Constituent-specific material behavior of soft biological tissue: experimental quantification and numerical identification for lung parenchyma

Birzle, Anna M.; Hobrack, Sophie M. K.; Martin, Christian; Uhlig, Stefan; Wall, Wolfgang A.

doi:10.1007/s10237-019-01151-3

Cited by 20 publications

(32 citation statements)

References 74 publications

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“…Early organ-scale studies have investigated the volume-pressure characteristic of the lung in saline (Ardila et al, 1974;Hoppin et al, 1975;Forrest, 1976) and air filled pulmonary specimens (Bayliss and Robertson, 1938;Mead et al, 1957). These works have been siloed from the material characterization of the airways (Eskandari et al, 2018) and parenchyma (Stamenovic and Yager, 1988;Birzle et al, 2019) at the tissue (Suki et al, 2005) or even microstructural scale (Eskandari et al, 2019). Furthermore, recent studies have recognized the important interplay between local tissue strains as it relates to mechanical overventilation in acute respiratory distress syndrome (Hurtado et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Novel Mechanical Strain Characterization of Ventilated ex vivo Porcine and Murine Lung using Digital Image Correlation

et al. 2020

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Respiratory illnesses, such as bronchitis, emphysema, asthma, and COVID-19, substantially remodel lung tissue, deteriorate function, and culminate in a compromised breathing ability. Yet, the structural mechanics of the lung is significantly understudied. Classical pressure-volume air or saline inflation studies of the lung have attempted to characterize the organ’s elasticity and compliance, measuring deviatory responses in diseased states; however, these investigations are exclusively limited to the bulk composite or global response of the entire lung and disregard local expansion and stretch phenomena within the lung lobes, overlooking potentially valuable physiological insights, as particularly related to mechanical ventilation. Here, we present a method to collect the first non-contact, full-field deformation measures of ex vivo porcine and murine lungs and interface with a pressure-volume ventilation system to investigate lung behavior in real time. We share preliminary observations of heterogeneous and anisotropic strain distributions of the parenchymal surface, associative pressure-volume-strain loading dependencies during continuous loading, and consider the influence of inflation rate and maximum volume. This study serves as a crucial basis for future works to comprehensively characterize the regional response of the lung across various species, link local strains to global lung mechanics, examine the effect of breathing frequencies and volumes, investigate deformation gradients and evolutionary behaviors during breathing, and contrast healthy and pathological states. Measurements collected in this framework ultimately aim to inform predictive computational models and enable the effective development of ventilators and early diagnostic strategies.

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Section: Introductionmentioning

confidence: 99%

Novel Mechanical Strain Characterization of Ventilated ex vivo Porcine and Murine Lung using Digital Image Correlation

et al. 2020

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“…This liquid film has a surface tension that is moderated by pulmonary surfactant and, along with the properties of the ECM, is an important determinant of the mechanical behaviour of the lungs. The properties of different components of the lung parenchyma were studied by Birzle et al [ 9 ], who treated rodent parenchymal slices with collagenase and elastase to remove the collagen and elastin, respectively. The authors proposed that the lung parenchyma’s mechanical behaviour is determined by four components: collagen, elastin, ground substance (proteoglycans), and fibre interaction between collagen and elastin, and quantified the contribution of each component by treating the tissue with the appropriate enzyme.…”

Section: Anatomy Of the Respiratory Systemmentioning

confidence: 99%

“…18-FDG, [ 18 F]-fluoro-2-deoxy- d -glucose; note that there is lower uptake of 18-FDG and lower radioactivity in regions with poor aeration in the PET scan. Images are taken from [ 8 , 9 , 12 , 54 – 60 ] with permission. …”

Section: Data Acquisitionmentioning

confidence: 99%

“…[96][97][98][99]); however, given the scope of this review, we will focus on studies that used ex vivo data to develop material models for parenchymal tissues. Birzle et al [9,25,100,101] measured the viscoelastic mechanical properties of lung parenchyma in uniaxial tension and developed a structurally based material model, differentiating between the contributions of different components of the parenchyma (including collagen and elastin fibres) to tissue-level behaviour. The mechanical testing of the parenchyma at the tissue level is complemented by its mechanical characterization at the alveolar wall level, which together could provide an understanding of force transition in the parenchyma across length scales once combined with modelling.…”

Section: Mechanical Testing Of the Parenchyma At Tissue And Alveolar ...mentioning

confidence: 99%

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Computational lung modelling in respiratory medicine

Neelakantan

Xin

Gaver

et al. 2022

J. R. Soc. Interface.

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Computational modelling of the lungs is an active field of study that integrates computational advances with lung biophysics, biomechanics, physiology and medical imaging to promote individualized diagnosis, prognosis and therapy evaluation in lung diseases. The complex and hierarchical architecture of the lung offers a rich, but also challenging, research area demanding a cross-scale understanding of lung mechanics and advanced computational tools to effectively model lung biomechanics in both health and disease. Various approaches have been proposed to study different aspects of respiration, ranging from compartmental to discrete micromechanical and continuum representations of the lungs. This article reviews several developments in computational lung modelling and how they are integrated with preclinical and clinical data. We begin with a description of lung anatomy and how different tissue components across multiple length scales affect lung mechanics at the organ level. We then review common physiological and imaging data acquisition methods used to inform modelling efforts. Building on these reviews, we next present a selection of model-based paradigms that integrate data acquisitions with modelling to understand, simulate and predict lung dynamics in health and disease. Finally, we highlight possible future directions where computational modelling can improve our understanding of the structure–function relationship in the lung.

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“…While DIC describes the kinematics, inverse finite element analysis (IFEA) can be employed to divulge the kinetics. IFEA yields specimen mechanical properties by minimizing the error between the displacements predicted by the Finite Element (FE) model and those measured via experiment ( Birzle et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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

Maghsoudi-Ganjeh

Mariano

Sattari

et al. 2021

Front. Bioeng. Biotechnol.

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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.

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Constituent-specific material behavior of soft biological tissue: experimental quantification and numerical identification for lung parenchyma

Cited by 20 publications

References 74 publications

Novel Mechanical Strain Characterization of Ventilated ex vivo Porcine and Murine Lung using Digital Image Correlation

Novel Mechanical Strain Characterization of Ventilated ex vivo Porcine and Murine Lung using Digital Image Correlation

Computational lung modelling in respiratory medicine

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

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