A technique for the classification of tissues by combining mechanics based models with Bayesian inference

Doraiswamy, Srikrishna; Criscione, John C.; Srinivasa, Arun R.

doi:10.1016/j.ijengsci.2016.04.002

Cited by 21 publications

(7 citation statements)

References 23 publications

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“…To calculate the posterior, an appropriate noise model and constitutive equations should be used to derive the likelihood function (likelihood). The posterior can be estimated by linking the prior and likelihood using Bayes' theorem [23][24][25],…”

Section: Mechanical Test Data Processing and Analysismentioning

confidence: 99%

Mechanical and Histological Characteristics of Aortic Dissection Tissues

Luo

Wang

et al. 2021

SSRN Journal

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Section: Mechanical Test Data Processing and Analysismentioning

confidence: 99%

Mechanical and Histological Characteristics of Aortic Dissection Tissues

Luo

Wang

et al. 2021

SSRN Journal

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“…An appropriate noise model and chosen constitutive equations are used to derive the likelihood function (likelihood) p(D|θ). The posterior can be calculated by linking the prior and the likelihood using Bayes' theorem [21]- [23],…”

Section: Bayesian Inference Frameworkmentioning

confidence: 99%

“…The strategy is to draw samples from a distribution similar to the posterior by using effective sampling methods. More details about Bayesian inference and different sampling methods can be found in references [21]- [23].…”

Section: Bayesian Inference Frameworkmentioning

confidence: 99%

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Bayesian Inference-Based Estimation of Normal Aortic, Aneurysmal and Atherosclerotic Tissue Mechanical Properties: From Material Testing, Modeling and Histology

Wang¹,

Tokgoz²,

Huang

et al. 2019

IEEE Trans. Biomed. Eng.

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Objective: Mechanical properties of healthy, aneurysmal, and atherosclerotic arterial tissues are essential for assessing the risk of lesion development and rupture. Strain energy density function (SEDF) has been widely used to describe these properties, where material constants of the SEDF are traditionally determined using the ordinary least square (OLS) method. However, the material constants derived using OLS are usually dependent on initial guesses. Methods: To avoid such dependencies, Bayesian inference-based estimation was used to fit experimental stress-stretch curves of 312 tissue strips from 8 normal aortas, 19 aortic aneurysms, and 21 carotid atherosclerotic plaques to determine the constants, C 1 , D 1 , and D 2 of the modified Mooney-Rivlin SEDF. Results: Compared with OLS, material constants varied much less with prior in the Bayesian inferencebased estimation. Moreover, fitted material constants differed amongst distinct tissue types. Atherosclerotic tissues associated with the biggest D 2 , an indicator of the rate of increase in stress during stretching, followed by aneurysmal tissues and those from normal aortas. Histological analyses showed that C 1 and D 2 were associated with elastin content and details of the colla

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“…Beck and Katafygiotis gave an appropriate statistical framework [17] for properly handling the uncertainties due to ill-conditioning and non-uniqueness associated with the inverse problem, which has been widely considered a candidate for easing the ill-posedness of the problem [18][19][20][21][22][23][24]. In the campaign of structural identification, another advantage of Bayesian statistical framework is that uncertainties due to endogenous factors that has been widely accepted can be appropriately considered [25,26]. The framework is not only to give more accurate results for identification but also to provide a quantitative assessment of this accuracy [27].…”

mentioning

confidence: 99%

A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted metamodeling strategy and ultrasound measurements

Yan

Chronopoulos

Cantero‐Chinchilla

et al. 2020

Mechanical Systems and Signal Processing

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Reliable verification and evaluation of the mechanical properties of an assembled layered composite ensemble are critical for industrially relevant applications, but it still remains an open engineering challenge. In this study, a fast Bayesian inference scheme based on multi-frequency single shot measurements of wave propagation characteristics is developed to overcome the limitations of ill-conditioning and non-uniqueness associated with the conventional approaches. A Transitional Markov chain Monte Carlo (TMCMC) algorithm is employed for the sampling process. A Wave and Finite Element (WFE)-assisted metamodeling scheme in lieu of expensive-to-evaluate explicit FE analysis is proposed to cope with the high computational cost involved in TMCMC sampling. For this, the Kriging predictor providing a surrogate mapping between the probability spaces of the model predictions for the wave characteristics and the mechanical properties in the likelihood evaluations is established based on the training outputs computed using a WFE forward solver, coupling periodic structure theory to conventional FE. The valuable uncertainty information 2 of the prediction variance introduced by the use of a surrogate model are also properly taken into account when estimating the parameters' posterior probability distribution by TMCMC.A numerical study as well as an experimental study are conducted to verify the computational efficiency and accuracy of the proposed methodology. Results show that the TMCMC algorithm in conjunction with the WFE forward solver-aided metamodeling can sample the posterior Probability Density Function (PDF) of the updated parameters at a very reasonable cost. This approach is capable of quantifying the uncertainties of recovered independent characteristics for each layer of the composite structure under investigation through fast and inexpensive experimental measurements on localized portions of the structure.

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A technique for the classification of tissues by combining mechanics based models with Bayesian inference

Cited by 21 publications

References 23 publications

Mechanical and Histological Characteristics of Aortic Dissection Tissues

Mechanical and Histological Characteristics of Aortic Dissection Tissues

Bayesian Inference-Based Estimation of Normal Aortic, Aneurysmal and Atherosclerotic Tissue Mechanical Properties: From Material Testing, Modeling and Histology

A fast Bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted metamodeling strategy and ultrasound measurements

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