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

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.

BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification

Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.

Modeling Realistic Geometries in Human Intrathoracic Airways

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.

The effects of airway disease on the deposition of inhaled drugs

INTRODUCTION

The deposition of inhaled medications is the first step in the pulmonary pharmacokinetic process to produce a therapeutic response. Not only lung dose but more importantly the distribution of deposited drug in the different regions of the lung determines local bioavailability, efficacy, and clinical safety. Assessing aerosol deposition patterns has been the focus of intense research that combines the fields of physics, radiology, physiology, and biology.

AREAS COVERED

The review covers the physics of aerosol transport in the lung, experimental, and in-silico modeling approaches to determine lung dose and aerosol deposition patterns, the effect of asthma, chronic obstructive pulmonary disease, and cystic fibrosis on aerosol deposition, and the clinical translation potential of determining aerosol deposition dose.

EXPERT OPINION

Recent advances in in-silico modeling and lung imaging have enabled the development of realistic subject-specific aerosol deposition models, albeit mainly in health. Accurate modeling of lung disease still requires additional refinements in existing imaging and modeling approaches to better characterize disease heterogeneity in peripheral airways. Nevertheless, recent patient-centric innovation in inhaler device engineering and the incorporation of digital technology have led to more consistent lung deposition and improved targeting of the distal airways, which better serve the clinical needs of patients.

Human-airway surface mesh smoothing based on graph convolutional neural networks

BACKGROUND AND OBJECTIVE

A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations.

METHOD

The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties.

RESULTS

In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method.

CONCLUSIONS

The airway model constructed by the AMSL method reproduces branch diameters accurately without any shrinkage, especially in the case of smaller airways. The accurate estimation of airway geometry using a smoothing method is critical for estimating flow properties in CFD simulations.

Investigating distributions of inhaled aerosols in the lungs of post-COVID-19 clusters through a unified imaging and modeling approach

BACKGROUND

Recent studies, based on clinical data, have identified sex and age as significant factors associated with an increased risk of long COVID. These two factors align with the two post-COVID-19 clusters identified by a deep learning algorithm in computed tomography (CT) lung scans: Cluster 1 (C1), comprising predominantly females with small airway diseases, and Cluster 2 (C2), characterized by older individuals with fibrotic-like patterns. This study aims to assess the distributions of inhaled aerosols in these clusters.

METHODS

140 COVID survivors examined around 112 days post-diagnosis, along with 105 uninfected, non-smoking healthy controls, were studied. Their demographic data and CT scans at full inspiration and expiration were analyzed using a combined imaging and modeling approach. A subject-specific CT-based computational model analysis was utilized to predict airway resistance and particle deposition among C1 and C2 subjects. The cluster-specific structure and function relationships were explored.

RESULTS

In C1 subjects, distinctive features included airway narrowing, a reduced homothety ratio of daughter over parent branch diameter, and increased airway resistance. Airway resistance was concentrated in the distal region, with a higher fraction of particle deposition in the proximal airways. On the other hand, C2 subjects exhibited airway dilation, an increased homothety ratio, reduced airway resistance, and a shift of resistance concentration towards the proximal region, allowing for deeper particle penetration into the lungs.

CONCLUSIONS

This study revealed unique mechanistic phenotypes of airway resistance and particle deposition in the two post-COVID-19 clusters. The implications of these findings for inhaled drug delivery effectiveness and susceptibility to air pollutants were explored.

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.

Predicting particle deposition using a simplified 8-path in silico human lung prototype

Understanding particle deposition in the human lung is crucial for the assessment of environmental pollutants and the design of new drug delivery systems. Traditionally, research has been carried out by experimental analysis, but this generally requires expensive equipment and exposure of volunteers to radiation, resulting in limited data. To overcome these drawbacks, there is an emphasis on the development of numerical models capable of accurate predictive analysis. The most advanced of these computer simulations are based on three-dimensional computational fluid dynamics. Solving the flow equations in a complete, fully resolved lung airway model is currently not feasible due to the computational resources required. In the present work, a simplified lung model is presented and validated for accurate prediction of particle deposition. Simulations are performed for an 8-path approximation to a full lung airway model. A novel boundary condition method is used to ensure accurate results in truncated flow branches. Simulations are performed at a steady inhalation flow rate of 18 l min-1, corresponding to a low activity breathing rate, while the effects of particle size and density are investigated. Comparison of the simulation results with available experimental data shows that reasonably accurate results can be obtained at a small fraction of the cost of a full airway model. The simulations clearly evaluate the effect of both particle size and particle density. Most importantly, the results show an improvement over a previously documented single-path model, both in terms of accuracy and the ability to obtain regional deposition rates. The present model represents an improvement over previously used simplified models, including single-path models. The multi-path reduced airway approach described can be used by researchers for general and patient-specific analyses of particle deposition and for the design of effective drug delivery systems.

