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

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.

Mouse lung mechanical properties under varying inflation volumes and cycling frequencies

“…The increase in static compliance is unexpected because it indicates a softer lung that is easier to inflate at higher volumes. Lung tissue is known to experience strain-stiffening which would cause a decrease in static compliance, a stiffer lung, at higher inflation volumes 40 , 41 ; this effect has been demonstrated on the surface of the murine lung during inflation at 0.7 and 0.9 ml using digital image correlation, which allows analysis of simultaneous global and local behavior 42 , 43 . However, increasing the internal space available within the lung, as caused by the opening of a secondary (daughter) set of alveoli, would create more regions for air migration, alleviating the excess strain on alveoli and thus increasing the static compliance 36 , 44 .…”

Examining lung mechanical strains as influenced by breathing volumes and rates using experimental digital image correlation

“…Given the similarity between porcine and human lung size and anatomy, findings can help support studies aimed at lowering the risk of ventilation injuries and complications [ 35 ]. Furthermore, the variable inflation volume and breathing rate data collected in this work can inform finite element computational models to enable predictive ventilation techniques [ 36 ].…”

Lung Mechanics: A Review of Solid Mechanical Elasticity in Lung Parenchyma

“…Investigations into the physiological and mechanical functions of the lung have since been rejuvenated due to the recent global Covid-19 pandemic. Computational mechanical models have the ability to enhance the predictive capabilities and determine more precise physiological estimates of lung properties, however, most of these studies are impeded by the lung’s complex mechanical responses and structural networks [ 117 ]. There is also an absence of mechanical experiments linking the load-bearing organ-level response to regional tissue behaviours.…”

“…Maghsoudi-Ganjeh et al. [ 117 ] address these shortcomings by introducing a novel reduced-order surface model of the lung, which incorporates the mechanical response of the bronchial network, parenchymal tissue, and the visceral pleura. Specifically, they provide the first inverse finite element analysis to computationally characterise the entire lung, supported through digital image correlation (DIC) analysis, resulting from applied pressure-volume loading [ 117 ].…”

“…[ 117 ] address these shortcomings by introducing a novel reduced-order surface model of the lung, which incorporates the mechanical response of the bronchial network, parenchymal tissue, and the visceral pleura. Specifically, they provide the first inverse finite element analysis to computationally characterise the entire lung, supported through digital image correlation (DIC) analysis, resulting from applied pressure-volume loading [ 117 ]. Inverse finite element analysis yields specimen-specific mechanical properties by minimising the error between displacement values approximated through general finite element methods and those determined through experimental techniques [ 111 , 117 ].…”