Coupled and reduced dimensional modeling of respiratory mechanics during spontaneous breathing

Investigation of inert gas washout methods in a new numerical model based on an electrical analogy

Inert gas washout methods have been shown to detect pathological changes in the small airways that occur in the early stages of obstructive lung diseases such as asthma and COPD. Numerical lung models support the analysis of characteristic washout curves, but are limited in their ability to simulate the complexity of lung anatomy over an appropriate time period. Therefore, the interpretation of patient-specific washout data remains a challenge. A new numerical lung model is presented in which electrical components describe the anatomical and physiological characteristics of the lung as well as gas-specific properties. To verify that the model is able to reproduce characteristic washout curves, the phase 3 slopes (S3) of helium washouts are simulated using simple asymmetric lung anatomies consisting of two parallel connected lung units with volume ratios of1.25 0.75 ,1.50 0.50 , and1.75 0.25 and a total volume flow of 250 ml/s which are evaluated for asymmetries in both the convection- and diffusion-dominated zone of the lung. The results show that the model is able to reproduce the S3 for helium and thus the processes underlying the washout methods, so that electrical components can be used to model these methods. This approach could form the basis of a hardware-based real-time simulator.

1D Network computational fluid dynamics for evaluating regional pressures in subjects with cement dust exposure

Cement dust is a primary contributor to air pollution and is responsible for causing numerous respiratory diseases. The impact of cement dust exposure on the respiratory health of residents is increasing owing to the demand for construction associated with urbanization. Long-term inhalation of cement dust leads to a reduction in lung function, alterations in airway structure, increased inhalation and exhalation resistance, and heightened work of breath. In this study, we investigated the effects of cement dust exposure on lung function based on the pulmonary function test (PFT) and one-dimensional computational fluid dynamics (1D CFD). Statistical tests were performed to address the disparity of airway function between healthy and cement dust-exposed participants. The percent predicted values of forced vital capacity percent (FVC%) and forced expiratory volume in 1 s (FEV1%) were found to be decreased in the group of dust-exposed participants. An elevation of regional pressure variation was found in cement dust-exposed airways during both inhalation and exhalation that was associated with alternations of airway structural features therein. The 1D CFD model is beneficial for a cost-effective estimation of airway regional pressure and provides valuable insights for more precise diagnosis and treatment planning in individuals exposed to cement dust.

Subject-Specific Multi-Scale Modeling of the Fate of Inhaled Aerosols

Determining the fate of inhaled aerosols in the respiratory system is essential in assessing the potential toxicity of inhaled airborne materials, responses to airborne pathogens, or in improving inhaled drug delivery. The availability of high-resolution clinical lung imaging and advances in the reconstruction of lung airways from CT images have led to the development of subject-specific in-silico 3D models of aerosol dosimetry, often referred to as computational fluid-particle-dynamics (CFPD) models. As CFPD models require extensive computing resources, they are typically confined to the upper and large airways. These models can be combined with lower-dimensional models to form multiscale models that predict the transport and deposition of inhaled aerosols in the entire respiratory tract. Understanding where aerosols deposit is only the first of potentially several key events necessary to predict an outcome, being a detrimental health effect or a therapeutic response. To that end, multiscale approaches that combine CFPD with physiologically-based pharmacokinetics (PBPK) models have been developed to evaluate the absorption, distribution, metabolism, and excretion (ADME) of toxic or medicinal chemicals in one or more compartments of the human body. CFPD models can also be combined with host cell dynamics (HCD) models to assess regional immune system responses. This paper reviews the state of the art of these different multiscale approaches and discusses the potential role of personalized or subject-specific modeling in respiratory health.

A personalised computational model of the impact of COVID-19 on lung function under mechanical ventilation

This work proposes a modelling framework to analyse flow and pressure distributions throughout the lung of mechanically ventilated COVID-19 patients. The methodology involves: segmentation of the lungs and major airways from patient CT images; a volume filling algorithm that creates a dichotomous airway network in the remaining volume of the lung; an estimate of resistance and compliance within the lung based on Hounsfield unit values from the CT scan; and a computational fluid dynamics model to analyse flow, lung inflation, and pressure throughout the airway network. Mechanically ventilated patients with differing progression and severity of the disease were simulated. The results indicate that the flow distribution within the lung can be significantly affected when there are competing types of lung damage. These competing types are primarily fibrosis-like lung damage that creates higher resistance and lower compliance in that region; and emphysema, which causes a decrease in resistance and increase in compliance. In a patient with severe disease, the model predicted an increase in inflation by 33% in an area affected by emphysema-like conditions. This could increase the risk of alveolar rupture. The framework could readily be adapted to study other respiratory diseases. Early interventions in critical respiratory care could be facilitated through such efficient patient-specific modelling approaches.

Non-local impact of distal airway constrictions on patterns of inhaled particle deposition

Airway constriction and blockage in obstructive lung diseases cause ventilation heterogeneity and create barriers to effective drug deposition. Established computational particle-deposition models have not accounted for these impacts of disease. We present a new particle-deposition model that calculates ventilation based on the resistance of each airway, such that ventilation responds to airway constriction. The model incorporates distal airway constrictions representative of cystic fibrosis, allowing us to investigate the resulting impact on patterns of deposition. Unlike previous models, our model predicts how constrictions affect deposition in airways throughout the lungs, not just in the constricted airways. Deposition is reduced in airways directly distal and proximal to constrictions. When constrictions are clustered together, central-airways deposition can increase significantly in regions away from constrictions, but distal-airways deposition in those regions remains largely unchanged. We use our model to calculate lung clearance index (LCI), a clinical measure of ventilation heterogeneity, after applying constrictions of varying severities in one lobe. We find an increase in LCI coinciding with significantly reduced deposition in the affected lobe. Our results show how the model provides a framework for development of computational tools that capture the impacts of airway disease, which could significantly affect predictions of regional dosing.

