Cargando…

An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta

The Constrained Mixture Model (CMM) is a novel approach to describe arterial wall mechanics, whose formulation is based on a referential physiological state. The CMM considers the arterial wall as a mixture of load-bearing constituents, each of them with characteristic mass fraction, material proper...

Descripción completa

Detalles Bibliográficos
Autores principales: Navarrete, Álvaro, Utrera, Andrés, Rivera, Eugenio, Latorre, Marcos, Celentano, Diego J., García-Herrera, Claudio M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694237/
http://dx.doi.org/10.3389/fbioe.2023.1301988
_version_ 1785153330554601472
author Navarrete, Álvaro
Utrera, Andrés
Rivera, Eugenio
Latorre, Marcos
Celentano, Diego J.
García-Herrera, Claudio M.
author_facet Navarrete, Álvaro
Utrera, Andrés
Rivera, Eugenio
Latorre, Marcos
Celentano, Diego J.
García-Herrera, Claudio M.
author_sort Navarrete, Álvaro
collection PubMed
description The Constrained Mixture Model (CMM) is a novel approach to describe arterial wall mechanics, whose formulation is based on a referential physiological state. The CMM considers the arterial wall as a mixture of load-bearing constituents, each of them with characteristic mass fraction, material properties, and deposition stretch levels from its stress-free state to the in-vivo configuration. Although some reports of this model successfully assess its capabilities, they barely explore experimental approaches to model patient-specific scenarios. In this sense, we propose an iterative fitting procedure of numerical-experimental nature to determine material parameters and deposition stretch values. To this end, the model has been implemented in a finite element framework, and it is calibrated using reported experimental data of descending thoracic aorta. The main results obtained from the proposed procedure consist of a set of material parameters for each constituent. Moreover, a relationship between deposition stretches and residual strain measurements (opening angle and axial stretch) has been numerically proved, establishing a strong consistency between the model and experimental data.
format Online
Article
Text
id pubmed-10694237
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-106942372023-12-05 An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta Navarrete, Álvaro Utrera, Andrés Rivera, Eugenio Latorre, Marcos Celentano, Diego J. García-Herrera, Claudio M. Front Bioeng Biotechnol Bioengineering and Biotechnology The Constrained Mixture Model (CMM) is a novel approach to describe arterial wall mechanics, whose formulation is based on a referential physiological state. The CMM considers the arterial wall as a mixture of load-bearing constituents, each of them with characteristic mass fraction, material properties, and deposition stretch levels from its stress-free state to the in-vivo configuration. Although some reports of this model successfully assess its capabilities, they barely explore experimental approaches to model patient-specific scenarios. In this sense, we propose an iterative fitting procedure of numerical-experimental nature to determine material parameters and deposition stretch values. To this end, the model has been implemented in a finite element framework, and it is calibrated using reported experimental data of descending thoracic aorta. The main results obtained from the proposed procedure consist of a set of material parameters for each constituent. Moreover, a relationship between deposition stretches and residual strain measurements (opening angle and axial stretch) has been numerically proved, establishing a strong consistency between the model and experimental data. Frontiers Media S.A. 2023-11-20 /pmc/articles/PMC10694237/ http://dx.doi.org/10.3389/fbioe.2023.1301988 Text en Copyright © 2023 Navarrete, Utrera, Rivera, Latorre, Celentano and García-Herrera. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Navarrete, Álvaro
Utrera, Andrés
Rivera, Eugenio
Latorre, Marcos
Celentano, Diego J.
García-Herrera, Claudio M.
An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta
title An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta
title_full An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta
title_fullStr An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta
title_full_unstemmed An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta
title_short An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta
title_sort inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694237/
http://dx.doi.org/10.3389/fbioe.2023.1301988
work_keys_str_mv AT navarretealvaro aninversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT utreraandres aninversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT riveraeugenio aninversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT latorremarcos aninversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT celentanodiegoj aninversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT garciaherreraclaudiom aninversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT navarretealvaro inversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT utreraandres inversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT riveraeugenio inversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT latorremarcos inversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT celentanodiegoj inversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta
AT garciaherreraclaudiom inversefittingstrategytodeterminetheconstrainedmixturemodelparametersapplicationinpatientspecificaorta