Solving membrane stress on deformed configuration using inverse elastostatic and forward penalty methods

Lu, Jia; Luo, Yuanming

doi:10.1016/j.cma.2016.05.017

Cited by 17 publications

(15 citation statements)

References 20 publications

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“…Although the resolution of routine diagnostic scans may not be enough to fully resolve the wall motion, it is expected that the gap can be soon filled by the advance of technology. The wall stress can be reasonably estimated from in vivo surface geometry and the blood pressure without invoking the elastic property of the tissue . Thus, it reasonable to expect the tensions and strain data in the physiological pressure range to be made available.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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“…To this end, we introduce a new benchmark data set comprised of 3D surface meshes of 26 cerebral aneurysm cases. According to relevant literature [LZR07, LZR08, LL16, LFBL18], the magnitude of the mechanical stress distribution on aneurysm is known to be correlated with the local surface geometry. The goal here, therefore, is to utilize CNNs to predict the stress distribution on aneurysm based on surface geometry.…”

Section: Methodsmentioning

confidence: 99%

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

Sun

Rooke

Charton

et al. 2020

Computer Graphics Forum

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In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two‐dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid‐like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid‐like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.

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“…The static determinacy approach is usually based on thin-walled models [17,18] which are incompatible with the residual deformation. Thus it is usually considered as a limitation that the All rights reserved.…”

Section: Introductionmentioning

confidence: 99%

Engineering Analysis of Aortic Wall Stress in the Surgery of V-shape Resection of the Noncoronary Sinus

Dong

Liu

Qin

et al. 2020

Preprint

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Ascending aortic aneurysms often include the sinotubular junction (STJ) and extend into the root portion of the aorta. The novel surgery of the V-shape resection of the noncoronary sinus of the aortic root has been shown to be a simpler procedure, comparing with traditional surgeries such as full aortic root replacement, for patients with moderate ascending aortic aneurysm and aortic root ectasia. This novel surgery could reduce the diameter and cross-sectional area of the aortic root. However, the detailed effect on the stress field and the rupture risk of the aortic root and aneurysm has not been fully investigated. In this study, we performed patient-specific finite element (FE) analysis based on the 3D geometries of the aortic root and ascending aortic aneurysm, reconstructed directly from the clinical computed tomographic (CT) images. By comparing the pre- and post-surgery results, we investigated the influence of the V-shape surgery on the stress field and rupture risk of the aortic root, ascending aortic aneurysm and aortic arch. It was found that the surgery could significantly reduce the wall stress of the aortic root, ascending aortic aneurysm, as well the aortic arch, and hence lower the rupture risk.

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Solving membrane stress on deformed configuration using inverse elastostatic and forward penalty methods

Cited by 17 publications

References 20 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

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

Engineering Analysis of Aortic Wall Stress in the Surgery of V-shape Resection of the Noncoronary Sinus

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