Digital twins for chronic lung diseases

Digital twins have recently emerged in healthcare. They combine advances in cyber-physical systems, modelling and computation techniques, and enable a bidirectional flow of information between the physical and virtual entities. In respiratory medicine, progress in connected devices and artificial intelligence make it technically possible to obtain digital twins that allow real-time visualisation of a patient's respiratory health. Advances in respiratory system modelling also enable the development of digital twins that could be used to predict the effectiveness of different therapeutic approaches for a patient. For researchers, digital twins could lead to a better understanding of the gene-environment-time interactions involved in the development of chronic respiratory diseases. For clinicians and patients, they could facilitate personalised and timely medicine, by enabling therapeutic adaptations specific to each patient and early detection of disease progression. The objective of this review is to allow the reader to explore the concept of digital twins, their feasibility in respiratory medicine, their potential benefits and the challenges to their implementation.

Design and comparison of computationally efficient uniaxial stress-strain models of the lung parenchyma for real-time applications

Real-time clinical applications such as robotic lung surgery, tumor localization, atelectasis diagnosis, tumor motion prediction for radiation therapy of lung cancer, or surgery training are in need of biomechanical models of lungs, not necessarily highly accurate, but with good computational properties. These properties can include one or several of the following: low computation time, low memory resource requirement, a low number of parameters, and ease of parameter identification in real-time. Among the numerous existing models of lung parenchyma, some may be well suited for real-time applications; however, they should be extensively assessed against both accuracy and computational efficiency criteria to make an informed choice depending on the requirements of the application. After demonstrating how to derive a real-time compliant force-indentation model from a unixial stress-strain model with rational expression, the core purpose of this paper is to propose such an evaluation of selected models in fitting human lung parenchyma experimental and synthetic data of uniaxial tension. Furthermore, new uniaxial stress-strain models are developed based on an empirical observation of the volumetric behavior of the lungs along with an emphasis on computational performance. These new proposed models are competitive with existing one in terms of computational efficiency and compliance with experimental and synthetic data. One of them reduces the prediction error by 2 compared to other investigated models while maintaining an excellent adjusted coefficient of determination between 0.999 and 1 across various datasets. It exhibits excellent real-time capabilities with an explicit rational expression, only 3 parameters and linear numerator and denominator in the parameters. It is computed with only 20 floating point operations (flops) while another proposed model even requires as few as 2 flops.

The utility of hybrid in silico models of airflow and aerosol dosimetry in the lung

The development and application of multi-scale models of the lung has significantly increased in recent years. These hybrid models merge realistic representations of the larger airways with lower-dimensional descriptions of the bronchioles and respiratory airways. Due to recent advancements, it is possible to calculate airflow and dosimetry throughout the entire lung, enabling model validation with human or animal data. Here, we present a hybrid modeling pipeline and corresponding characteristic airflow and particle deposition hotspots. Next, we discuss the limitations of current hybrid models, including the need to update lower-dimensional deposition function descriptions to better represent realistic airway geometries. Future directions should include modeling diseased lungs and use of machine learning to predict whole lung dosimetry maps for a wider population.

Multi-scale simulations of pulmonary airflow based on a coupled 3D-1D-0D model

Pulmonary airflow simulation is a valuable tool for studying respiratory function and disease. However, the respiratory system is a complex multiscale system that involves various physical and biological processes across different spatial and temporal scales. In this study, we propose a 3D-1D-0D multiscale method for simulating pulmonary airflow, which integrates different levels of detail and complexity of the respiratory system. The method consists of three components: a 3D computational fluid dynamics model for the airflow in the trachea and bronchus, a 1D pipe model for the airflow in the terminal bronchioles, and a 0D biphasic mixture model for the airflow in the respiratory bronchioles and alveoli coupled with the lung deformation. The coupling between the different components is achieved by satisfying the mass and momentum conservation law and the pressure continuity condition at the interfaces. We demonstrate the validity and applicability of our method by comparing the results with data of previous models. We also investigate the reduction in inhaled air volume due to the pulmonary fibrosis using the developed multiscale model. Our method provides a comprehensive and realistic framework for simulating pulmonary airflow and can potentially facilitate the diagnosis and treatment of respiratory diseases.

Pressure- and time-dependent alveolar recruitment/derecruitment in a spatially resolved patient-specific computational model for injured human lungs

We present a novel computational model for the dynamics of alveolar recruitment/derecruitment (RD), which reproduces the underlying characteristics typically observed in injured lungs. The basic idea is a pressure- and time-dependent variation of the stress-free reference volume in reduced dimensional viscoelastic elements representing the acinar tissue. We choose a variable reference volume triggered by critical opening and closing pressures in a time-dependent manner from a straightforward mechanical point of view. In the case of (partially and progressively) collapsing alveolar structures, the volume available for expansion during breathing reduces and vice versa, eventually enabling consideration of alveolar collapse and reopening in our model. We further introduce a method for patient-specific determination of the underlying critical parameters of the new alveolar RD dynamics when integrated into the tissue elements, referred to as terminal units, of a spatially resolved physics-based lung model that simulates the human respiratory system in an anatomically correct manner. Relevant patient-specific parameters of the terminal units are herein determined based on medical image data and the macromechanical behavior of the lung during artificial ventilation. We test the whole modeling approach for a real-life scenario by applying it to the clinical data of a mechanically ventilated patient. The generated lung model is capable of reproducing clinical measurements such as tidal volume and pleural pressure during various ventilation maneuvers. We conclude that this new model is an important step toward personalized treatment of ARDS patients by considering potentially harmful mechanisms-such as cyclic RD and overdistension-and might help in the development of relevant protective ventilation strategies to reduce ventilator-induced lung injury (VILI).

Full-lung simulations of mechanically ventilated lungs incorporating recruitment/derecruitment dynamics

This study developed and investigated a comprehensive multiscale computational model of a mechanically ventilated ARDS lung to elucidate the underlying mechanisms contributing to the development or prevention of VILI. This model is built upon a healthy lung model that incorporates realistic airway and alveolar geometry, tissue distensibility, and surfactant dynamics. Key features of the ARDS model include recruitment and derecruitment (RD) dynamics, alveolar tissue viscoelasticity, and surfactant deficiency. This model successfully reproduces realistic pressure-volume (PV) behavior, dynamic surface tension, and time-dependent descriptions of RD events as a function of the ventilation scenario. Simulations of Time-Controlled Adaptive Ventilation (TCAV) modes, with short and long durations of exhalation (T Low - and T Low +, respectively), reveal a higher incidence of RD for T Low + despite reduced surface tensions due to interfacial compression. This finding aligns with experimental evidence emphasizing the critical role of timing in protective ventilation strategies. Quantitative analysis of energy dissipation indicates that while alveolar recruitment contributes only a small fraction of total energy dissipation, its spatial concentration and brief duration may significantly contribute to VILI progression due to its focal nature and higher intensity. Leveraging the computational framework, the model may be extended to facilitate the development of personalized protective ventilation strategies to enhance patient outcomes. As such, this computational modeling approach offers valuable insights into the complex dynamics of VILI that may guide the optimization of ventilation strategies in ARDS management.

