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.

A whole lung in silico model to estimate age dependent particle dosimetry.

Anatomical and physiological changes alter airflow characteristics and aerosol distribution in the developing lung. Correlation between age and aerosol dosimetry is needed, specifically because youth are more susceptible to medication side effects. In this study, we estimate aerosol dosages (particle diameters of 1, 3, and 5 [Formula: see text]m) in a 3 month-old infant, a 6 year-old child, and a 36 year-old adult by performing whole lung subject-specific particle simulations throughout respiration. For 3 [Formula: see text]m diameter particles we estimate total deposition as 88, 73, and [Formula: see text] and the conducting versus respiratory deposition ratios as 4.0, 0.5, and 0.4 for the infant, child, and adult, respectively. Due to their lower tidal volumes and functional residual capacities the deposited mass is smaller while the tissue concentrations are larger in the infant and child subjects, compared to the adult. Furthermore, we find that dose cannot be predicted by simply scaling by tidal volumes. These results highlight the need for additional clinical and computational studies that investigate the efficiency of treatment, while optimizing dosage levels in order to alleviate side effects, in youth.

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 (dp) = 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%, dp = 3 µm) but variable among the asthmatic subjects (ranging from 5.9% to 59.3%, dp = 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.

Regional flow and deposition variability in adult female lungs: A numerical simulation pilot study.

BACKGROUND: Despite the promise of respiratory simulations improving diagnosis and treatment of pulmonary diseases, model predictions have yet to be translated into the clinical setting. Current state-of-the-art in silico models have not yet incorporated subject variability in their predictions of airflow distributions and extent of deposited particles. Until inter-subject variability is accounted for in lung modeling, it will remain impossible to translate model predictions into clinical practice. METHODS: Airflow and particle trajectories (dp=1,3,5µm) are calculated in three subject-specific female adults by performing physiologically-based simulations. The computation framework features the ability to track air and particles throughout the respiration cycle and in the entire lung. Airway resistances, air velocities, and local deposition sites are correlated to airway anatomical features. FINDINGS: Smaller airway diameters are correlated to larger airway resistances and pressure gradients in one subject compared to the other two. Irregular shape of the airway and flow direction (e.g. inspiration or expiration) correspond with peak velocities and secondary flow motions. Largest subject variability in deposition between conducting and respiratory zones is seen for 1 µm diameter particles. Little difference in total deposition is found among subjects. Localized deposited particle concentration hotspots are linked to airway anatomy and flow motion. INTERPRETATION: Simulation predictions provide a first look into the correlation of anatomical features with airflow characteristics and deposited particle concentrations. Global deposition percentages ranged (at most, by 20%) between subjects and variances in localized deposition hotspots are correlated to variances in flow characteristics.

Patient-Specific Computational Simulations of Hyperpolarized 3He MRI Ventilation Defects in Healthy and Asthmatic Subjects.

Combined, medical imaging data and respiratory computer simulations may facilitate novel insight into pulmonary disease phenotypes, including the structure/function relationships within the airways. This integration may ultimately enable improved classification and treatment of asthma. Severe asthma (15% of asthmatics) is particularly challenging to treat, as these patients do not respond well to inhaled therapeutics. METHODS: This study combines medical image data with patient-specific computational models to predict gas distributions and airway mechanics in healthy and asthmatic subjects. We achieve this by integrating segmental volume defect percent (SVDP), measured from hyperpolarized 3He MRI and CT images, to create models of patient-specific gas flow within the conducting airways. Predicted and measured SVDP distributions are achieved when the prescribed resistances are increased systematically. RESULTS: Because of differences in airway morphology and regional function, airway resistances and flow structures varied between the asthmatic subjects. Specifically, while mean SVDP was similar between the severe asthmatics (4.30±5.22 versus 3.54±5.98%), one subject exhibited abnormal flow structures, high near wall flow gradients, and enhanced conducting airway resistances (17.3E-3versus 1.1E-3 cmH2O-s/mL) in comparison to the other severe asthmatic subject. CONCLUSION: By coupling medical imaging data with computer simulations, we provide detailed insight into pathological flow characteristics and airway mechanics in asthmatics, beyond what could be inferred independently.