No Cover Image

Journal article 601 views 72 downloads

A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves

Ankush Aggarwal Orcid Logo, Luke T. Hudson Orcid Logo, Devin W. Laurence, Chung-Hao Lee Orcid Logo, Sanjay Pant Orcid Logo

Journal of the Mechanical Behavior of Biomedical Materials, Volume: 138, Start page: 105657

Swansea University Authors: Ankush Aggarwal Orcid Logo, Sanjay Pant Orcid Logo

  • 62267_VoR.pdf

    PDF | Version of Record

    © 2023 The Author(s). This is an open access article under the CC BY license

    Download (3.61MB)

Check full text

DOI (Published version): 10.1016/j.jmbbm.2023.105657

Abstract

A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be...

Full description

Published in: Journal of the Mechanical Behavior of Biomedical Materials
ISSN: 1751-6161
Published: Elsevier BV 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa62267
first_indexed 2023-01-05T12:03:46Z
last_indexed 2023-02-02T04:16:49Z
id cronfa62267
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2023-02-01T16:21:38.3281685</datestamp><bib-version>v2</bib-version><id>62267</id><entry>2023-01-05</entry><title>A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves</title><swanseaauthors><author><sid>33985d0c2586398180c197dc170d7d19</sid><ORCID>0000-0002-1755-8807</ORCID><firstname>Ankush</firstname><surname>Aggarwal</surname><name>Ankush Aggarwal</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>43b388e955511a9d1b86b863c2018a9f</sid><ORCID>0000-0002-2081-308X</ORCID><firstname>Sanjay</firstname><surname>Pant</surname><name>Sanjay Pant</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2023-01-05</date><abstract>A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. 46 samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May&#x2013;Newman model was the most probable model for the porcine aortic valve data. When the samples were grouped into different cusp types, the May&#x2013;Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee&#x2013;Sacks model and the May&#x2013;Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode&#x2019;s amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to any tissue type, and has the potential to provide a structured and rational way of making simulations population-based.</abstract><type>Journal Article</type><journal>Journal of the Mechanical Behavior of Biomedical Materials</journal><volume>138</volume><journalNumber/><paginationStart>105657</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1751-6161</issnPrint><issnElectronic/><keywords>Soft-tissue; Aortic valve; Constitutive model; Model selection; Bayesian; Biomechanics</keywords><publishedDay>1</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-02-01</publishedDate><doi>10.1016/j.jmbbm.2023.105657</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm/><funders>This work was supported by grant EP/P018912/2 from the Engineering and Physical Sciences Research Council of the UK and grant R01 HL159475 from the National Institutes of Health, United States.</funders><projectreference/><lastEdited>2023-02-01T16:21:38.3281685</lastEdited><Created>2023-01-05T12:02:02.2332867</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Ankush</firstname><surname>Aggarwal</surname><orcid>0000-0002-1755-8807</orcid><order>1</order></author><author><firstname>Luke T.</firstname><surname>Hudson</surname><orcid>0000-0003-4518-5531</orcid><order>2</order></author><author><firstname>Devin W.</firstname><surname>Laurence</surname><order>3</order></author><author><firstname>Chung-Hao</firstname><surname>Lee</surname><orcid>0000-0002-8019-3329</orcid><order>4</order></author><author><firstname>Sanjay</firstname><surname>Pant</surname><orcid>0000-0002-2081-308X</orcid><order>5</order></author></authors><documents><document><filename>62267__26449__4c537960f3f642eaa00b992a3af6a49e.pdf</filename><originalFilename>62267_VoR.pdf</originalFilename><uploaded>2023-02-01T16:20:08.0990173</uploaded><type>Output</type><contentLength>3787178</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2023 The Author(s). This is an open access article under the CC BY license</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2023-02-01T16:21:38.3281685 v2 62267 2023-01-05 A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves 33985d0c2586398180c197dc170d7d19 0000-0002-1755-8807 Ankush Aggarwal Ankush Aggarwal true false 43b388e955511a9d1b86b863c2018a9f 0000-0002-2081-308X Sanjay Pant Sanjay Pant true false 2023-01-05 A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. 46 samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May–Newman model was the most probable model for the porcine aortic valve data. When the samples were grouped into different cusp types, the May–Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee–Sacks model and the May–Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode’s amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to any tissue type, and has the potential to provide a structured and rational way of making simulations population-based. Journal Article Journal of the Mechanical Behavior of Biomedical Materials 138 105657 Elsevier BV 1751-6161 Soft-tissue; Aortic valve; Constitutive model; Model selection; Bayesian; Biomechanics 1 2 2023 2023-02-01 10.1016/j.jmbbm.2023.105657 COLLEGE NANME COLLEGE CODE Swansea University This work was supported by grant EP/P018912/2 from the Engineering and Physical Sciences Research Council of the UK and grant R01 HL159475 from the National Institutes of Health, United States. 2023-02-01T16:21:38.3281685 2023-01-05T12:02:02.2332867 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ankush Aggarwal 0000-0002-1755-8807 1 Luke T. Hudson 0000-0003-4518-5531 2 Devin W. Laurence 3 Chung-Hao Lee 0000-0002-8019-3329 4 Sanjay Pant 0000-0002-2081-308X 5 62267__26449__4c537960f3f642eaa00b992a3af6a49e.pdf 62267_VoR.pdf 2023-02-01T16:20:08.0990173 Output 3787178 application/pdf Version of Record true © 2023 The Author(s). This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
spellingShingle A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
Ankush Aggarwal
Sanjay Pant
title_short A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
title_full A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
title_fullStr A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
title_full_unstemmed A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
title_sort A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: Application to porcine aortic valves
author_id_str_mv 33985d0c2586398180c197dc170d7d19
43b388e955511a9d1b86b863c2018a9f
author_id_fullname_str_mv 33985d0c2586398180c197dc170d7d19_***_Ankush Aggarwal
43b388e955511a9d1b86b863c2018a9f_***_Sanjay Pant
author Ankush Aggarwal
Sanjay Pant
author2 Ankush Aggarwal
Luke T. Hudson
Devin W. Laurence
Chung-Hao Lee
Sanjay Pant
format Journal article
container_title Journal of the Mechanical Behavior of Biomedical Materials
container_volume 138
container_start_page 105657
publishDate 2023
institution Swansea University
issn 1751-6161
doi_str_mv 10.1016/j.jmbbm.2023.105657
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. 46 samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May–Newman model was the most probable model for the porcine aortic valve data. When the samples were grouped into different cusp types, the May–Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee–Sacks model and the May–Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode’s amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to any tissue type, and has the potential to provide a structured and rational way of making simulations population-based.
published_date 2023-02-01T14:21:31Z
_version_ 1821325018022281216
score 11.04776

Similar Items