A coupled model for the dynamics of gas exchanges in the human lung with Haldane and Bohr's effects

We propose an integrated dynamical model for oxygen and carbon dioxide transfer from the lung into the blood, coupled with a lumped mechanical model for the ventilation process, for healthy patients as well as in pathological cases. In particular, we take into account the nonlinear interaction between oxygen and carbon dioxide in the blood volume, referred to as the Bohr and Haldane effects. We also propose a definition of the physiological dead space volume (the lung volume that does not contribute to gas exchange) which depends on the pathological state and the breathing scenario. This coupled ventilation-gas diffusion model is driven by the sole action of the respiratory muscles. We analyse its sensitivity with respect to characteristic parameters: the resistance of the bronchial tree, the elastance of the lung tissue and the oxygen and carbon dioxide diffusion coefficients of the alveolo-capillary membrane. Idealized pathological situations are also numerically investigated. We obtain realistic qualitative tendencies, which represent a first step towards classification of the pathological behaviours with respect to the considered input parameters.

An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung

We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient-specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure-driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient-specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.

Fluid dynamic assessment of positive end-expiratory pressure in a tracheostomy tube connector during respiration

High-flow oxygen therapy using a tracheostomy tube is a promising clinical approach to reduce the work of breathing in tracheostomized patients. Positive end-expiratory pressure (PEEP) is usually applied during oxygen inflow to improve oxygenation by preventing end-expiratory lung collapse. However, much is still unknown about the geometrical effects of PEEP, especially regarding tracheostomy tube connectors (or adapters). Quantifying the degree of end-expiratory pressure (EEP) that takes patient-specific spirometry into account would be useful in this regard, but no such framework has been established yet. Thus, a platform to assess PEEP under respiration was developed, wherein three-dimensional simulation of airflow in a tracheostomy tube connector is coupled with a lumped lung model. The numerical model successfully reflected the magnitude of EEP measured experimentally using a lung phantom. Numerical simulations were further performed to quantify the effects of geometrical parameters on PEEP, such as inlet angles and rate of stenosis in the connector. Although sharp inlet angles increased the magnitude of EEP, they cannot be expected to achieve clinically reasonable PEEP. On the other hand, geometrical constriction in the connector can potentially result in PEEP as obtained with conventional nasal cannulae.

Algebraic approximation of the distributed model for the pressure drop in the respiratory airways

The complexity of the human respiratory system causes that one of the main methods of analyzing the dynamic pulmonary phenomena and interpreting experimental results are simulations of its computational models. Among the most compound elements of these models, apart from the bronchial tree structure, is the phenomenon of flow limitation in flexible bronchi, which causes them to collapse with increasing flow, thus their properties, such as resistance, compliance and inertance, are highly nonlinear and time-varying. Commonly, this phenomenon is ignored, or a distributed model for the airway pressure drop is applied, simulated with a modified numerical solver of this differential equation (ODE). The disadvantages of this solution are the problems with taking into account the inherent singularity of the model and the long computation time due to iterative nature of the ODE procedure. The aim of the work was to derive an algebraic approximation of this distributed model, suitable for implementation in continuous dynamic models, to validate it by comparing the results of simulations with the respiratory system model including approximate and original (ODE solver) numerical procedures, as well as to evaluate possible acceleration of calculations. All simulations, including spontaneous breathing, mechanical ventilation with the optimal ventilatory waveform and forced expiration, proved that algebraic approximation yielded results negligibly differing from the ODE solution, and shortened the computation time by an order. The proposed approach is an attractive alternative in the case of computer implementations of pulmonary models, where simulations of flows and pressures in the complex respiratory system are of primary importance.

Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19

Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics.

Computational lung modelling in respiratory medicine

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.

Integrative Computational Models of Lung Structure-Function Interactions

Anatomically based integrative models of the lung and their interaction with other key components of the respiratory system provide unique capabilities for investigating both normal and abnormal lung function. There is substantial regional variability in both structure and function within the normal lung, yet it remains capable of relatively efficient gas exchange by providing close matching of air delivery (ventilation) and blood delivery (perfusion) to regions of gas exchange tissue from the scale of the whole organ to the smallest continuous gas exchange units. This is despite remarkably different mechanisms of air and blood delivery, different fluid properties, and unique scale-dependent anatomical structures through which the blood and air are transported. This inherent heterogeneity can be exacerbated in the presence of disease or when the body is under stress. Current computational power and data availability allow for the construction of sophisticated data-driven integrative models that can mimic respiratory system structure, function, and response to intervention. Computational models do not have the same technical and ethical issues that can limit experimental studies and biomedical imaging, and if they are solidly grounded in physiology and physics they facilitate investigation of the underlying interaction between mechanisms that determine respiratory function and dysfunction, and to estimate otherwise difficult-to-access measures. © 2021 American Physiological Society. Compr Physiol 11:1501-1530, 2021.

