Evidence map›Paper›PMID 39586942›Full record

ArticleBiomechanics and modeling in mechanobiology2025

Estimating nonlinear anisotropic properties of healthy and aneurysm ascending aortas using magnetic resonance imaging.

Álvaro T Latorre Molins, Andrea Guala, Lydia Dux-Santoy, Gisela Teixidó-Turà, José Fernando Rodríguez-Palomares, Miguel Ángel Martínez Barca, Estefanía Peña Baquedano

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Article in Biomechanics and modeling in mechanobiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Inverse finite element identification of murine aortic material properties:Computer methods in biomechanics and biomedical engineering · 2025
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Álvaro T Latorre MolinsAragón Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain. alatorr@unizar.es.
Andrea GualaVall d'Hebron Institut de Recerca, Barcelona, Spain.
Lydia Dux-SantoyVall d'Hebron Institut de Recerca, Barcelona, Spain.
Gisela Teixidó-TuràVall d'Hebron Institut de Recerca, Barcelona, Spain.
José Fernando Rodríguez-PalomaresVall d'Hebron Institut de Recerca, Barcelona, Spain.
Miguel Ángel Martínez BarcaAragón Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
Estefanía Peña BaquedanoAragón Institute for Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain. fany@unizar.es.

Funding

Gobierno de Aragón CUS/581/2020Instituto de Salud Carlos III PI19/01480'la Caixa' Foundation LCF/BQ/PR22/11920008Ministerio de Ciencia e Innovación PID2022-140219OB-I00Sociedad Española de Cardiología SEC/FEC-INV-CLI 21/030
6 · The paper itself

Abstract

An ascending aortic aneurysm is an often asymptomatic localized dilatation of the aorta. Aortic rupture is a life-threatening event that occurs when the stress on the aortic wall exceeds its mechanical strength. Therefore, patient-specific finite element models could play an important role in estimating the risk of rupture. This requires not only the geometry of the aorta but also the nonlinear anisotropic properties of the tissue. In this study, we presented a methodology to estimate the mechanical properties of the aorta from magnetic resonance imaging (MRI). As a theoretical framework, we used finite element models to which we added noise to simulate clinical data from real patient geometry and different properties of healthy and aneurysmal aortic tissues collected from the literature. The proposed methodology considered the nonlinear properties, the zero pressure geometry, the heart motion, and the external tissue support. In addition, we analyzed the aorta as a homogeneous material and as a heterogeneous model with different properties for the ascending and descending parts. The methodology was also applied to pre-surgical,in vivo MRI data of a patient who underwent surgery during which an aortic wall sample was obtained. The results were compared with those obtained from ex vivo biaxial test of the patient's tissue sample. The methodology showed promising results after successfully recovering the nonlinear anisotropic material properties of all analyzed cases. This study demonstrates that the variable used during the optimization process can affect the result. In particular, variables such as principal strains were found to obtain more realistic materials than the displacement field.

Indexed as

AortaAortic AneurysmMagnetic Resonance ImagingNonlinear DynamicsAnisotropyBiomechanical PhenomenaFinite Element AnalysisHumansMaleModels, CardiovascularStress, MechanicalAscending aortaInverse modelingMechanical characterizationNonlinear

Identifiers

PMID39586942
PMCPMC11846743

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.