Patient-specific finite element analysis of ascending aorta aneurysms

Martin, Caitlin; Sun, Wei; Elefteriades, John A.

doi:10.1152/ajpheart.00908.2014

Cited by 82 publications

(86 citation statements)

References 41 publications

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“…As our community moves forward to more patient-based geometries and simulations involving realistic geometries that necessarily lead to complex stress fields, validation of models in multidimensional loading is crucial. For example, it is common [62][63][64][65][66] to report results in terms of principal stresses, which are informative but do not address the fact that a stress acting radially or in shear is more likely to lead to tissue failure than one acting circumferentially. Martin et al [65] used a potentially generalizable energy-based failure threshold, but they based the failure criterion on uniaxial circumferential tests.…”

Section: Discussionmentioning

confidence: 99%

“…For example, it is common [62][63][64][65][66] to report results in terms of principal stresses, which are informative but do not address the fact that a stress acting radially or in shear is more likely to lead to tissue failure than one acting circumferentially. Martin et al [65] used a potentially generalizable energy-based failure threshold, but they based the failure criterion on uniaxial circumferential tests. Although Martin's work represents a significant advance and demonstrates the potential of the patient-specific FE approach, there is clear need for a more accurate failure model, which could be informed by the current work.…”

Section: Discussionmentioning

confidence: 99%

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Failure of the Porcine Ascending Aorta: Multidirectional Experiments and a Unifying Microstructural Model

Witzenburg

Dhume

Shah

et al. 2017

Journal of Biomechanical Engineering

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The ascending thoracic aorta is poorly understood mechanically, especially its risk of dissection. To make better predictions of dissection risk, more information about the multidimensional failure behavior of the tissue is needed, and this information must be incorporated into an appropriate theoretical/computational model. Toward the creation of such a model, uniaxial, equibiaxial, peel, and shear lap tests were performed on healthy porcine ascending aorta samples. Uniaxial and equibiaxial tests showed anisotropy with greater stiffness and strength in the circumferential direction. Shear lap tests showed catastrophic failure at shear stresses (150-200 kPa) much lower than uniaxial tests (750-2500 kPa), consistent with the low peel tension ($60 mN/mm). A novel multiscale computational model, including both prefailure and failure mechanics of the aorta, was developed. The microstructural part of the model included contributions from a collagenreinforced elastin sheet and interlamellar connections representing fibrillin and smooth muscle. Components were represented as nonlinear fibers that failed at a critical stretch. Multiscale simulations of the different experiments were performed, and the model, appropriately specified, agreed well with all experimental data, representing a uniquely complete structure-based description of aorta mechanics. In addition, our experiments and model demonstrate the very low strength of the aorta in radial shear, suggesting an important possible mechanism for aortic dissection.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Failure of the Porcine Ascending Aorta: Multidirectional Experiments and a Unifying Microstructural Model

Witzenburg

Dhume

Shah

et al. 2017

Journal of Biomechanical Engineering

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“…Examining surgical specimens, the ‘delamination strength’ of aneurysmal aortic tissue was significantly lower with BAV as compared to TAV 38. However, in a study of aneurysm patients undergoing surgery, there was no correlation found between experimental rupture risk between BAV and TAV aneurysms 39. Currently, in vivo data about the correlation between size of BAV-associated aortic aneurysm, BSA, age, and gender and risk of acute aortic events are very limited.…”

Section: Could Biomechanical Analysis Of the Ascending Aorta Predict mentioning

confidence: 98%

“…Potential factors influencing the risk of aortic dissection in the setting of aortic aneurysm may include gender, body size, family history, aortic growth rate, BAV morphology, aortic dilatation phenotype, aortic wall stress and valve dysfunction 1 32 39…”

Section: Prognostic Factors In Patients With Bav Aortopathymentioning

confidence: 99%

Surgical threshold for bicuspid aortic valve aneurysm: a case for individual decision-making

Adamo

Braverman

2015

Heart

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The bicuspid aortic valve (BAV) affects 1-2% of the population and may be associated with important valvular disease and an increased risk of aortic root and/or ascending aortic aneurysm and dissection. BAV aortic aneurysm and dissection occur earlier in life than when these disorders are associated with a tricuspid aortic valve (TAV). Alterations in the aortic media and differences in aortic elastic properties and wall stress also accompany BAV aortopathy. With appropriate follow-up and timely surgery, population studies have documented a survival rate for patients with BAV no different from age-matched controls. Guidelines have previously recommended prophylactic aortic surgery at a smaller aortic aneurysm size for patients with BAV compared with aneurysms in patients with a TAV. Recent guidelines have presented differing indications regarding the appropriate timing of prophylactic surgery for BAV aneurysms, giving the recommendation for surgery when the aortic root and/or ascending aortic exceeds 5.5 cm (unless certain factors are present), the same size for which TAV-associated aortic aneurysm surgery is recommended. We review the pathophysiology of BAV aortopathy, the clinical history of BAV ascending aortic disease, areas of uncertainty and make a case for a patient-centered, individualised decision regarding the optimal timing of aortic aneurysm surgery in BAV disease.

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“…Finite element (FE) analyses have been utilized for studying aortic aneurysm biomechanics and rupture risk (Celi and Berti 2014; Doyle et al 2009; Erhart et al 2015; Fillinger et al 2002; Gasser 2016; Georgakarakos et al 2010; Maier et al 2010; Martin et al 2015; Rodríguez et al 2008; Venkatasubramaniam et al 2004; Vorp et al 1998). The main limitations in these studies are the use of simplified and isotropic tissue properties (Celi and Berti 2014; Doyle et al 2009; Fillinger et al 2002; Georgakarakos et al 2010; Maier et al 2010; Vorp et al 1998), idealized geometries (Celi and Berti 2014; Rodríguez et al 2008; Vorp et al 1998), neglect of pre-stress (Celi and Berti 2014; Doyle et al 2009; Georgakarakos et al 2010) and lack of tissue failure criteria (Celi and Berti 2014; Doyle et al 2009; Georgakarakos et al 2010; Rodríguez et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm

Liang

Liu

Martin

et al. 2017

Biomech Model Mechanobiol

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140

130

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Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g. diameter and curvature) and wall stress. This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues. This method consists of four main steps: (1) constructing a statistical shape model (SSM) from clinical 3D CT images of AsAA patients; (2) generating a dataset of representative aneurysm shapes and obtaining FEA predicted risk scores defined as systolic pressure divided by rupture pressure (rupture is determined by a threshold criterion); (3) establishing relationship between shape features and risk by using classifiers and regressors; and (4) evaluating such relationship in cross validation. The results show that SSM parameters can be used as strong shape features to make predictions of risk scores consistent with FEA, which lead to an average risk classification accuracy of 95.58% by using support vector machine and an average regression error of 0.0332 by using support vector regression, while intuitive geometric features have relatively weak performance. Compared to FEA, this machine learning approach is magnitudes faster. In our future studies, material properties and inhomogeneous thickness will be incorporated into the models and learning algorithms, which may lead to a practical system for clinical applications.

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Patient-specific finite element analysis of ascending aorta aneurysms

Abstract: Martin C, Sun W, Elefteriades J. Patient-specific finite element analysis of ascending aorta aneurysms.

Cited by 82 publications

References 41 publications

Failure of the Porcine Ascending Aorta: Multidirectional Experiments and a Unifying Microstructural Model

Failure of the Porcine Ascending Aorta: Multidirectional Experiments and a Unifying Microstructural Model

Surgical threshold for bicuspid aortic valve aneurysm: a case for individual decision-making

A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm

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