Investigation of airflow at different activity conditions in a realistic model of human upper respiratory tract

In the present study, the turbulent flows inside a realistic model of the upper respiratory tract were investigated numerically and experimentally. The airway model included the geometrical details of the oral cavity to the end of the trachea that was based on a series of CT-scan images. The topological data of the respiratory tract were used for generating the computational model as well as the 3D-printed model that was used in the experimental pressure drop measurement. Different airflow rates of 30, 45, and 60 L/min, which correspond to the light, semi-light, and heavy activity breathing conditions, were investigated numerically using turbulence and transition models, as well as experimentally. Simulation results for airflow properties, including velocity vectors, pressure drops, streamlines, eddy viscosity, and turbulent kinetic energy contours in the oral-trachea airway model, were presented. The simulated pressure drop was compared with the experimental data, and reasonable agreement was found. The obtained results showed that the maximum pressure drop occurs in the narrowest part of the larynx region. A comparison between the numerical results and experimental data showed that the transition (γ-Reθ) SST model predicts higher pressure losses, especially at higher breathing rates. Formations of the secondary flows in the oropharynx and trachea regions were also observed. In addition, the simulation results showed that in the trachea region, the secondary flow structures dissipated faster for the flow rate of 60 L/min compared to the lower breathing rates of 30 and 45 L/min.

An integrated 1D breathing lung simulation with relative hysteresis of airway structure and regional pressure for healthy and asthmatic human lungs

This study aims to develop a one-dimensional (1D) computational fluid dynamics (CFD) model with dynamic airway geometry that considers airway wall compliance and acinar dynamics. The proposed 1D model evaluates the pressure distribution and the hysteresis between the pressure and tidal volume (V_tidal) in the central and terminal airways for healthy and asthmatic subjects. Four-dimensional CT images were captured at 11-14 time points during the breathing cycle. The airway diameter and length were reconstructed using a volume-filling method and a stochastic model at respective time points. The obtained values for the airway diameter and length were interpolated via the Akima spline to avoid unboundedness. A 1D energy balance equation considering the effects of wall compliance and parenchymal inertance was solved using the efficient aggregation-based algebraic multigrid solver, a sparse matrix solver, reducing the computational costs by around 90% when compared with the generalized minimal residual solver. In the V_tidal versus displacement in the basal direction (z-coordinate), the inspiration curve was lower than the expiration curve, leading to relative hysteresis. The dynamic deformation model was the major factor influencing the difference in the workload in the central and terminal airways. In contrast, wall compliance and parenchymal inertance appeared only marginally to affect the pressure and workload. The integrated 1D model mimicked dynamic deformation by predicting airway diameter and length at each time point, describing the effects of wall compliance and parenchymal inertance. This computationally efficient model could be utilized to assess breathing mechanism as an alternative to pulmonary function tests.NEW & NOTEWORTHY This study introduces a one-dimensional (1D) computational fluid dynamics (CFD) model mimicking the realistic changes in diameter and length in whole airways and reveals differences in lung deformation between healthy and asthmatic subjects. Utilizing computational models, the effects of parenchymal inertance and airway wall compliance are investigated by changing ventilation frequency and airway wall elastance, respectively.

CT-based Airway Flow Model to Assess Ventilation in Chronic Obstructive Pulmonary Disease: A Pilot Study

Background The lack of functional information in thoracic CT remains a limitation of its use in the clinical management of chronic obstructive pulmonary disease (COPD). Purpose To compare the distribution of pulmonary ventilation assessed by a CT-based full-scale airway network (FAN) flow model with hyperpolarized xenon 129 (¹²⁹Xe) MRI (hereafter, ¹²⁹Xe MRI) and technetium 99m-diethylenetriaminepentaacetic acid aerosol SPECT ventilation imaging (hereafter, V-SPECT) in participants with COPD. Materials and Methods In this prospective study performed between May and August 2017, pulmonary ventilation in participants with COPD was computed by using the FAN flow model. The modeled pulmonary ventilation was compared with functional imaging data from breath-hold time-series ¹²⁹Xe MRI and V-SPECT. FAN-derived ventilation images on the coronal plane and volumes of interest were compared with functional lung images. Percentage lobar ventilation estimated by the FAN model was compared with that measured at ¹²⁹Xe MRI and V-SPECT. The statistical significance of ventilation distribution between FAN and functional images was demonstrated with the Spearman correlation coefficient and χ² distance. Results For this study, nine participants (seven men [mean age, 65 years ± 5 {standard deviation}] and two women [mean age, 63 years ± 7]) with COPD that was Global Initiative for Chronic Obstructive Lung Disease stage II-IV were enrolled. FAN-modeled ventilation profile showed strong positive correlation with images from ¹²⁹Xe MRI (ρ = 0.67; P < .001) and V-SPECT (ρ = 0.65; P < .001). The χ² distances of the ventilation histograms in the volumes of interest between the FAN and ¹²⁹Xe MRI and FAN and V-SPECT were 0.16 ± 0.08 and 0.28 ± 0.14, respectively. The ratios of lobar ventilations in the models were linearly correlated to images from ¹²⁹Xe MRI (ρ = 0.67; P < .001) and V-SPECT (ρ = 0.59; P < .001). Conclusion A CT-based full-scale airway network flow model provided regional pulmonary ventilation information for chronic obstructive pulmonary disease and correlates with hyperpolarized xenon 129 MRI and technetium 99m-diethylenetriaminepentaacetic acid aerosol SPECT ventilation imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Schiebler and Parraga in this issue.

Assessing Airflow Sensitivity to Healthy and Diseased Lung Conditions in a Computational Fluid Dynamics Model Validated In Vitro

Computational models are useful for understanding respiratory physiology. Crucial to such models are the boundary conditions specifying the flow conditions at truncated airway branches (terminal flow rates). However, most studies make assumptions about these values, which are difficult to obtain in vivo. We developed a computational fluid dynamics (CFD) model of airflows for steady expiration to investigate how terminal flows affect airflow patterns in respiratory airways. First, we measured in vitro airflow patterns in a physical airway model, using particle image velocimetry (PIV). The measured and computed airflow patterns agreed well, validating our CFD model. Next, we used the lobar flow fractions from a healthy or chronic obstructive pulmonary disease (COPD) subject as constraints to derive different terminal flow rates (i.e., three healthy and one COPD) and computed the corresponding airflow patterns in the same geometry. To assess airflow sensitivity to the boundary conditions, we used the correlation coefficient of the shape similarity (R) and the root-mean-square of the velocity magnitude difference (Drms) between two velocity contours. Airflow patterns in the central airways were similar across healthy conditions (minimum R, 0.80) despite variations in terminal flow rates but markedly different for COPD (minimum R, 0.26; maximum Drms, ten times that of healthy cases). In contrast, those in the upper airway were similar for all cases. Our findings quantify how variability in terminal and lobar flows contributes to airflow patterns in respiratory airways. They highlight the importance of using lobar flow fractions to examine physiologically relevant airflow characteristics.

