Construction of a hybrid lung model by combining a real geometry of the upper airways and an idealized geometry of the lower airways

A quasi-realistic computational model development and flow field study of the human upper and central airways

The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka's deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka's model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15 l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract.

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.

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.

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.

A numerical study of the effects of ambient temperature and humidity on the particle growth and deposition in the human airway

A numerical study was conducted on the effects of ambient temperature and humidity on the transportation of sodium chloride particles (100 nm-1 μm) in a human airway model ranging from the nasal cavity to bronchi. A mucus-tissue structure was adopted to model the mass and heat transfer on the airway surface boundary. The temperature and humidity distributions of the respiratory flow were calculated and then the interaction between the particle and water vapor was further analyzed. It was predicted that the particle size grew to the ratio of 5-6 under subsaturation conditions because of hygroscopicity, which shifted the deposition efficiency in opposite directions on dependence of the initial particle size. However, the particles could be drastically raised to 40 times of the initial 100 nm diameter if the supersaturation-induced condensation was established, that was prone to occur under the cold-dry condition, and consequently promoted the deposition significantly. Such behavior might effectively contribute to the revitalized coronavirus disease 2019 (COVID-19) pandemic in addition to the more active virus itself in winter.

The Role of Respiratory Flora in the Pathogenesis of Chronic Respiratory Diseases

Large quantities of bacteria, including Firmicutes, Actinobacteria, and Bacteroidetes, colonize the surface of the respiratory mucosa of healthy people. They interact and coexist with the local mucosal immune system of the human airway, maintaining the immune stability and balance of the respiratory system. While suffering from chronic respiratory diseases, the microbial population in the airway changes and the proportion of Proteobacteria is increased in patients with asthma. The abundance of the microbial population in patients with chronic obstructive pulmonary disease (COPD) is decreased, and conversely, the proportion of Firmicutes and Proteobacteria increased. The diversity of airway microorganisms in cystic fibrosis (CF) patients is decreased, while pathogenic bacteria and conditional pathogenic bacteria are proliferated in large numbers. The proportion of Firmicutes and Proteobacteria is increased in patients with upper airway cough syndrome (UACS), which replaces the dominance of Streptococcus and Neisseria in the pharynx of a normal population. Therefore, a clear understanding of the immune process of the airway flora and the immune dysfunction of the flora on the pathogenesis of chronic respiratory diseases can provide new ideas for the prevention and treatment of human respiratory diseases.

Various reduced-order surrogate models for fluid flow and mass transfer in human bronchial tree

The bronchial tree plays a main role in the human respiratory system because the air distribution throughout the lungs and gas exchange with blood occur in the airways whose dimensions vary from several centimeters to micrometers. Organization of about 60,000 conducting airways and 33 million respiratory airways in a limited space results in a complex structure. Due to this inherent complexity and a high number of airways, using target-oriented dimensional reduction is inevitable. In addition, there is no general reduced-order model for various types of problems. This necessitates coming up with an appropriate model from a variety of different reduced-order models to solve the desired problem. Lumped formulation, trumpet, or typical path model of whole or parts of bronchial tree are frequently used reduced-order models. On the other hand, using any of these models results in underestimation of flow heterogeneity leading to inaccurate prediction of the systems whose mechanisms depend on the fluid heterogeneity. In this study, a simple robust model combining mechanistic and non-mechanistic modeling approaches of the bronchial tree is proposed which overcomes the limitations of the previous reduced-order models and gives the same results of a detailed mechanistic model for the first time. This model starts from an accurate multi-branching model of conducting and respiratory airways (i.e., the base model) and suggests a proxy model of conducting airway and reduced-order model of respiratory airways based on the base model to significantly reduce computational cost while retaining the accuracy. The combination of these models suggests various reduced-order surrogate models of the human bronchial tree for different problems. The applications and limitations of each reduced-order model are also discussed. The accuracy of the proposed model in the prediction of fluid heterogeneity has been examined by the simulation of multi-breath inert gas washout because the alveolar slope is the reflection of fluid heterogeneity where the computational time decreases from 121 h (using the base model) to 4.8 s (using the reduced-order model). A parallel strategy for solving the equations is also proposed which decreases run time by 0.18 s making the model suitable for real-time applications. Furthermore, the ability of the model has been evaluated in the modeling of asthmatic lung as an instance of abnormal lungs, and in the modeling of O2-CO2 exchange as an instance of nonlinear reacting systems. The results indicate that the proposed model outperforms previous models based on accuracy, robustness, and run time.