Aortic local biomechanical properties in ascending aortic aneurysms

Lin, Shih-Yang; Morgant, Marie-Catherine; Marín-Castrillón, Diana M.; Walker, Paul; Glélé, Ludwig Serge Aho; Boucher, Arnaud; Presles, Benoît; Bouchot, Olivier; Lalande, Alain

doi:10.1016/j.actbio.2022.06.019

Cited by 9 publications

(18 citation statements)

References 62 publications

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“…To implement that, the local outer perimeter of each quadrant is required. Similar behavior can be observed between in-vivo strain and the ex-vivo parameters of Young's modulus [9] (Table 4). If we compare the quadrants in pairs, medial and anterior have the minimum values, while lateral and posterior have the maximum ones.…”

Section: Discussionsupporting

confidence: 81%

“…Some studies [ 7 , 8 ] emerge that in many cases of acute aortic dissection, the diameter is less than 55 mm. Lin et al [ 9 ] declared that there is a need for new, more effective criteria for the prevention of aneurysm complications based on biaxial tensile tests on the human ascending aorta. For instance, we can consider parameters like aortic compliance and Young’s modulus [ 10 ] that quantify the biomechanical properties of the aorta.…”

Section: Introductionmentioning

confidence: 99%

“…While studies like the one of Morgant et al [ 12 ] assessed the global elastic properties of the aorta, our study contribution is based on the in-vivo assessment of local biomechanical properties. The reason we study the local properties is that the aortic wall certainly involves differences in the behavior of stiffness depending on the localization [ 9 ]. Considering four different quadrants, each one should result in different elasticity and distensibility properties.…”

Section: Introductionmentioning

confidence: 99%

“…Considering this ex-vivo study, the lateral quadrant was the stiffest, while the medial quadrant had the lowest aortic stiffness across the walls. Local distensibility measurements from ex-vivo experiments [ 9 , 12 ] named maximum Young’s modulus and physiological Young’s modulus are compared with local in-vivo compliance of each quadrant to analyze the possible correlation between them. Aortic samples retrieved during surgery are stretched to investigate their ex-vivo elastic properties.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing

Markodimitrakis

Lin

Koutoulakis

et al. 2023

JCM

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Ascending aortic aneurysm is a pathology that is important to be supervised and treated. During the years the aorta dilates, it becomes stiff, and its elastic properties decrease. In some cases, the aortic wall can rupture leading to aortic dissection with a high mortality rate. The main reference standard to measure when the patient needs to undertake surgery is the aortic diameter. However, the aortic diameter was shown not to be sufficient to predict aortic dissection, implying other characteristics should be considered. Therefore, the main objective of this work is to assess in-vivo the elastic properties of four different quadrants of the ascending aorta and compare the results with equivalent properties obtained ex-vivo. The database consists of 73 cine-MRI sequences of thoracic aorta acquired in axial orientation at the level of the pulmonary trunk. All the patients have dilated aorta and surgery is required. The exams were acquired just prior to surgery, each consisting of 30 slices on average across the cardiac cycle. Multiple deep learning architectures have been explored with different hyperparameters and settings to automatically segment the contour of the aorta on each image and then automatically calculate the aortic compliance. A semantic segmentation U-Net network outperforms the rest explored networks with a Dice score of 98.09% (±0.96%) and a Hausdorff distance of 4.88 mm (±1.70 mm). Local aortic compliance and local aortic wall strain were calculated from the segmented surfaces for each quadrant and then compared with elastic properties obtained ex-vivo. Good agreement was observed between Young’s modulus and in-vivo strain. Our results suggest that the lateral and posterior quadrants are the stiffest. In contrast, the medial and anterior quadrants have the lowest aortic stiffness. The in-vivo stiffness tendency agrees with the values obtained ex-vivo. We can conclude that our automatic segmentation method is robust and compatible with clinical practice (thanks to a graphical user interface), while the in-vivo elastic properties are reliable and compatible with the ex-vivo ones.

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Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing

Markodimitrakis

Lin

Koutoulakis

et al. 2023

JCM

Self Cite

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“…However, in the LAT quadrant of both AsAA-BAV and AsAA-QAV samples, the LON direction had significantly greater failure stiffness than the CIR direction. In a previous study [ 5 ] on 100 patients, the value of failure stiffness in the LAT quadrant was the highest among all regions, while the value of the MED quadrant was the lowest. Therefore, the stress–strain and Young’s modulus–stress curves of MED and LAT quadrants were chosen for comparison ( Figure 2 ).…”

Section: Discussionmentioning

confidence: 92%

What Are the Biomechanical Properties of an Aortic Aneurysm Associated with Quadricuspid Aortic Valve?

Lin

Morgant

Marín-Castrillón

et al. 2022

JCM

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Association of quadricuspid aortic valve (QAV) with ascending aortic aneurysms (AsAA) is rare. A 63-year-old female with hypertension was found (on MRI) to have an ascending aortic aneurysm (52 mm in maximum diameter) and dilatation at the level of the sinotubular junction (38 mm in diameter) associated with quadricuspid aortic valve. An ascending aortic wall replacement surgery was performed. In this study, we focus on the behavior of the aorta associated with QAV considering the in vitro biomechanical characteristics and histology. The properties of QAV are closer to bicuspid aortic valve than tricuspid aortic valve, but with higher wall thickness.

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Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks

Wu,

Daneker,

Turner

et al. 2024

Small Methods

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The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full‐field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data‐driven models for learning full‐field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full‐field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics‐informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics‐informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo‐Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

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Aortic local biomechanical properties in ascending aortic aneurysms

Cited by 9 publications

References 62 publications

Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing

Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing

What Are the Biomechanical Properties of an Aortic Aneurysm Associated with Quadricuspid Aortic Valve?

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks

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