Integrated lung tissue mechanics one piece at a time: Computational modeling across the scales of biology

The lung is a delicately balanced and highly integrated mechanical system. Lung tissue is continuously exposed to the environment via the air we breathe, making it susceptible to damage. As a consequence, respiratory diseases present a huge burden on society and their prevalence continues to rise. Emergent function is produced not only by the sum of the function of its individual components but also by the complex feedback and interactions occurring across the biological scales - from genes to proteins, cells, tissue and whole organ - and back again. Computational modeling provides the necessary framework for pulling apart and putting back together the pieces of the body and organ systems so that we can fully understand how they function in both health and disease. In this review, we discuss models of lung tissue mechanics spanning from the protein level (the extracellular matrix) through to the level of cells, tissue and whole organ, many of which have been developed in isolation. This is a vital step in the process but to understand the emergent behavior of the lung, we must work towards integrating these component parts and accounting for feedback across the scales, such as mechanotransduction. These interactions will be key to unlocking the mechanisms occurring in disease and in seeking new pharmacological targets and improving personalized healthcare.

1D network simulations for evaluating regional flow and pressure distributions in healthy and asthmatic human lungs

This study aimed to introduce a one-dimensional (1D) computational fluid dynamics (CFD) model for airway resistance and lung compliance to examine the relationship between airway resistance, pressure, and regional flow distribution. We employed five healthy and five asthmatic subjects who had dynamic computed tomography (CT) scans (4D CT) along with two static scans at total lung capacity and functional residual capacity. Fractional air-volume change ( ΔVairf ) from 4D CT was used for a validation of the 1D CFD model. We extracted the diameter ratio from existing data sets of 61 healthy subjects for computing mean and standard deviation (SD) of airway constriction/dilation in CT-resolved airways. The lobar mean (SD) of airway constriction/dilation was used to determine diameters of CT-unresolved airways. A 1D isothermal energy balance equation was solved, and pressure boundary conditions were imposed at the acinar region (model A) or at the pleural region (model B). A static compliance model was only applied for model B to link acinar and pleural regions. The values of 1D CFD-derived ΔVairf for model B demonstrated better correlation with 4D CT-derived ΔVairf than model A. In both inspiration and expiration, asthmatic subjects with airway constriction show much greater pressure drop than healthy subjects without airway constriction. This increased transpulmonary pressures in the asthmatic subjects, leading to an increased workload (hysteresis). The 1D CFD model was found to be useful in investigating flow structure, lung hysteresis, and pressure distribution for healthy and asthmatic subjects. The derived flow distribution could be used for imposing boundary conditions of 3D CFD. NEW & NOTEWORTHY A one-dimensional (1D) computational fluid dynamics (CFD) model for airway resistance and lung compliance was introduced to examine the relationship between airway resistance, pressure, and regional flow distribution. The 1D CFD model investigated differences of flow structure, lung hysteresis, and pressure distribution for healthy and asthmatic subjects. The derived flow distribution could be used for imposing boundary conditions of three-dimensional CFD.

Predicted airway obstruction distribution based on dynamical lung ventilation data: A coupled modeling-machine learning methodology

In asthma and chronic obstructive pulmonary disease, some airways of the tracheobronchial tree can be constricted, from moderate narrowing up to closure. Those pathological patterns of obstructions affect the lung ventilation distribution. While some imaging techniques enable visualization and quantification of constrictions in proximal generations, no noninvasive technique exists to provide the airway morphology and obstruction distribution in distal areas. In this work, we propose a method that exploits lung ventilation measures to access positions of airway obstructions (restrictions and closures) in the tree. This identification approach combines a lung ventilation model, in which a 0D tree is strongly coupled to a 3D parenchyma description, along with a machine learning approach. On the basis of synthetic data generated with typical temporal and spatial resolutions as well as reconstruction errors, we obtain very encouraging results of the obstruction distribution, with a detection rate higher than 85%.

Effect of upper airway on tracheobronchial fluid dynamics

The upper airways play a significant role in the tracheal flow dynamics. Despite many previous studies, however, the effect of the upper airways on the ventilation distribution in distal airways has remained a challenge. The aim of this study is to experimentally and computationally investigate the dynamic behaviour in the intratracheal flow induced by the upper respiratory tract and to assess its influence on the subsequent tributaries. Patient-specific images from 2 different modalities (magnetic resonance imaging of the upper airways and computed tomography of the lower airways) were segmented and combined. An experimental phantom of patient-specific airways (including the oral cavity, larynx, trachea, down to generations 6-8) was generated using 3D printing. The flow velocities in this phantom model were measured by the flow-sensitised phase contrast magnetic resonance imaging technique and compared with the computational fluid dynamics simulations. Both experimental and computational results show a good agreement in the time-averaged velocity fields as well as fluctuating velocity. The flows in the proximal trachea were complex and unsteady under both lower- and higher-flow rate conditions. Computational fluid dynamics simulations were also performed with an airways model without the upper airways. Although the flow near the carina remained unstable only when the inflow rate was high, the influence of the upper airways caused notable changes in distal flow distributions when the 2 airways models were compared with and without the upper airways. The results suggest that the influence of the upper airways should be included in the respiratory flow assessment as the upper airways extensively affect the flows in distal airways and consequent ventilation distribution in the lungs.

