Integration of cross-links, discrete fiber distributions and of a non-local theory in the Homogenized Constrained Mixture Model to Simulate Patient-Specific Thoracic Aortic Aneurysm Progression

Sempértegui, Felipe; Avril, Stéphane

doi:10.1016/j.jbiomech.2024.112297

Journal of Biomechanics

2025

DOI: 10.1016/j.jbiomech.2024.112297

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Integration of cross-links, discrete fiber distributions and of a non-local theory in the Homogenized Constrained Mixture Model to Simulate Patient-Specific Thoracic Aortic Aneurysm Progression

Felipe Sempértegui,

Stéphane Avril

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References 42 publications

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Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

Vervenne,

Peirlinck,

Famaey

et al. 2024

Preprint

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Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross procedure, using neural networks to discover the most suitable material model. The Ross procedure is a complex cardiac surgery where the patient's own pulmonary valve is used to replace the diseased aortic valve. Ensuring the successful long-term outcomes of this intervention requires a detailed understanding of the mechanical properties of pulmonary tissue. Constitutive artificial neural networks offer a novel approach to capture such complex stress-strain relationships. Here we design and train different constitutive neural networks to characterize the hyperelastic, anisotropic behavior of the main pulmonary artery. Informed by experimental biaxial testing data under various axial-circumferential loading ratios, these networks automatically discover the inherent material behavior, without the limitations of predefined mathematical models. We regularize the model discovery using cross-sample feature selection and explore its sensitivity to the collagen fiber distribution. Strikingly, we uniformly discover an isotropic exponential first-invariant term and an anisotropic quadratic fifth-invariant term. We show that constitutive models with both these terms can reliably predict arterial responses under diverse loading conditions. Our results provide crucial improvements in experimental data agreement, and enhance our understanding into the biomechanical properties of pulmonary tissue. The model outcomes can be used in a variety of computational frameworks of autograft adaptation, ultimately improving the surgical outcomes after the Ross procedure.

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Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

Vervenne,

Peirlinck,

Famaey

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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Integration of cross-links, discrete fiber distributions and of a non-local theory in the Homogenized Constrained Mixture Model to Simulate Patient-Specific Thoracic Aortic Aneurysm Progression

Cited by 1 publication

References 42 publications

Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

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