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Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework

Mohammad Maghsoudi-Ganjeh, Crystal A. Mariano, Samaneh Sattari, Hari Arora Orcid Logo, Mona Eskandari

Frontiers in Bioengineering and Biotechnology, Volume: 9

Swansea University Author: Hari Arora Orcid Logo

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DOI (Published version): 10.3389/fbioe.2021.684778

Abstract

Pulmonary diseases, driven by pollution, industrial farming, vaping, and the infamous COVID-19 pandemic, lead morbidity and mortality rates worldwide. Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hi...

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Published in: Frontiers in Bioengineering and Biotechnology
ISSN: 2296-4185
Published: Frontiers Media SA 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58493
first_indexed 2021-10-28T12:51:51Z
last_indexed 2023-01-11T14:39:07Z
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Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hindered by the lung&#x2019;s complex and hierarchical structure, and the lack of mechanical experiments linking the load-bearing organ-level response to local behaviors. In this study we address these impedances by introducing a novel reduced-order surface model of the lung, combining the response of the intricate bronchial network, parenchymal tissue, and visceral pleura. The inverse finite element analysis (IFEA) framework is developed using 3-D digital image correlation (DIC) from experimentally measured non-contact strains and displacements from an ex-vivo porcine lung specimen for the first time. A custom-designed inflation device is employed to uniquely correlate the multiscale classical pressure-volume bulk breathing measures to local-level deformation topologies and principal expansion directions. Optimal material parameters are found by minimizing the error between experimental and simulation-based lung surface displacement values, using both classes of gradient-based and gradient-free optimization algorithms and by developing an adjoint formulation for efficiency. The heterogeneous and anisotropic characteristics of pulmonary breathing are represented using various hyperelastic continuum formulations to divulge compound material parameters and evaluate the best performing model. While accounting for tissue anisotropy with fibers assumed along medial-lateral direction did not benefit model calibration, allowing for regional material heterogeneity enabled accurate reconstruction of lung deformations when compared to the homogeneous model. The proof-of-concept framework established here can be readily applied to investigate the impact of assorted organ-level ventilation strategies on local pulmonary force and strain distributions, and to further explore how diseased states may alter the load-bearing material behavior of the lung. 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spelling 2022-10-31T19:13:18.8716064 v2 58493 2021-10-28 Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework ed7371c768e9746008a6807f9f7a1555 0000-0002-9790-0907 Hari Arora Hari Arora true false 2021-10-28 EAAS Pulmonary diseases, driven by pollution, industrial farming, vaping, and the infamous COVID-19 pandemic, lead morbidity and mortality rates worldwide. Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hindered by the lung’s complex and hierarchical structure, and the lack of mechanical experiments linking the load-bearing organ-level response to local behaviors. In this study we address these impedances by introducing a novel reduced-order surface model of the lung, combining the response of the intricate bronchial network, parenchymal tissue, and visceral pleura. The inverse finite element analysis (IFEA) framework is developed using 3-D digital image correlation (DIC) from experimentally measured non-contact strains and displacements from an ex-vivo porcine lung specimen for the first time. A custom-designed inflation device is employed to uniquely correlate the multiscale classical pressure-volume bulk breathing measures to local-level deformation topologies and principal expansion directions. Optimal material parameters are found by minimizing the error between experimental and simulation-based lung surface displacement values, using both classes of gradient-based and gradient-free optimization algorithms and by developing an adjoint formulation for efficiency. The heterogeneous and anisotropic characteristics of pulmonary breathing are represented using various hyperelastic continuum formulations to divulge compound material parameters and evaluate the best performing model. While accounting for tissue anisotropy with fibers assumed along medial-lateral direction did not benefit model calibration, allowing for regional material heterogeneity enabled accurate reconstruction of lung deformations when compared to the homogeneous model. The proof-of-concept framework established here can be readily applied to investigate the impact of assorted organ-level ventilation