Contributions of Kinetic Energy and Viscous Dissipation to Airway Resistance in Pulmonary Inspiratory and Expiratory Airflows in Successive Symmetric Airway Models With Various Bifurcation Angles

The aim of this study was to investigate and quantify contributions of kinetic energy and viscous dissipation to airway resistance during inspiration and expiration at various flow rates in airway models of different bifurcation angles. We employed symmetric airway models up to the 20th generation with the following five different bifurcation angles at a tracheal flow rate of 20 L/min: 15 deg, 25 deg, 35 deg, 45 deg, and 55 deg. Thus, a total of ten computational fluid dynamics (CFD) simulations for both inspiration and expiration were conducted. Furthermore, we performed additional four simulations with tracheal flow rate values of 10 and 40 L/min for a bifurcation angle of 35 deg to study the effect of flow rate on inspiration and expiration. Using an energy balance equation, we quantified contributions of the pressure drop associated with kinetic energy and viscous dissipation. Kinetic energy was found to be a key variable that explained the differences in airway resistance on inspiration and expiration. The total pressure drop and airway resistance were larger during expiration than inspiration, whereas wall shear stress and viscous dissipation were larger during inspiration than expiration. The dimensional analysis demonstrated that the coefficients of kinetic energy and viscous dissipation were strongly correlated with generation number. In addition, the viscous dissipation coefficient was significantly correlated with bifurcation angle and tracheal flow rate. We performed multiple linear regressions to determine the coefficients of kinetic energy and viscous dissipation, which could be utilized to better estimate the pressure drop in broader ranges of successive bifurcation structures.

The prediction of viscous losses and pressure drop in models of the human airways

This paper examines the viscous flow resistance in branching tubes as applied to simplified models of the lungs and compares the results of computational fluid dynamics simulations for a range of conditions with measurement data. The results are in good agreement with the available measurement data for both inspiration and expiration. A detailed sensitivity analysis of the dissipation and viscous resistance in a branch then examines the ratio of the viscous resistance to that for a fully developed Poiseuille flow, Z. As other researchers have noted, the calculated resistances give lower values than those from the standard correlation of Pedley et al. The results demonstrate that the resistance is sensitive to the velocity profile upstream of the bifurcations and explain from fluid dynamical considerations the apparent sensitivity of the resistance to the generation number of the branch. The paper also suggests a revised value for the calibration constant in the expression for Z. Finally, a limited set of results are presented for junction losses, and for expiration.

Capturing complexity in pulmonary system modelling

Respiratory disease is a significant problem worldwide, and it is a problem with increasing prevalence. Pathology in the upper airways and lung is very difficult to diagnose and treat, as response to disease is often heterogeneous across patients. Computational models have long been used to help understand respiratory function, and these models have evolved alongside increases in the resolution of medical imaging and increased capability of functional imaging, advances in biological knowledge, mathematical techniques and computational power. The benefits of increasingly complex and realistic geometric and biophysical models of the respiratory system are that they are able to capture heterogeneity in patient response to disease and predict emergent function across spatial scales from the delicate alveolar structures to the whole organ level. However, with increasing complexity, models become harder to solve and in some cases harder to validate, which can reduce their impact clinically. Here, we review the evolution of complexity in computational models of the respiratory system, including successes in translation of models into the clinical arena. We also highlight major challenges in modelling the respiratory system, while making use of the evolving functional data that are available for model parameterisation and testing.

A tree-parenchyma coupled model for lung ventilation simulation

In this article, we develop a lung ventilation model. The parenchyma is described as an elastic homogenized media. It is irrigated by a space-filling dyadic resistive pipe network, which represents the tracheobronchial tree. In this model, the tree and the parenchyma are strongly coupled. The tree induces an extra viscous term in the system constitutive relation, which leads, in the finite element framework, to a full matrix. We consider an efficient algorithm that takes advantage of the tree structure to enable a fast matrix-vector product computation. This framework can be used to model both free and mechanically induced respiration, in health and disease. Patient-specific lung geometries acquired from computed tomography scans are considered. Realistic Dirichlet boundary conditions can be deduced from surface registration on computed tomography images. The model is compared to a more classical exit compartment approach. Results illustrate the coupling between the tree and the parenchyma, at global and regional levels, and how conditions for the purely 0D model can be inferred. Different types of boundary conditions are tested, including a nonlinear Robin model of the surrounding lung structures.

Computational modeling of tracheal angioedema due to swelling of the submucous tissue layer

Angioedema is a tissue-swelling pathology due to rapid change in soft tissue fluid content. Its occurrence in the trachea is predominantly localized to the soft mucous tissue that forms the innermost tracheal layer. The biomechanical consequences, such as airway constriction, are dependent upon the ensuing mechanical interactions between all of the various tissues that comprise the tracheal tube. We model the stress interactions by treating the trachea organ as a three-tissue system consisting of swellable mucous in conjunction with nonswelling cartilage and nonswelling trachealis musculature. Hyperelastic constitutive modeling is used by generalizing the standard anisotropic, incompressible soft tissue framework to incorporate the swelling effect. Finite element stress analysis then proceeds with swelling of the mucous layer providing the driving factor for the mechanical analysis. The amount of airway constriction is governed by the mechanical interaction between the three predominant tissue types. The detailed stress analysis indicates the presence of stress concentrations near the various tissue junctions. Because of the tissue's nonlinear mechanical behavior, this can lead to material stiffness fluctuations as a function of location on the trachea. Patient specific modeling is presented. The role of the modeling in the interpretation of diagnostic procedures and the assessment of therapies is discussed.

Aerosol transport throughout inspiration and expiration in the pulmonary airways

Little is known about transport throughout the respiration cycle in the conducting airways. It is challenging to appropriately describe the time-dependent number of particles entering back into the model during exhalation. Modeling the entire lung is not feasible; therefore, multidomain methods must be used. Here, we present a new framework that is designed to simulate particles throughout the respiration cycle, incorporating realistic airway geometry and respiration. This framework is applied for a healthy rat lung exposed to  ∼ 1μm diameter particles, chosen to facilitate parameterization and validation. The flow field is calculated in the conducting airways (3D domain) by solving the incompressible Navier-Stokes equations with experimentally derived boundary conditions. Particles are tracked throughout inspiration by solving a modified Maxey-Riley equation. Next, we pass the time-dependent particle concentrations exiting the 3D model to the 1D volume conservation and advection-diffusion models (1D domain). Once the 1D models are solved, we prescribe the time-dependent number of particles entering back into the 3D airways to again solve for 3D transport. The coupled simulations highlight that about twice as many particles deposit during inhalation compared to exhalation for the entire lung. In contrast to inhalation, where most particles deposit at the bifurcation zones, particles deposit relatively uniformly on the gravitationally dependent side of the 3D airways during exhalation. Strong agreement to previously collected regional experimental data is shown, as the 1D models account for lobe-dependent morphology. This framework may be applied to investigate dosimetry in other species and pathological lungs.

