Experimental study and constitutive modelling of the passive mechanical properties of the porcine carotid artery and its relation to histological analysis: Implications in animal cardiovascular device trials

García, A.; Peña, E.; Laborda, Alicia; Lostalé, Fernando; Gregorio, Miguel Ángel De; Doblaré, M.; Martı́nez, Miguel Ángel

doi:10.1016/j.medengphy.2011.01.016

Cited by 55 publications

(72 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most importantly, by a continuous turn over of VSMC and collagen, the tissue microstructure adapts to its mechanical environment/needs (see for example [17]), which naturally has significant implications on the physiological vessel performance. Vessel wall adaptation to mechanical needs also explains the reported intraspecies and interspecies variabilities of structural and mechanical properties [11,21,34].…”

Section: Introductionmentioning

confidence: 90%

“…A set of uniaxial tests obtained by the authors in a previous study was used to validate mean collagen organization obtained in this study [11]. The experimental data consists of a simple tension tests of circumferential and longitudinal strips from the proximal and distal parts of porcine carotids.…”

Section: Validation Datamentioning

confidence: 99%

“…Three preliminary cycles at all load levels were applied in order to precondition the sample and the last (fourth) cycle was used for subsequent analysis. For more detail of the experiments, see García et al [11].…”

Section: Validation Datamentioning

confidence: 99%

“…However, since vessel histology is markedly different between the muscular distal and the elastic proximal segments (see for example [9,11,18,21]), also the axial stretch could be inhomogeneous distributed amongst them. Separate read-outs for distal and proximal segments with optical markers, for example, could avoid this draw-back in our study and perhaps improve the reported results.…”

Section: Family Of Collagen Fibers With Positive Elevationmentioning

confidence: 99%

“…Specifically, inflation [35,38], planar biaxial [20,24] and uniaxial [11,32] testing are preferable in-vitro mechanical test protocols for vascular tissue. Here, animal models remain popular in clinical hypothesis testing, where specifically the pig carotid artery has a central role.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Microstructural quantification of collagen fiber orientations and its integration in constitutive modeling of the porcine carotid artery

et al. 2016

Self Cite

View full text Add to dashboard Cite

Background Mechanical characteristics of vascular tissue may play a role in different arterial pathologies, which, amongst others, requires robust constitutive descriptions to capture the vessel wall’s anisotropic and non-linear properties.Specifically, the complex 3D network of collagen and its interaction with other structural elements has a dominating effect of arterial properties at higher stress levels.The aim of this study is to collect quantitative collagen organization as well as mechanical properties to facilitate structural constitutive models for the porcine carotid artery.This helps the understanding of the mechanics of swine carotid arteries, being a standard in clinical hypothesis testing, in endovascular preclinical trials for example. Method Porcine common carotid arteries (n = 10) were harvested and used to (i) characterize the collagen fiber organization with polarized light microscopy, and (ii) the biaxial mechanical properties by inflation testing.The collagen organization was quantified by the Bingham orientation density function (ODF), which in turn was integrated in a structural constitutive model of the vessel wall.A one-layered and thick-walled model was used to estimate mechanical constitutive parameters by least-square fitting the recorded in vitro inflation test results.Finally, uniaxial data published elsewhere were used to validate the mean collagen organization described by the Bingham ODF. Results Thick collagen fibers, i.e.the most mechanically relevant structure, in the common carotid artery are dispersed around the circumferential direction.In addition, almost all samples showed two distinct families of collagen fibers at different elevation, but not azimuthal, angles.Collagen fiber organization could be accurately represented by the Bingham ODF (¿1,2,3=[13.5,0.0,25.2] and ¿1,2,3=[14.7,0.0,26.6]; average error of about 5%), and their integration into a structural constitutive model captured the inflation characteristics of individual carotid artery samples.Specifically, only four mechanical parameters were required to reasonably (average error from 14% to 38%) cover the experimental data over a wide range of axial and circumferential stretches.However, it was critical to account for fibrilar links between thick collagen fibers.Finally, the mean Bingham ODF provide also good approximation to uniaxial experimental data. Conclusions The applied structural constitutive model, based on individually measured collagen orientation densities, was able to capture the biaxial properties of the common carotid artery. Since the model required coupling amongst thick collagen fibers, the collagen fiber orientations measured from polarized light microscopy, alone, seem to be insufficient structural information. Alternatively, a larger dispersion of collagen fiber orientations, that is likely to arise from analyzing larger wall sections, could have had a similar effect, i.e. could have avoided coupling amongst thick collagen fibers.Peer ReviewedPostprint (author's final draft

show abstract

Section: Introductionmentioning

confidence: 90%

Section: Validation Datamentioning

confidence: 99%

Section: Validation Datamentioning

confidence: 99%

Section: Family Of Collagen Fibers With Positive Elevationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Microstructural quantification of collagen fiber orientations and its integration in constitutive modeling of the porcine carotid artery

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

On the use of machine learning techniques for the mechanical characterization of soft biological tissues

Cilla

Pérez-Rey

Martı́nez

et al. 2018

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees, and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden, and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the strain energy function and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted; thus, the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues.

show abstract

High resolution imaging of the fibrous microstructure in bovine common carotid artery using optical polarization tractography

Azinfar

Ravanfar

Wang

et al. 2015

Journal of Biophotonics

View full text Add to dashboard Cite

The biomechanical properties of artery are primarily determined by the fibrous structures in the vessel wall. Many vascular diseases are associated with alternations in the orientation and alignment of the fibrous structure in the arterial wall. Knowledge on the structural features of the artery wall is crucial to our understanding of the biology of vascular diseases and the development of novel therapies. Optical coherence tomography (OCT) and polarization-sensitive OCT have shown great promise in imaging blood vessels due to their high resolution, fast acquisition, good imaging depth, and large field of view. However, the feasibility of using OCT based methods for imaging fiber orientation and distribution in the arterial wall has not been investigated. Here we show that the optical polarization tractography (OPT), a technology developed from Jones matrix OCT, can reveal the fiber orientation and alignment in the bovine common carotid artery. The fiber orientation and alignment data obtained in OPT provided a robust contrast marker to clearly resolve the intima and media boundary of the carotid artery wall. Optical polarization tractography can visualize fiber orientation and alignment in carotid artery.

show abstract

Experimental study and constitutive modelling of the passive mechanical properties of the porcine carotid artery and its relation to histological analysis: Implications in animal cardiovascular device trials

Cited by 55 publications

References 33 publications

Microstructural quantification of collagen fiber orientations and its integration in constitutive modeling of the porcine carotid artery

Microstructural quantification of collagen fiber orientations and its integration in constitutive modeling of the porcine carotid artery

On the use of machine learning techniques for the mechanical characterization of soft biological tissues

High resolution imaging of the fibrous microstructure in bovine common carotid artery using optical polarization tractography

Contact Info

Product

Resources

About