Estimating aortic thoracic aneurysm rupture risk using tension–strain data in physiological pressure range: an in vitro study

He, Xuehuan; Avril, Stéphane; Lu, Jia

doi:10.1007/s10237-020-01410-8

Cited by 16 publications

(8 citation statements)

References 86 publications

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“…Current treatment for aortic aneurysms and dissections remains insufficient by relying only on diameters and growth rates to inform treatment decisions (Glimåker et al, 1991;Kurvers et al, 2004;Satriano et al, 2015). However, there are several ongoing studies aimed at improving patient risk stratification for expansion and rupture (He et al, 2021). Investigating the relationship between the vessel biomechanics and composition helps provide a comprehensive understanding of the disease pathology.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Characterization of Tissue Types in Murine Dissecting Aneurysms based on Histology and 4DUltrasound-Derived Strain

Hegner

Cebull²,

Gámez

et al. 2023

Preprint

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Abdominal aortic aneurysm disease is the local enlargement of the aorta, typically in the infrarenal section, causing up to 200,000 deaths/year. In vivo information to characterize the individual elastic properties of the aneurysm wall in terms of rupture risk is lacking. We used a method that combines 4D ultrasound and direct deformation estimation to compute in vivo 3D Green-Lagrange strain in murine angiotensin II-induced dissecting aortic aneurysms, a commonly used mouse model. After euthanasia, histological staining of cross-sectional sections along the aorta was performed in areas where in vivo strains had previously been measured. The histological sections were segmented into intact and fragmented elastin, thrombus with and without red blood cells, and outer vessel wall including the adventitia. Meshes were then created from the individual contours based on the histological segmentations. The isolated contours of the outer wall and lumen from both imaging modalities were registered individually using a coherent point drift algorithm. 2D finite element models were generated from the meshes, and the displacements from the registration were used as displacement boundaries of the lumen and wall contours. Based on the resulting deformed contours, the strains recorded were grouped according to segmented tissue regions. Strains were highest in areas containing intact elastin without thrombus attachment. Strains in areas with intact elastin and thrombus attachment, as well as areas with disrupted elastin, were significantly lower. Strains in thrombus regions with red blood cells were significantly higher compared to thrombus regions without. We then compared this analysis to statistical distribution indices and found that the results of each aligned, elucidating the relationship between vessel strain and structural changes. This work demonstrates the possibility of advancing in vivo assessments to a micro-structural level ultimately improving patient outcomes.

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Section: Introductionmentioning

confidence: 99%

Biomechanical Characterization of Tissue Types in Murine Dissecting Aneurysms based on Histology and 4DUltrasound-Derived Strain

Hegner

Cebull²,

Gámez

et al. 2023

Preprint

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“…Current advancements in modeling now provide the opportunity to leverage machine learning, which has emerged as an effective surrogate model for high-fidelity solvers, in order to overcome previous computational hurdles that would otherwise make such modeling intractable for clinically relevant time frames. Such surrogate models have demonstrated the potential for automated measurement of aortic geometry, patient risk stratification, and the prediction of aneurysm growth and rupture [6,[20][21][22][23][24]. Additionally, physics-informed neural networks (PINNs) [25][26][27][28] have been a promising advancement in the domain of scientific machine learning.…”

Section: Introductionmentioning

confidence: 99%

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Goswami¹,

Li²,

Rego³

et al. 2022

Preprint

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Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture. Several genetic mutations and predisposing factors that contribute to TAA have been studied in mouse models to characterize specific changes in aortic microstructure and material properties that result from a wide range of mechanobiological insults. By contrast, assessments of TAA progression in vivo are largely limited to measurements of aneurysm size and growth rate. It has been shown, however, that aortic geometry alone is not sufficient to predict the patient-specific progression of TAA but computational modeling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating insults. In this work, we present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify contributing factors for TAA by using synthetic finite element-based datasets of aortic growth and remodeling (G&R) resulting from prescribed mechanobiological insults. For training data, we investigate multiple types of TAA risk factors and spatial distributions within a computationally efficient constrained mixture model to generate axial-azimuthal maps of aortic dilatation and distensibility. The trained network is then capable of predicting the initial distribution and extent of the insult from a given set of dilatation and distensibility information, which in turn can be used to determine subsequent aortic geometry and mechanical properties. Two DeepONet frameworks are proposed, one trained on sparse information and one on full-field grayscale images, to gain insight into a preferred neural operator-based approach. Performance of the surrogate models is evaluated through multiple simulations carried out on insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We show that this integrated continuous learning modeling approach can predict the patient-specific mechanobiological insult profile associated with any given dilatation and distensibility map with a high accuracy, particularly when based on full-field images. Our findings demonstrate the feasibility of

show abstract

“…Current advancements in modelling now provide the opportunity to leverage machine learning, which has emerged as an effective surrogate model for high-fidelity solvers, in order to overcome previous computational hurdles that would otherwise make such modelling intractable for clinically relevant time frames. Such surrogate models have demonstrated the potential for automated measurement of aortic geometry, patient risk stratification, and prediction of aneurysm growth and rupture [6,[21][22][23][24][25]. Additionally, physics-informed neural networks (PINNs) [26][27][28][29] have been a promising advancement in the domain of scientific machine learning.…”

Section: Introductionmentioning

confidence: 99%

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Goswami

Rego

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta that can lead to life-threatening dissection or rupture. In vivo assessments of TAA progression are largely limited to measurements of aneurysm size and growth rate. There is promise, however, that computational modelling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating mechanobiological insults. We present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify TAA contributing factors using synthetic finite-element-based datasets. For training, we employ a constrained mixture model of aortic growth and remodelling to generate maps of local aortic dilatation and distensibility for multiple TAA risk factors. We evaluate the performance of the surrogate model for insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We propose two frameworks, one trained on sparse information and one on full-field greyscale images, to gain insight into a preferred neural operator-based approach. We show that this continuous learning approach can predict the patient-specific insult profile associated with any given dilatation and distensibility map with high accuracy, particularly when based on full-field images. Our findings demonstrate the feasibility of applying DeepONet to support transfer learning of patient-specific inputs to predict TAA progression.

show abstract

Estimating aortic thoracic aneurysm rupture risk using tension–strain data in physiological pressure range: an in vitro study

Cited by 16 publications

References 86 publications

Biomechanical Characterization of Tissue Types in Murine Dissecting Aneurysms based on Histology and 4DUltrasound-Derived Strain

Biomechanical Characterization of Tissue Types in Murine Dissecting Aneurysms based on Histology and 4DUltrasound-Derived Strain

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

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