Fluid-structure interaction including volumetric coupling with homogenised subdomains for modeling respiratory mechanics

In this article, a novel approach is presented for combining standard fluid-structure interaction with additional volumetric constraints to model fluid flow into and from homogenised solid domains. The proposed algorithm is particularly interesting for investigations in the field of respiratory mechanics as it enables the mutual coupling of airflow in the conducting part and local tissue deformation in the respiratory part of the lung by means of a volume constraint. In combination with a classical monolithic fluid-structure interaction approach, a comprehensive model of the human lung can be established that will be useful to gain new insights into respiratory mechanics in health and disease. To illustrate the validity and versatility of the novel approach, three numerical examples including a patient-specific lung model are presented. The proposed algorithm proves its capability of computing clinically relevant airflow distribution and tissue strain data at a level of detail that is not yet achievable, neither with current imaging techniques nor with existing computational models. Copyright © 2016 John Wiley & Sons, Ltd.

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel “virtual EIT” module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling.

NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future.

A comprehensive computational human lung model incorporating inter-acinar dependencies: Application to spontaneous breathing and mechanical ventilation

In this article, we propose a comprehensive computational model of the entire respiratory system, which allows simulating patient-specific lungs under different ventilation scenarios and provides a deeper insight into local straining and stressing of pulmonary acini. We include novel 0D inter-acinar linker elements to respect the interplay between neighboring alveoli, an essential feature especially in heterogeneously distended lungs. The model is applicable to healthy and diseased patient-specific lung geometries. Presented computations in this work are based on a patient-specific lung geometry obtained from computed tomography data and composed of 60,143 conducting airways, 30,072 acini, and 140,135 inter-acinar linkers. The conducting airways start at the trachea and end before the respiratory bronchioles. The acini are connected to the conducting airways via terminal airways and to each other via inter-acinar linkers forming a fully coupled anatomically based respiratory model. Presented numerical examples include simulation of breathing during a spirometry-like test, measurement of a quasi-static pressure-volume curve using a supersyringe maneuver, and volume-controlled mechanical ventilation. The simulations show that our model incorporating inter-acinar dependencies successfully reproduces physiological results in healthy and diseased states. Moreover, within these scenarios, a deeper insight into local pressure, volume, and flow rate distribution in the human lung is investigated and discussed. Copyright © 2016 John Wiley & Sons, Ltd.

Computationally efficient analysis of particle transport and deposition in a human whole-lung-airway model. Part I: Theory and model validation

Computational predictions of aerosol transport and deposition in the human respiratory tract can assist in evaluating detrimental or therapeutic health effects when inhaling toxic particles or administering drugs. However, the sheer complexity of the human lung, featuring a total of 16 million tubular airways, prohibits detailed computer simulations of the fluid-particle dynamics for the entire respiratory system. Thus, in order to obtain useful and efficient particle deposition results, an alternative modeling approach is necessary where the whole-lung geometry is approximated and physiological boundary conditions are implemented to simulate breathing. In Part I, the present new whole-lung-airway model (WLAM) represents the actual lung geometry via a basic 3-D mouth-to-trachea configuration while all subsequent airways are lumped together, i.e., reduced to an exponentially expanding 1-D conduit. The diameter for each generation of the 1-D extension can be obtained on a subject-specific basis from the calculated total volume which represents each generation of the individual. The alveolar volume was added based on the approximate number of alveoli per generation. A wall-displacement boundary condition was applied at the bottom surface of the first-generation WLAM, so that any breathing pattern due to the negative alveolar pressure can be reproduced. Specifically, different inhalation/exhalation scenarios (rest, exercise, etc.) were implemented by controlling the wall/mesh displacements to simulate realistic breathing cycles in the WLAM. Total and regional particle deposition results agree with experimental lung deposition results. The outcomes provide critical insight to and quantitative results of aerosol deposition in human whole-lung airways with modest computational resources. Hence, the WLAM can be used in analyzing human exposure to toxic particulate matter or it can assist in estimating pharmacological effects of administered drug-aerosols. As a practical WLAM application, the transport and deposition of asthma drugs from a commercial dry-powder inhaler is discussed in Part II.

A poroelastic model coupled to a fluid network with applications in lung modelling

We develop a lung ventilation model based on a continuum poroelastic representation of lung parenchyma that is strongly coupled to a pipe network representation of the airway tree. The continuous system of equations is discretized using a low-order stabilised finite element method. The framework is applied to a realistic lung anatomical model derived from computed tomography data and an artificially generated airway tree to model the conducting airway region. Numerical simulations produce physiologically realistic solutions and demonstrate the effect of airway constriction and reduced tissue elasticity on ventilation, tissue stress and alveolar pressure distribution. The key advantage of the model is the ability to provide insight into the mutual dependence between ventilation and deformation. This is essential when studying lung diseases, such as chronic obstructive pulmonary disease and pulmonary fibrosis. Thus the model can be used to form a better understanding of integrated lung mechanics in both the healthy and diseased states. Copyright © 2015 John Wiley & Sons, Ltd.

Development and Analysis of Patient-Based Complete Conducting Airways Models

The analysis of high-resolution computed tomography (CT) images of the lung is dependent on inter-subject differences in airway geometry. The application of computational models in understanding the significance of these differences has previously been shown to be a useful tool in biomedical research. Studies using image-based geometries alone are limited to the analysis of the central airways, down to generation 6-10, as other airways are not visible on high-resolution CT. However, airways distal to this, often termed the small airways, are known to play a crucial role in common airway diseases such as asthma and chronic obstructive pulmonary disease (COPD). Other studies have incorporated an algorithmic approach to extrapolate CT segmented airways in order to obtain a complete conducting airway tree down to the level of the acinus. These models have typically been used for mechanistic studies, but also have the potential to be used in a patient-specific setting. In the current study, an image analysis and modelling pipeline was developed and applied to a number of healthy (n = 11) and asthmatic (n = 24) CT patient scans to produce complete patient-based airway models to the acinar level (mean terminal generation 15.8 ± 0.47). The resulting models are analysed in terms of morphometric properties and seen to be consistent with previous work. A number of global clinical lung function measures are compared to resistance predictions in the models to assess their suitability for use in a patient-specific setting. We show a significant difference (p < 0.01) in airways resistance at all tested flow rates in complete airway trees built using CT data from severe asthmatics (GINA 3-5) versus healthy subjects. Further, model predictions of airways resistance at all flow rates are shown to correlate with patient forced expiratory volume in one second (FEV1) (Spearman ρ = -0.65, p < 0.001) and, at low flow rates (0.00017 L/s), FEV1 over forced vital capacity (FEV1/FVC) (ρ = -0.58, p < 0.001). We conclude that the pipeline and anatomical models can be used directly in mechanistic modelling studies and can form the basis for future patient-based modelling studies.