strategies on local pulmonary force and strain distributions, and to further explore how diseased states may alter the load-bearing material behavior of the lung. In the age of a respiratory pandemic, advancing our understanding of lung biomechanics is more pressing than ever before. Journal Article Frontiers in Bioengineering and Biotechnology 9 Frontiers Media SA 2296-4185 lung mechanics, inverse finite element analysis, digital image correlation (DIC), heterogeneity,anisotropy, hyperelasticity, in-silico ventilation 26 10 2021 2021-10-26 10.3389/fbioe.2021.684778 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University Dassault Systèmes U.S. Foundation Grant and the Hellman Fellows Program to ME, the Sêr Cymru programme, Welsh Government for supporting HA, and the Global Wales International Research Mobility Fund (UNIW/RMF-SU/03) 2022-10-31T19:13:18.8716064 2021-10-28T13:44:14.4964489 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Mohammad Maghsoudi-Ganjeh 1 Crystal A. Mariano 2 Samaneh Sattari 3 Hari Arora 0000-0002-9790-0907 4 Mona Eskandari 5 58493__21335__90f0dbe702a44478922b1d0159eee132.pdf 58493.pdf 2021-10-28T13:50:49.3129669 Output 2568476 application/pdf Version of Record true © 2021 Maghsoudi-Ganjeh, Mariano, Sattari, Arora and Eskandari. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) true eng https://creativecommons.org/licenses/by/4.0/
title Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework
spellingShingle Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework
Hari Arora
title_short Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework
title_full Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework
title_fullStr Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework
title_full_unstemmed Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework
title_sort Developing a Lung Model in the Age of COVID-19: A Digital Image Correlation and Inverse Finite Element Analysis Framework
author_id_str_mv ed7371c768e9746008a6807f9f7a1555
author_id_fullname_str_mv ed7371c768e9746008a6807f9f7a1555_***_Hari Arora
author Hari Arora
author2 Mohammad Maghsoudi-Ganjeh
Crystal A. Mariano
Samaneh Sattari
Hari Arora
Mona Eskandari
format Journal article
container_title Frontiers in Bioengineering and Biotechnology
container_volume 9
publishDate 2021
institution Swansea University
issn 2296-4185
doi_str_mv 10.3389/fbioe.2021.684778
publisher Frontiers Media SA
college_str Faculty of Science and Engineering
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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 Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
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description Pulmonary diseases, driven by pollution, industrial farming, vaping, and the infamous COVID-19 pandemic, lead morbidity and mortality rates worldwide. Computational biomechanical models can enhance predictive capabilities to understand fundamental lung physiology; however, such investigations are hindered by the lung’s complex and hierarchical structure, and the lack of mechanical experiments linking the load-bearing organ-level response to local behaviors. In this study we address these impedances by introducing a novel reduced-order surface model of the lung, combining the response of the intricate bronchial network, parenchymal tissue, and visceral pleura. The inverse finite element analysis (IFEA) framework is developed using 3-D digital image correlation (DIC) from experimentally measured non-contact strains and displacements from an ex-vivo porcine lung specimen for the first time. A custom-designed inflation device is employed to uniquely correlate the multiscale classical pressure-volume bulk breathing measures to local-level deformation topologies and principal expansion directions. Optimal material parameters are found by minimizing the error between experimental and simulation-based lung surface displacement values, using both classes of gradient-based and gradient-free optimization algorithms and by developing an adjoint formulation for efficiency. The heterogeneous and anisotropic characteristics of pulmonary breathing are represented using various hyperelastic continuum formulations to divulge compound material parameters and evaluate the best performing model. While accounting for tissue anisotropy with fibers assumed along medial-lateral direction did not benefit model calibration, allowing for regional material heterogeneity enabled accurate reconstruction of lung deformations when compared to the homogeneous model. The proof-of-concept framework established here can be readily applied to investigate the impact of assorted organ-level ventilation strategies on local pulmonary force and strain distributions, and to further explore how diseased states may alter the load-bearing material behavior of the lung. In the age of a respiratory pandemic, advancing our understanding of lung biomechanics is more pressing than ever before.
published_date 2021-10-26T14:10:15Z
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