A computed tomography imaging-based subject-specific whole-lung deposition model

The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this work, we proposed an imaging-based subject-specific whole-lung deposition model. The geometries of airways and lobes were segmented from computed tomography (CT) lung images at total lung capacity (TLC), and the regional air-volume changes were calculated by registering CT images at TLC and functional residual capacity (FRC). The geometries were used to create the structure of entire subject-specific conducting airways and acinar units. The air-volume changes were used to estimate the function of subject-specific ventilation distributions among acinar units and regulate flow rates in respiratory airway models. With the airway dimensions rescaled to a desired lung volume and the airflow field simulated by a computational fluid dynamics model, particle deposition fractions were calculated using deposition probability formulae adjusted with an enhancement factor to account for the effects of secondary flow and airway geometry in proximal airways. The proposed model was validated in silico against existing whole-lung deposition models, three-dimensional (3D) computational fluid and particle dynamics (CFPD) for an acinar unit, and 3D CFPD deep lung model comprising conducting and respiratory regions. The model was further validated in vivo against the lobar particle distribution and the coefficient of variation of particle distribution obtained from CT and single-photon emission computed tomography (SPECT) images, showing good agreement. Subject-specific airway structure increased the deposition fraction of 10.0-μm particles and 0.01-μm particles by approximately 10%. An enhancement factor increased the overall deposition fractions, especially for particle sizes between 0.1 and 1.0 μm.

Structural and functional alterations of subjects with cement dust exposure: A longitudinal quantitative computed tomography-based study

Cement dust exposure (CDE) can be a risk factor for pulmonary disease, causing changes in segmental airways and parenchymal lungs. This study investigates longitudinal alterations in quantitative computed tomography (CT)-based metrics due to CDE. We obtained CT-based airway structural and lung functional metrics from CDE subjects with baseline CT and follow-up CT scans performed three years later. From the CT, we extracted wall thickness (WT) and bifurcation angle (θ) at total lung capacity (TLC) and functional residual capacity (FRC), respectively. We also computed air volume (V_air), tissue volume (V_tissue), global lung shape, percentage of emphysema (Emph%), and more. Clinical measures were used to associate with CT-based metrics. Three years after their baseline, the pulmonary function tests of CDE subjects were similar or improved, but there were significant alterations in the CT-based structural and functional metrics. The follow-up CT scans showed changes in θ at most of the central airways; increased WT at the subgroup bronchi; smaller V_air at TLC at all except the right upper and lower lobes; smaller V_tissue at all lobes in TLC and FRC except for the upper lobes in FRC; smaller global lung shape; and greater Emph% at the right upper and lower lobes. CT-based structural and functional variables are more sensitive to the early identification of CDE subjects, while most clinical lung function changes were not noticeable. We speculate that the significant long-term changes in CT are uniquely observed in CDE subjects, different from smoking-induced structural changes.

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.

Origins of and lessons from quantitative functional X-ray computed tomography of the lung

