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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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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58493
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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 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.
Keywords: lung mechanics, inverse finite element analysis, digital image correlation (DIC), heterogeneity,anisotropy, hyperelasticity, in-silico ventilation
College: College of Engineering
Funders: 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)