Miloslav Belka

A method for in vitro regional aerosol deposition measurement in a model of the human tracheobronchial tree by the positron emission tomography

Researchers have been studying aerosol transport in human lungs for some decades. The overall lung deposition can be predicted with sufficient precision nowadays. However, the prediction of local deposition remains an unsolved problem. Numerical modeling of aerosol transport can provide detailed data with such precision and spatial resolution which were unavailable in the past. Yet, the necessary validation of numerical results represents a difficult task, as the experimental data in a sufficient spatial resolution are hardly available. This article introduces a method based on positron emission tomography, which allows acquisition of detailed experimental data on local aerosol deposition in a realistic model of human lungs. The method utilizes the Condensation Monodisperse Aerosol Generator modified for a safe production of radioactive aerosol particles and a special measuring rig. The scanning of the model is performed on a positron emission tomography–computed tomography scanner. The evaluation of aerosol deposition is based on a volume radioactivity analysis in a specialized, yet publicly available software. The reliability of the method was tested and its first results are discussed in the article. The measurements performed using the presented method can serve for validation of numerical simulations, since the presented lung model digital geometry is available.

Multicomponent aerosol particle deposition in a realistic cast of the human upper respiratory tract

Abstract Inhalation of aerosols generated by electronic cigarettes leads to deposition of multiple chemical compounds in the human airways. In this work, an experimental method to determine regional deposition of multicomponent aerosols in an in vitro segmented, realistic human lung geometry was developed and applied to two aerosols, i.e. a monodisperse glycerol aerosol and a multicomponent aerosol. The method comprised the following steps: (1) lung cast model preparation, (2) aerosol generation and exposure, (3) extraction of deposited mass, (4) chemical quantification and (5) data processing. The method showed good agreement with literature data for the deposition efficiency when using a monodisperse glycerol aerosol, with a mass median aerodynamic diameter (MMAD) of 2.3 μm and a constant flow rate of 15 L/min. The highest deposition surface density rate was observed in the bifurcation segments, indicating inertial impaction deposition. The experimental method was also applied to the deposition of a nebulized multicomponent aerosol with a MMAD of 0.50 μm and a constant flow rate of 15 L/min. The deposited amounts of glycerol, propylene glycol and nicotine were quantified. The three analyzed compounds showed similar deposition patterns and fractions as for the monodisperse glycerol aerosol, indicating that the compounds most likely deposited as parts of the same droplets. The developed method can be used to determine regional deposition for multicomponent aerosols, provided that the compounds are of low volatility. The generated data can be used to validate aerosol deposition simulations and to gain insight in deposition of electronic cigarette aerosols in human airways.

Application of image analysis method to detection and counting of glass fibers from filter samples

ABSTRACT Man-made vitreous fibers (MMVFs) are noncrystalline substances made of glass, rock or slag and are widely used as thermal or acoustic insulation materials. There is continued concern about their potential health impacts and thus, their dosimetry and behavior in the environment still require study using filters to collect fiber samples. After deposition or exposure measurements of MMVFs it is often necessary to analyze the filters with deposited fibers. This task is tedious, time-consuming, and requires skill. Therefore, many researchers have tried to simplify or automatize fiber detection and quantification. This article describes features of our in-house software, which automatically detects and counts fibers in images of filter samples. The image analysis is based on the use of a histogram equalization and an adaptive radial convolution filter that enhances fiber contrast and thus, improves the fiber identification. The accuracy of the software analysis was verified by comparison with manual counting using ordinary phase-contrast microscopy method. The correlation between the methods was very high (coefficient of determination was 0.977). However, there were some discrepancies caused by false identifications, which led to implementation of manual corrective functions. Copyright