Functional CT of the lung has emerged from quantitative CT (qCT). Structural details extracted at multiple lung volumes offer indices of function. Additionally, single volumetric images, if acquired at standardized lung volumes and body posture, can be used to model function by employing such engineering techniques as computational fluid dynamics. With the emergence of multispectral CT imaging including dual energy from energy integrating CT scanners and multienergy binning using the newly released photon counting CT technology, function is tagged via use of contrast agents. Lung disease phenotypes have previously been lumped together by the limitations of spirometry and plethysmography. QCT and its functional embodiment have been imbedded into studies seeking to characterize chronic obstructive pulmonary disease, severe asthma, interstitial lung disease and more. Reductions in radiation dose by an order of magnitude or more have been achieved. At the same time, we have seen significant increases in spatial and density resolution along with methodologic validations of extracted metrics. Together, these have allowed attention to turn towards more mild forms of disease and younger populations. In early applications, clinical CT offered anatomic details of the lung. Functional CT offers regional measures of lung mechanics, the assessment of functional small airways disease, as well as regional ventilation-perfusion matching (V/Q) and more. This paper will focus on the use of quantitative/functional CT for the non-invasive exploration of dynamic three-dimensional functioning of the breathing lung and beating heart within the unique negative pressure intrathoracic environment of the closed chest.

Effects of hygroscopic growth of ambient urban aerosol particles on their modelled regional and local deposition in healthy and COPD-compromised human respiratory system

Total, regional and local deposition fractions of urban-type aerosol particles with diameters of 50, 75, 110 and 145 nm were modelled and studied in their dry state and after their hygroscopic growth using a Stochastic Lung Model and a Computational Fluid and Particle Dynamics method. Healthy subjects and patients with severe chronic obstructive pulmonary disease (COPD) were considered. The hygroscopic growth factors (HGFs) adopted were determined experimentally and represent a real urban-type environment. The hygroscopic growth of particles resulted in decrease of the deposition fractions in all major parts of the healthy respiratory system and the extent of the deposited fractions was rising monotonically with particle size. In the extrathoracic (ET) region, the relative decrease was between 7% and 13%. In the lungs the deposition decreased by 11-16%. The decrease of deposition fraction due to hygroscopic growth was more accentuated in the conductive airways (up to 25%) and less pronounced towards the terminal airways. The spatial distribution of the deposited particles remained highly inhomogeneous with some areas containing thousands times more particles than the average number of particles per unit surface area. For COPD patients, the hygroscopic growth produced similar deposition alterations in the ET region than for healthy subjects. In the conductive airways, however, the particle growth caused a substantial relative decrease in the deposition fractions. In contrast, the relative depositions of hygroscopic particles increased in the acinar region.

Leveraging statistical shape modeling in computational respiratory dynamics: Nanomedicine delivery in remodeled airways

BACKGROUND AND OBJECTIVE

Accurate knowledge of the delivered doses to the diseased site in the respiratory tract is crucial to elicit desired therapeutic outcomes. However, such information is still difficult to obtain due to inaccessibility for measurement or visualization, complex network structure, and challenges in reconstructing lung geometries with disease-invoked airway remodeling. This study presents a novel method to simulate the airway remodeling in a mouth-lung geometry extending to G9.

METHODS

Statistical shape modeling was used to extract morphological features from a lung geometry database and four new models (i.e., M1-M4) were generated with parameter-controlled dilated/constricted bronchioles in the left-lower (LL) lung. The variations in airflow and particle deposition due to the airway remodeling were simulated using a well-tested k-ω turbulence model and a Lagrangian tracking approach.

RESULTS

Significant variations in flow partitions between the lower and upper lobes of the left lung, as well as between the left and right lungs. The flow partition into the LL lobe varied by 10-fold between the most dilated and constricted models in this study. Significantly lower doses were also predicted on the surface of the constricted LL bronchioles G4-G9, as well as into the peripheral airways beyond G9. However, the total dosimetry in the mouth-lung geometry (up to G9) exhibited low sensitivity to the LL lobar remodeling. Results in this study suggest that the optimal nanomedicine should be 2-10 nm in diameter if targeted at the constricted bronchioles G4-G9 as in topical inhalation therapy but should be larger than 20 nm if targeted at the alveolar region as in systemic therapy.

CONCLUSIONS

This study highlights the large dose variability from local airway remodeling and the need to consider these variations in the treatment planning for pneumonia and other obstructive respiratory diseases.

