Publication detail

Structure-based constitutive model can accurately predicts planar biaxial properties of arotic wall tissue

POLZER, S. GASSER, T. NOVÁK, K. MAN, V. TICHÝ, M. SKÁCEL, P. BURŠA, J.

Czech title

Konstitutivní model respektující strukturu tepny je schopen přesně predikovat biaxiální vlastnosti tkáně aortální stěny

English title

Structure-based constitutive model can accurately predicts planar biaxial properties of arotic wall tissue

Type

journal article in Web of Science

Language

en

Original abstract

Introduction. Structure-based constitutive models might help exploring mechanisms by which arterial wall histology is linked to wall mechanics. This study aims at validating a recently proposed structure-based constitutive model. Specifically, the models ability to predict mechanical biaxial response of porcine aortic tissue with predefined collagen structure was tested. Materials and Methods. Histological slices from porcine thoracic aorta wall (n=9) were automatically processed to quantify the collagen fiber organization, and mechanical testing identified non-linear properties of the wall samples (n=18) over a wide range of biaxial stretches. Histological and mechanical experimental data was used to identify model parameters of a multi-scale constitutive description for arterial layers proposed recently. The model predictive capability was tested with respect to interpolation and extrapolation. Results. Collagen in the media was predominantly aligned in circumferential direction (planar von Mises distribution with concentration parameter b_M=1.03±0.23), and its coherence decreased gradually from the luminal to the abluminal tissue layers (inner media: b=1.54±0.40; outer media: b=0.72±0.20). In contrast, the collagen in the adventitia was aligned almost isotropically (b_A=0.27±0.11), and no features like families of coherent fibers were identified. The applied constitutive model captured the aorta biaxial properties accurately (coefficient of determination R^2=0.95±0.03) over the entire range of biaxial deformations and with physically meaningful model parameters. Good predictive properties, well outside the parameter identification space, were observed (R^2=0.92±0.04). Conclusions. Multi-scale constitutive models equipped with realistic micro-histological data can predict macroscopic non-linear aorta wall properties. Collagen largely defines already low strain properties of media, which explains the origin of wall anisotropy seen at this strain level. Structure and mechanical properties of adventitia are well designed to protect the media from axial and circumferential overloads

Czech abstract

Konstituivní model založený na struktuře stěny tepny může pomoci odhalit mechanismy kterými je hystologie spojená s mechanickými vlastnostmi tepny. Tato práce se zabývá validací vybraného konstitutivního modelu a jeho schopnosti predikovat biaxiální mechanickou odezvu, pokud jsou vlastnosti kolagenu definovány nezávisle. Výsledky analýzy kolagenních vláken odhalily postupný nárůst rozptylu kolagenních vláken okolo dominantního směru (obvodového) směrem k vnějšímu okraji stěny tepny. V adventicii už byla pozorována prkaticky izotropní orientace kolagenu. Při použití těchto výsledků v daném konstitutivním modelu bylo monžé velmi přesně predikovat různé biaxiální stavy stěny tepny.

English abstract

Introduction. Structure-based constitutive models might help exploring mechanisms by which arterial wall histology is linked to wall mechanics. This study aims at validating a recently proposed structure-based constitutive model. Specifically, the models ability to predict mechanical biaxial response of porcine aortic tissue with predefined collagen structure was tested. Materials and Methods. Histological slices from porcine thoracic aorta wall (n=9) were automatically processed to quantify the collagen fiber organization, and mechanical testing identified non-linear properties of the wall samples (n=18) over a wide range of biaxial stretches. Histological and mechanical experimental data was used to identify model parameters of a multi-scale constitutive description for arterial layers proposed recently. The model predictive capability was tested with respect to interpolation and extrapolation. Results. Collagen in the media was predominantly aligned in circumferential direction (planar von Mises distribution with concentration parameter b_M=1.03±0.23), and its coherence decreased gradually from the luminal to the abluminal tissue layers (inner media: b=1.54±0.40; outer media: b=0.72±0.20). In contrast, the collagen in the adventitia was aligned almost isotropically (b_A=0.27±0.11), and no features like families of coherent fibers were identified. The applied constitutive model captured the aorta biaxial properties accurately (coefficient of determination R^2=0.95±0.03) over the entire range of biaxial deformations and with physically meaningful model parameters. Good predictive properties, well outside the parameter identification space, were observed (R^2=0.92±0.04). Conclusions. Multi-scale constitutive models equipped with realistic micro-histological data can predict macroscopic non-linear aorta wall properties. Collagen largely defines already low strain properties of media, which explains the origin of wall anisotropy seen at this strain level. Structure and mechanical properties of adventitia are well designed to protect the media from axial and circumferential overloads

Keywords in English

biaxial testing; collagen structure; thoracic aorta; constitutive modeling

RIV year

2015

Released

10.01.2015

Publisher

Elsevier

Location

London

ISSN

1742-7061

Volume

14

Number

1

Pages from–to

133–145

Pages count

12

BIBTEX


@article{BUT113126,
  author="Stanislav {Polzer} and Thomas Christian {Gasser} and Kamil {Novák} and Vojtěch {Man} and Michal {Tichý} and Pavel {Skácel} and Jiří {Burša},
  title="Structure-based constitutive model can accurately predicts planar biaxial properties of arotic wall tissue",
  year="2015",
  volume="14",
  number="1",
  month="January",
  pages="133--145",
  publisher="Elsevier",
  address="London",
  issn="1742-7061"
}