Dynamic flow characteristics in normal and asthmatic lungs

Complex flow patterns exist within the asymmetric branching airway network in the lungs. These flow patterns are known to become increasingly heterogeneous during disease as a result of various mechanisms such as bronchoconstriction or alterations in lung tissue compliance. Here, we present a coupled model of tissue deformation and network airflow enabling predictions of dynamic flow properties, including temporal flow rate, pressure distribution, and the occurrence of reverse flows. We created two patient-specific airway geometries, one for a healthy subject and one for a severe asthmatic subject, derived using a combination of high-resolution CT data and a volume-filling branching algorithm. In addition, we created virtually constricted airway geometry by reducing the airway radii of the healthy subject model. The flow model was applied to these three different geometries to solve the pressure and flow distribution over a breathing cycle. The differences in wave phase of the flows in parallel airways induced by asymmetric airway geometry and bidirectional interaction between intra-acinar and airway network pressures were small in central airways but were more evident in peripheral airways. The asthmatic model showed elevated ventilation heterogeneity and significant flow disturbance. The reverse flows in the asthmatic model not only altered the local flow characteristics but also affected total lung resistance. The clinical significance of temporal flow disturbance on lung ventilation in normal airway model is obscure. However, increased flow disturbance and ventilation heterogeneity observed in the asthmatic model suggests that reverse flow may be an important factor for asthmatic lung function.

Toward a comprehensive interpretation of intravital microscopy images in studies of lung tissue dynamics

Intravital microscopy (IVM) is a well-established imaging technique for real-time monitoring of microscale lung tissue dynamics. Although accepted as a gold standard in respiratory research, its characteristic image features are scarcely understood, especially when trying to determine the actual position of alveolar walls. To allow correct interpretation of these images with respect to the true geometry of the lung parenchyma, we analyzed IVM data of alveoli in a mouse model in comparison with simultaneously acquired optical coherence tomography images. Several IVM characteristics, such as double ring structures or disappearing alveoli in regions of liquid filling, could be identified and related to the position of alveoli relative to each other. Utilizing a ray tracing approach based on an idealized geometry of the mouse lung parenchyma, two major reflection processes could be attributed to the IVM image formation: partial reflection and total internal reflection between adjacent alveoli. Considering the origin of the reflexes, a model was developed to determine the true position of alveolar walls within IVM images. These results allow thorough understanding of IVM data and may serve as a basis for the correction of alveolar sizes for more accurate quantitative analysis within future studies of lung tissue dynamics.

Validation of airway resistance models for predicting pressure loss through anatomically realistic conducting airway replicas of adults and children

This work describes in vitro measurement of the total pressure loss at varying flow rate through anatomically realistic conducting airway replicas of 10 children, 4 to 8 years old, and 5 adults. Experimental results were compared with analytical predictions made using published airway resistance models. For the adult replicas, the model proposed by van Ertbruggen et al. (2005. J. Appl. Physiol. 98, 970-980) most accurately predicted central conducting airway resistance for inspiratory flow rates ranging from 15 to 90 L/min. Models proposed by Pedley et al. (1970. J. Respir. Physiol. 9, 371-386) and by Katz et al. (2011. J. Biomech. 44, 1137-1143) also provided reasonable estimates, but with a tendency to over predict measured pressure loss for both models. For child replicas, the Pedley and Katz models both provided good estimation of measured pressure loss at flow rates representative of resting tidal breathing, but under predicted measured values at high inspiratory flow rate (60 L/min). The van Ertbruggen model, developed based on flow simulations performed in an adult airway model, tended to under predict measured pressure loss through the child replicas across the range of flow rates studied (2 to 60 L/min). These results are intended to provide guidance for selection of analytical pressure loss models for use in predicting airway resistance and ventilation distribution in adults and children.

Computational modeling of the obstructive lung diseases asthma and COPD

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling.

There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods.

Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the current state-of-the-art in techniques developed for pulmonary image analysis, development of structural models of the respiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models to investigate structure-function relationships in asthma and COPD.

A stable approach for coupling multidimensional cardiovascular and pulmonary networks based on a novel pressure-flow rate or pressure-only Neumann boundary condition formulation

In many biomedical flow problems, reversed flows along with standard treatment of Neumann boundary conditions can cause instabilities. We have developed a method that resolves these instabilities in a consistent way while maintaining correct pressure and flow rate values. We also are able to remove the necessary prescription of both pressure and velocities/flow rates to problems where only pressure is known. In addition, the method is extended to coupled 3D/reduced-D fluid and fluid-structure interaction models. Numerical examples mainly focus on using Neumann boundary condition in cardiovascular and pulmonary systems, particularly, coupled with 3D-1D and 3D-0D models. Inflow pressure, traction, and impedance boundary conditions are first tested on idealized tubes for various Womersley numbers. Both pressure and flow rate are shown to match the analytical solutions for these examples. Our method is then tested on a coupled 1D-3D-1D artery example, demonstrating the power and simplicity of extending this method toward fluid-structure interaction. Finally, the proposed method is investigated for a coupled 3D-0D patient-specific full lung model during spontaneous breathing. All coupled 3D/reduced-D results show a perfect matching of pressure and flow rate between 3D and corresponding reduced-D boundaries. The methods are straight-forward to implement in contrast to using Lagrange multipliers as previously proposed in other studies.