- Article
- Bayesian inference of constitutive model parameters from uncertain uniaxial experiments on murine tendons.Journal of the mechanical behavior of biomedical materials · 2019Article
- Reactive Constrained Mixtures for Modeling the Solid Matrix of Biological Tissues.Journal Of Elasticity · 2017Article
- Empirically Determined Vascular Smooth Muscle Cell Mechano-Adaptation Law.Journal of biomechanical engineering · 2017Article
- Computational Growth and Remodeling of Abdominal Aortic Aneurysms Constrained by the Spine.Journal of biomechanical engineering · 2015Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.