Inhalation dosimetry of nasally inhaled respiratory aerosols in the human respiratory tract with locally remodeled conducting lungs

Objective: Respiratory diseases are often accompanied by alterations to airway morphology. However, inhalation dosimetry data in remodeled airways are scarce due to the challenges in reconstructing diseased respiratory morphologies. This study aims to study the airway remodeling effects on the inhalation dosimetry of nasally inhaled nanoparticles in a nose-lung geometry that extends to G9 (ninth generation).Materials and methods: Statistical shape modeling was used to develop four diseased lung models with varying levels of bronchiolar dilation/constriction in the left-lower (LL) lobe (i.e. M1-M4). Respiratory airflow and particle deposition were simulated using a low Reynolds number k-ω turbulence model and a Lagrangian tracking approach.Results: Significant discrepancies were observed in the flow partitions between the left and right lungs, as well as between the lower and upper lobes of the left lung, which changed by 10-fold between the most dilated and constricted models.Much lower doses were predicted on the surface of the constricted LL bronchioles G4-G9, as well as into the peripheral airways beyond G9 of the LL lung. However, the LL lobar remodeling had little effect on the dosimetry in the nasopharynx, as well as on the total dosimetry in the nose-lung geometry (up to G9).Conclusion: It is suggested that airway remodeling may pose a higher viral infection risk to the host by redistributing the inhaled viruses to healthy lung lobes. Airway remodeling effects should also be considered in the treatment planning of inhalation therapies, not only because of the dosimetry variation from altered lung morphology but also its evolution as the disease progresses.

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.

Sound transmission in human thorax through airway insonification: an experimental and computational study with diagnostic applications

Pulmonary diseases and injury lead to structural and functional changes in the lung parenchyma and airways, often resulting in measurable sound transmission changes on the chest wall surface. Additionally, noninvasive imaging of externally driven mechanical wave motion in the chest (e.g., using magnetic resonance elastography) can provide information about lung stiffness and other structural property changes which may be of diagnostic value. In the present study, a comprehensive computational simulation (in silico) model was developed to simulate sound wave propagation in the airways, parenchyma, and chest wall under normal and pathological conditions that create distributed structural (e.g., pneumothoraces) and diffuse material (e.g., fibrosis) changes, as well as a localized structural and material changes as may be seen with a neoplasm. Experiments were carried out in normal subjects to validate the baseline model. Sound waves with frequency content from 50 to 600 Hz were introduced into the airways of three healthy human subjects through the mouth, and transthoracic transmitted waves were measured by scanning laser Doppler vibrometry at the chest wall surface. The computational model predictions of a frequency-dependent decreased sound transmission due to pneumothorax were consistent with experimental measurements reported in previous work. Predictions for the case of fibrosis show that while shear wave motion is altered, changes to compression wave propagation are negligible, and thus, insonification, which primarily drives compression waves, is not ideal to detect the presence of fibrosis. Results from the numerical simulation of a tumor show an increase in the wavelength of propagating waves in the immediate vicinity of the tumor region. Graphical abstract.

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.

Transport and deposition of hygroscopic particles in asthmatic subjects with and without airway narrowing

This study numerically investigates the effect of hygroscopicity on transport and deposition of particles in severe asthmatic lungs with distinct airway structures. The study human subjects were selected from two imaging-based severe asthmatic clusters with one characterized by non-constricted airways and the other by constricted airways in the lower left lobe (LLL). We compared the deposition fractions of sodium chloride (NaCl) particles with a range of aerodynamic diameters (1-8 μm) in cluster archetypes under conditions with and without hygroscopic growth. The temperature and water vapor distributions in the airways were simulated with an airway wall boundary condition that accounts for variable temperature and water vapor evaporation at the interface between the lumen and the airway surface liquid layer. On average, the deposition fraction increased by about 6% due to hygroscopic particle growth in the cluster subjects with constricted airways, while it increased by only about 0.5% in those with non-constricted airways. The effect of particle growth was most significant for particles with an initial diameter of 2 μm in the cluster subjects with constricted airways. The effect diminished with increasing particle size, especially for particles with an initial diameter larger than 4 μm. This suggests the necessity to differentiate asthmatic subjects by cluster in engineering the aerosol size for tailored treatment. Specifically, the treatment of severe asthmatic subjects who have constricted airways with inhalation aerosols may need submicron-sized hygroscopic particles to compensate for particle growth, if one targets for delivering to the peripheral region. These results could potentially inform the choice of particle size for inhalational drug delivery in a cluster-specific manner.