A tree-parenchyma coupled model for lung ventilation simulation

Analysis of the effect of inert gas on alveolar/venous blood partial pressure by using the operator splitting method

This study aims to investigate how inert gas affects the partial pressure of alveolar and venous blood using a fast and accurate operator splitting method (OSM). Unlike previous complex methods, such as the finite element method (FEM), OSM effectively separates governing equations into smaller sub-problems, facilitating a better understanding of inert gas transport and exchange between blood capillaries and surrounding tissue. The governing equations were discretized with a fully implicit finite difference method (FDM), which enables the use of larger time steps. The model employed partial differential equations, considering convection-diffusion in blood and only diffusion in tissue. The study explores the impact of initial arterial pressure, breathing frequency, blood flow velocity, solubility, and diffusivity on the partial pressure of inert gas in blood and tissue. Additionally, the effects of anesthetic inert gas and oxygen on venous blood partial pressure were analyzed. Simulation results demonstrate that the high solubility and diffusivity of anesthetic inert gas lead to its prolonged presence in blood and tissue, resulting in lower partial pressure in venous blood. These findings enhance our understanding of inert gas interaction with alveolar/venous blood, with potential implications for medical diagnostics and therapies.

Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks

For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

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.

Whole-lung finite-element models for mechanical ventilation and respiratory research applications

Mechanical ventilation has been a vital treatment for Covid-19 patients with respiratory failure. Lungs assisted with mechanical ventilators present a wide variability in their response that strongly depends on air-tissue interactions, which motivates the creation of simulation tools to enhance the design of ventilatory protocols. In this work, we aim to create anatomical computational models of the lungs that predict clinically-relevant respiratory variables. To this end, we formulate a continuum poromechanical framework that seamlessly accounts for the air-tissue interaction in the lung parenchyma. Based on this formulation, we construct anatomical finite-element models of the human lungs from computed-tomography images. We simulate the 3D response of lungs connected to mechanical ventilation, from which we recover physiological parameters of high clinical relevance. In particular, we provide a framework to estimate respiratory-system compliance and resistance from continuum lung dynamic simulations. We further study our computational framework in the simulation of the supersyringe method to construct pressure-volume curves. In addition, we run these simulations using several state-of-the-art lung tissue models to understand how the choice of constitutive models impacts the whole-organ mechanical response. We show that the proposed lung model predicts physiological variables, such as airway pressure, flow and volume, that capture many distinctive features observed in mechanical ventilation and the supersyringe method. We further conclude that some constitutive lung tissue models may not adequately capture the physiological behavior of lungs, as measured in terms of lung respiratory-system compliance. Our findings constitute a proof of concept that finite-element poromechanical models of the lungs can be predictive of clinically-relevant variables in respiratory medicine.

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.

AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations

This paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making.

Use of computational fluid dynamics deposition modeling in respiratory drug delivery

INTRODUCTION

Respiratory drug delivery is a surprisingly complex process with a number of physical and biological challenges. Computational fluid dynamics (CFD) is a scientific simulation technique that is capable of providing spatially and temporally resolved predictions of many aspects related to respiratory drug delivery from initial aerosol formation through respiratory cellular drug absorption.

AREAS COVERED

This review article focuses on CFD-based deposition modeling applied to pharmaceutical aerosols. Areas covered include the development of new complete-airway CFD deposition models and the application of these models to develop a next-generation of respiratory drug delivery strategies.

EXPERT OPINION

Complete-airway deposition modeling is a valuable research tool that can improve our understanding of pharmaceutical aerosol delivery and is already supporting medical hypotheses, such as the expected under-treatment of the small airways in asthma. These complete-airway models are also being used to advance next-generation aerosol delivery strategies, like controlled condensational growth. We envision future applications of CFD deposition modeling to reduce the need for human subject testing in developing new devices and formulations, to help establish bioequivalence for the accelerated approval of generic inhalers, and to provide valuable new insights related to drug dissolution and clearance leading to microdosimetry maps of drug absorption.

Modelling structural determinants of ventilation heterogeneity: A perturbative approach

We have developed a computational model of gas mixing and ventilation in the human lung represented as a bifurcating network. We have simulated multiple-breath washout (MBW), a clinical test for measuring ventilation heterogeneity (VH) in patients with obstructive lung conditions. By applying airway constrictions inter-regionally, we have predicted the response of MBW indices to obstructions and found that they detect a narrow range of severe constrictions that reduce airway radius to 10%–30% of healthy values. These results help to explain the success of the MBW test to distinguish obstructive lung conditions from healthy controls. Further, we have used a perturbative approach to account for intra-regional airway heterogeneity that avoids modelling each airway individually. We have found, for random airway heterogeneity, that the variance in MBW indices is greater when indices are already elevated due to constrictions. By quantifying this effect, we have shown that variability in lung structure and mechanical properties alone can lead to clinically significant variability in MBW indices (specifically the Lung Clearance Index—LCI, and the gradient of phase-III slopes—S_cond), but only in cases simulating obstructive lung conditions. This method is a computationally efficient way to probe the lung’s sensitivity to structural changes, and to quantify uncertainty in predictions due to random variations in lung mechanical and structural properties.

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%.

The effect of disease and respiration on airway shape in patients with moderate persistent asthma

Computational models of gas transport and aerosol deposition frequently utilize idealized models of bronchial tree structure, where airways are considered a network of bifurcating cylinders. However, changes in the shape of the lung during respiration affect the geometry of the airways, especially in disease conditions. In this study, the internal airway geometry was examined, concentrating on comparisons between mean lung volume (MLV) and total lung capacity (TLC). A set of High Resolution CT images were acquired during breath hold on a group of moderate persistent asthmatics at MLV and TLC after challenge with a broncho-constrictor (methacholine) and the airway trees were segmented and measured. The airway hydraulic diameter (Dh) was calculated through the use of average lumen area (Ai) and average internal perimeter (Pi) at both lung volumes and was found to be systematically higher at TLC by 13.5±9% on average, with the lower lobes displaying higher percent change in comparison to the lower lobes. The average internal diameter (Din) was evaluated to be 12.4±6.8% (MLV) and 10.8±6.3% (TLC) lower than the Dh, for all the examined bronchi, a result displaying statistical significance. Finally, the airway distensibility per bronchial segment and per generation was calculated to have an average value of 0.45±0.28, exhibiting high variability both between and within lung regions and generations. Mixed constriction/dilation patterns were recorded between the lung volumes, where a number of airways either failed to dilate or even constricted when observed at TLC. We conclude that the Dh is higher than Din, a fact that may have considerable effects on bronchial resistance or airway loss at proximal regions. Differences in caliber changes between lung regions are indicative of asthma-expression variability in the lung. However, airway distensibility at generation 3 seems to predict distensibility more distally.