A Feasible Computational Fluid Dynamics Study for Relationships of Structural and Functional Alterations with Particle Depositions in Severe Asthmatic Lungs

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.

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.

Structural and functional features of asthma participants with fixed airway obstruction using CT imaging and 1D computational fluid dynamics: A feasibility study

Asthma with fixed airway obstruction (FAO) is associated with significant morbidity and rapid decline in lung function, making its treatment challenging. Quantitative computed tomography (QCT) along with data postprocessing is a useful tool to obtain detailed information on airway structure, parenchymal function, and computational flow features. In this study, we aim to identify the structural and functional differences between asthma with and without FAO. The FAO group was defined by a ratio of forced expiratory volume in 1 s (FEV1 ) to forced vital capacity (FVC), FEV1 /FVC <0.7. Accordingly, we obtained two sets of QCT images at inspiration and expiration of asthma subjects without (N = 24) and with FAO (N = 12). Structural and functional QCT-derived airway variables were extracted, including normalized hydraulic diameter, normalized airway wall thickness, functional small airway disease, and emphysema percentage. A one-dimensional (1D) computational fluid dynamics (CFD) model considering airway deformation was used to compare the pressure distribution between the two groups. The computational pressures showed strong correlations with the pulmonary function test (PFT)-based metrics. In conclusion, asthma participants with FAO had worse lung functions and higher-pressure drops than those without FAO.

Dosimetry Sensitivity in a Lower Dimensional Model of Patient-Specific Asthma Subjects

OBJECTIVE

Experimental uncertainty will impact in silico model calculations of aerosol delivery and deposition. Patient-specific dosimetry models are often parameterized based on medical imaging data, which contain inherent experimental variability.

METHODS

Here, we created and parameterized 1D models of three subject-specific asthmatic subjects and randomly assigned perturbations of up to 15 % on airway diameter, segmental volume, and defected volume. Sensitivity of imaging data experimental variability on dosimetry metrics were quantified.

RESULTS

Lobar particle delivery primarily depended on the distal segmental volumes; 15 % range of noise resulted in delivery to the upper right lobe to vary at most from 15.2 and 18.2 % for one of the severe subjects. Particle deposition was most sensitive to airway diameter; 95 % confidence intervals spanned from 8 to 10.6 % in the mild/moderate subject for 15 % variation on input metrics for 5 [Formula: see text] diameter particles. While these results provide possible ranges of dosimetry calculations for a specific subject, the perturbations were not sufficient to model the large observed inter-subject variability (8.9, 19, and 14.5 % deposition, subjects 1--3, respectively, 5 [Formula: see text] diameter particles).

CONCLUSION

This study highlights that in silico model predictions are robust in the presence of experimental uncertainty and that it continues to be necessary to perform subject-specific simulations, especially within the presence of heterogeneous airway disease.

SIGNIFICANCE

Sensitivity analysis provides confidence in calculating deposition in the airways of asthmatic subjects within the presence of experimental uncertainty.

CFD simulation of porous microsphere particles in the airways of pulmonary fibrosis

BACKGROUND AND OBJECTIVE

Pulmonary fibrosis (PF) is a chronic progressive disease with an extremely high mortality rate and is a complication of COVID-19. Inhalable microspheres have been increasingly used in the treatment of lung diseases such as PF in recent years. Compared to the direct inhalation of drugs, a larger particle size is required to ensure the sustained release of microspheres. However, the clinical symptoms of PF may lead to the easier deposition of microspheres in the upper respiratory tract. Therefore, it is necessary to understand the effects of PF on the deposition of microspheres in the respiratory tract.

METHODS

In this study, airway models with different degrees of PF in humans and mice were established, and the transport and deposition of microspheres in the airway were simulated using computational fluid dynamics.

RESULTS

The simulation results showed that PF increases microsphere deposition in the upper respiratory tract and decreases bronchial deposition in both humans and mice. Porous microspheres with low density can ensure deposition in the lower respiratory tract and larger particle size. In healthy and PF humans, porous microspheres of 10 µm with densities of 700 and 400 kg/m³ were deposited most in the bronchi. Unlike in humans, microspheres larger than 4 µm are completely deposited in the upper respiratory tract of mice owing to their high inhalation velocity. For healthy and PF mice, microspheres of 6 µm with densities of and 100 kg/m³ are recommended.

