Glottis motion effects on the particle transport and deposition in a subject-specific mouth-to-trachea model: A CFPD study

• Published in: Computers in biology and medicine Vol. 116; no. C; p. 103532
• Main Authors: Zhao, Jianan, Feng, Yu, Fromen, Catherine A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2020; Elsevier Limited; Elsevier
• Subjects: Accuracy; Administration, Inhalation; Aerosols; Bronchi - physiology; Chronic obstructive pulmonary disease; Computational fluid-particle dynamics (CFPD); Computer applications; Computer Simulation; Deposition; Drug delivery; Drug delivery systems; Dynamic mesh; Efficiency; Finite element method; Generalized glottis motion function (GGMF); Glottis; Glottis - physiology; Glottis adduction and abduction; Humans; Hydrodynamics; Influence; Internal Medicine; Lung diseases; Lungs; Models, Biological; Motion effects; Mouth; Movement - physiology; Other; Predictions; Respiration; Secondary flow; Simulation; Throat; Trachea; Trachea - physiology; Transport properties; Turbulence models; Waveforms; Generalized glottis motion function (GGMF); Glottis adduction and abduction; Dynamic mesh; Computational fluid-particle dynamics (CFPD)
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2019.103532

Computational Fluid-Particle Dynamics (CFPD) models have been employed to predict lung aerosol dynamics for decades, estimating the delivery efficiency of inhaled drugs into the tracheobronchial tree. However, existing CFPD models assume the glottis is static during the breathing cycle. Failing to capture the dynamic motion of the glottis may introduce significant errors in drug deposition estimations. A novel CFPD model was developed with the capability of modeling the glottis motion using the dynamic mesh method. To explore the causal relationships between the glottis motion and the inhaled drug particle dynamics, simulations were performed to compare static and different dynamic glottis models in a subject-specific mouth-to-trachea geometry under idealized sinusoidal and realistic breathing waveforms. By defining the movement of each node in the glottis region using a generalized glottis motion function (GGMF) validated with clinical data, the abduction and adduction of the glottis were accurately described. Transient transport characteristics of inhaled particle-laden airflows were investigated and analyzed, including the glottis motion effect on the inhaled particles with the aerodynamic diameters from 0.1 to 10 μm. Numerical results indicate that the static glottis assumption deviates the total deposition fraction predictions by more than 8% in relative differences. Compared with the CFPD models with the static glottis assumption, the dynamic glottis model can more realistically predict the complexity of the secondary flows near the vocal fold and the resultant particle depositions. Inter-subject variabilities of the glottis motion patterns were observed, and their influences on particle transport dynamics are not uniform. Parametric analyses also demonstrate that the maximum deformation ratio of the glottis is a key feature to describe whether the glottis motion can enhance or reduce particle depositions in the mouth-to-trachea region, over the static glottis model. The glottis motion shows a significant influence on the accuracy of predicting inhaled particle dynamics, and it should be integrated into CFPD simulations validated by subject-specific glottis motion data from clinical studies in the future. Furthermore, the proposed dynamic glottis model has been demonstrated to be a computationally effective method to recover the physiologically realistic motions of the glottis, and ready to be added into the next-generation holistic virtual lung modeling approach. •A clinically optimized dynamic glottis (DG) model has been developed.•A generalized glottis motion function (GGMF) has been proposed and validated.•The static glottis assumption leads to significant errors on lung aerosol dynamics prediction.•The glottis motion needs to be modeled to predict lung aerosol dynamics precisely.