Characteristics of thoracic aortic aneurysm rupture in vitro

Luo, Yuanming; Duprey, Ambroise; Avril, Stéphane; Lu, Jia

doi:10.1016/j.actbio.2016.06.036

Cited by 28 publications

(18 citation statements)

References 36 publications

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“…This correlation opens the possibility of estimating the strength from in vivo response or using the yielding parameter as a surrogate. In a recent study, the present authors found that ATAA samples rupture uniformly in the direction of maximum stiffness, showing a strong directional correlation. For ATAA tissue (and alike), it is reasonable to hypothesize that correlation exists between the strength (that is, the stress at rupture) and response characteristics prior to rupture.…”

Section: Introductionmentioning

confidence: 49%

“…Tension‐strain curves were obtained using inflation test and a pointwise identification method . Ascending thoracic aortic aneurysm samples were collected from patients undergoing surgery repair, in accordance with a protocol approved by the local institutional review board.…”

Section: Methodsmentioning

confidence: 99%

“…Each specimen was loaded to rupture. A local window containing 3 × 3 elements centered at crack was designated as rupture site . For Gauss points inside the window, the tension at the second last pressure state was defined as the (local) strength.…”

Section: Methodsmentioning

confidence: 99%

“…The goal of the study is to explore the relationship between the strength and elastic properties. Recently, the authors' team collected a large number of tension (the stress resultant over thickness) strain data from in vitro tests on ATAA samples . These data were obtained at different locations in tissue specimens, some at or near sites where the tissue eventually ruptured and most from locations where the tissue remained intact.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning–aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm

Luo

Fan

Baek

et al. 2018

Numer Methods Biomed Eng

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Machine learning was applied to classify tension-strain curves harvested from inflation tests on ascending thoracic aneurysm samples. The curves were classified into rupture and nonrupture groups using prerupture response features. Two groups of features were used as the basis for classification. The first was the constitutive parameters fitted from the tension-strain data, and the second was geometric parameters extracted from the tension-strain curve. Based on the importance scores provided by the machine learning, implications of some features were interrogated. It was found that (1) the value of a constitutive parameter is nearly the same for all members in the rupture group and (2) the strength correlates strongly with a tension in the early phase of response as well as with the end stiffness. The study suggests that the strength, which is not available without rupturing the tissue, may be indirectly inferred from prerupture response features.

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Section: Introductionmentioning

confidence: 49%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning–aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm

Luo

Fan

Baek

et al. 2018

Numer Methods Biomed Eng

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“…where (19) is the fourth-order elasticity tensor. Intact material can propagate waves and, consequently,…”

Section: Loss Of Ellipticity and Crack Directionmentioning

confidence: 99%

Constitutive model of human artery adventitia enhanced with a failure description

Volokh

2019

Mech Soft Mater

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A constitutive model of a human artery adventitia is presented and calibrated in uniaxial tension tests in longitudinal and circumferential directions. Both the experiment and the model describe the descending branch of the stress-stretch curve and, thus, failure becomes a part of the constitutive law. The theoretical failure description is provided by the introduction of energy limiters and it does not use internal damage variables. The latter feature allows for an easy analysis of material instability and the onset of failure. Particularly, the failure envelope is created in biaxial tension under various stretch ratios. In addition, the loss of ellipticity is analyzed in equibiaxial stretch and pure shear and it is found that the localized failure-crack-is approximately aligned with the directions of collagen fibers in accordance with experimental observations.

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Modeling planar response of vascular tissues using quadratic functions of effective strain

2022

Numer Methods Biomed Eng

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Simulation-based studies of the cardiovascular structure such as aorta have become increasingly popular for many biomedical problems such as predictions of aneurysm rupture. A critical step in these simulations is the development of constitutive models that accurately describe the tissue's mechanical behavior. In this work, we present a new constitutive model, which explicitly accounts for the gradual recruitment of collagen fibers. The recruitment is considered using an effective stretch, which is a continuum-scale kinematic variable measuring the uncrimped stretch of the tissue in an average sense. The strain energy of a fiber bundle is described by a quadratic function of the effective strain. Constitutive models formulated in this manner are applied to describe the responses of ascending thoracic aortic aneurysm and porcine thoracic aorta tissues. The heterogeneous properties of the ATAA tissue are extracted from bulge inflation test data, and then used in finite element analysis to simulate the inflation test. The descriptive and predictive capabilities are further assessed using planar testing data of porcine thoracic aortic tissues. It is found that the constitutive model can accurately describe the stress-strain relations. In particular, the finite element simulation replicates the displacement, strain, and stress distributions with excellent fidelity.

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Characteristics of thoracic aortic aneurysm rupture in vitro

Cited by 28 publications

References 36 publications

Machine learning–aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm

Machine learning–aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm

Constitutive model of human artery adventitia enhanced with a failure description

Modeling planar response of vascular tissues using quadratic functions of effective strain

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