CONCLUSIONS

The results showed that with the exacerbation of PF, it is more difficult for microsphere particles to deposit in the subsequent airway. In addition, there were significant differences in the deposition patterns among the different species. Therefore, it is necessary to process specific microspheres from different individuals. Our study can guide the processing of microspheres and achieve differentiated drug delivery in different subjects to maximize therapeutic effects.

Imaging in Asthma Management

Asthma is a heterogeneous disease characterized by chronic airway inflammation that affects more than 300 million people worldwide. Clinically, asthma has a widely variable presentation and is defined based on a history of respiratory symptoms alongside airflow limitation. Imaging is not needed to confirm a diagnosis of asthma, and thus the use of imaging in asthma has historically been limited to excluding alternative diagnoses. However, significant advances continue to be made in novel imaging methodologies, which have been increasingly used to better understand respiratory impairment in asthma. As a disease primarily impacting the airways, asthma is best understood by imaging methods with the ability to elucidate airway impairment. Techniques such as computed tomography, magnetic resonance imaging with gaseous contrast agents, and positron emission tomography enable assessment of the small airways. Others, such as optical coherence tomography and endobronchial ultrasound enable high-resolution imaging of the large airways accessible to bronchoscopy. These imaging techniques are providing new insights in the pathophysiology and treatments of asthma and are poised to impact the clinical management of asthma.

Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique

BACKGROUND AND OBJECTIVE

Cement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed (CDE) subjects using quantitative computed tomography-based airway structural and functional features.

METHODS

We obtained the airway features in five central and five sub-grouped segmental regions and the lung features in five lobar regions and one total lung region from 311 CDE and 298 non-CDE (NCDE) subjects. The five-fold cross-validation method was adopted to train the following classification models:ANN, support vector machine (SVM), logistic regression (LR), and decision tree (DT). For all the classification models, linear discriminant analysis (LDA) and genetic algorithm (GA) were applied for dimensional reduction and hyperparameterization, respectively. The ANN model without LDA was also optimized by the GA method to observe the effect of the dimensional reduction.

RESULTS

The genetically optimized ANN model without the LDA method was the best in terms of the classification accuracy. The accuracy, sensitivity, and specificity of the GA-ANN model with four layers were greater than those of the other classification models (i.e., ANN, SVM, LR, and DT using LDA and GA methods) in the five-fold cross-validation. The average values of accuracy, sensitivity, and specificity for the five-fold cross-validation were 97.0%, 98.7%, and 98.6%, respectively.

CONCLUSIONS

We demonstrated herein that a quantitative computed tomography-based ANN model could more effectively detect CDE subjects when compared to their counterpart models. By employing the model, the CDE subjects may be identified early for therapeutic intervention.

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.

Particle transport and deposition correlation with near-wall flow characteristic under inspiratory airflow in lung airways

Exposure of lung airways to detrimental suspended aerosols in the environment increases the vulnerability of the respiratory and cardiovascular systems. In addition, recent developments in therapeutic inhalation devices magnify the importance of particle transport. In this manuscript, particle transport and deposition patterns in the upper tracheobronchial (TB) tree were studied where the inertial forces are considerable for microparticles. Wall shear stress divergence (WSSdiv) is proposed as a wall-based parameter that can predict particle deposition patterns. WSSdiv is proportional to near-wall normal velocity and can quantify the strength of flow towards and away from the wall. Computational fluid dynamics (CFD) simulations were performed to quantify airflow velocity and WSS vectors for steady inhalation in one case-control and unsteady inhalation in six subject-specific airway trees. Turbulent flow simulation was performed for the steady case using large eddy simulation to study the effect of turbulence. Magnetic resonance velocimetry (MRV) measurements were used to validate the case-control CFD simulation. Inertial particle transport was modeled by solving the Maxey-Riley equation in a Lagrangian framework. Deposition percentage (DP) was quantified for the case-control model over five particle sizes. DP was found to be proportional to particle size in agreement with previous studies in the literature. A normalized deposition concentration (DC) was defined to characterize localized deposition. A relatively strong correlation (Pearson value > 0.7) was found between DC and positive WSSdiv for physiologically relevant Stokes (St) numbers. Additionally, a regional analysis was performed after dividing the lungs into smaller areas. A spatial integral of positive WSSdiv over each division was shown to maintain a very strong correlation (Pearson value > 0.9) with cumulative spatial DC or regional dosimetry. The conclusions were generalized to a larger population in which two healthy and four asthmatic patients were investigated. This study shows that WSSdiv could be used to predict the qualitative surface deposition and relative regional dosimetry without the need to solve a particle transport problem.

Patient-specific modeling of aerosol delivery in healthy and asthmatic adults

The magnitude and regional heterogeneity of airway obstructions in severe asthmatics is likely linked to insufficient drug delivery, as evidenced by the inability to mitigate exacerbations with inhaled aerosol medications. To understand the correlation between morphometric features, airflow distribution, and inhaled dosimetry, we perform dynamic computational simulations in two healthy and four asthmatic subjects. Models incorporate computed tomography-based and patient-specific central airway geometries and hyperpolarized ³He MRI-measured segmental ventilation defect percentages (SVDPs), implemented as resistance boundary conditions. Particles [diameters (d_p) = 1, 3, and 5 μm] are simulated throughout inhalation, and we record their initial conditions, both spatially and temporally, with their fate in the lung. Predictions highlight that total central airway deposition is the same between the healthy subjects (26.6%, d_p = 3 μm) but variable among the asthmatic subjects (ranging from 5.9% to 59.3%, d_p = 3 μm). We found that by preferentially releasing the particles during times of fast or slow inhalation rates we enhance either central airway deposition percentages or peripheral particle delivery, respectively. These predictions highlight the potential to identify with simulations patients who may not receive adequate therapeutic dosages with inhaled aerosol medication and therefore identify patients who may benefit from alternative treatment strategies. Furthermore, by improving regional dose levels, we may be able to preferentially deliver drugs to the airways in need, reducing associated adverse side effects.NEW & NOTEWORTHY Although it is evident that exacerbation mitigation is unsuccessful in some asthmatics, it remains unclear whether or not these patients receive adequate dosages of inhaled therapeutics. By coupling MRI and computed tomography data with patient-specific computational models, our predictions highlight the large intersubject variability, specifically in severe asthma.

Biological Obstacles for Identifying In Vitro-In Vivo Correlations of Orally Inhaled Formulations

Oral inhalation of drugs is the classic therapy of obstructive lung diseases. In contrast to the oral route, the link between in vitro and in vivo findings is less well defined and predictive models and parameters for in vitro-in vivo correlations are missing. Frequently used in vitro models and problems in obtaining in vivo values to establish such models and to identify the action of formulations in vivo are discussed. It may be concluded that major obstacles to link in vitro parameters on in vivo action include lack of treatment adherence and incorrect use of inhalers by patients, variation in inhaler performance, changes by humidity, uncertainties about lung deposition, and difficulties to measure drug levels in epithelial lining fluid and tissue. Physiologically more relevant in vitro models, improvement in inhaler performance, and better techniques for in vivo measurements may help to better understand importance and interactions between individual in vitro parameters in pulmonary delivery.

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.

In vivo dynamics of the tracheal airway and its influences on respiratory airflows

Respiration is a dynamic process accompanied by morphological changes in the airways. Although deformation of large airways is expected to exacerbate pulmonary disease symptoms by obstructing airflow during increased minute ventilation, its quantitative effects on airflow characteristics remain unclear. Here, we used an exemplar case derived from in vivo dynamic imaging and examined the effects of tracheal deformation on airflow characteristics under different conditions. First, we measured tracheal deformation profiles of a healthy lung using magnetic resonance imaging during forced exhalation, which we simulated to characterize subject-specific airflow patterns. Subsequently, for both inhalation and exhalation, we compared the airflows when the maximal deformation in tracheal cross-sectional area was 0% (rigid), 33% (mild), 50% (moderate), or 75% (severe). We quantified differences in airflow patterns between deformable and rigid airways by computing the correlation coefficients (R) and the root-mean-square of differences (Drms) between their velocity contours. For both inhalation and exhalation, airflow patterns were similar in all branches between the rigid and mild conditions (R > 0.9; Drms < 32%). However, airflow characteristics in the moderate and severe conditions differed markedly from those in the rigid and mild conditions in all lung branches, particularly for inhalation (moderate: R > 0.1, Drms < 76%; severe: R > 0.2, Drms < 96%). Our exemplar case supports the use of a rigid airway assumption to compute flows for mild deformation. For moderate or severe deformation, however, dynamic contraction should be considered, especially during inhalation, to accurately predict airflow and elucidate the underlying pulmonary pathology.