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Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.

Xi J, Si XA, Kim J, Mckee E, Lin EB - PLoS ONE (2014)

Bottom Line: With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest.Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum.Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.

ABSTRACT

Background: Exhaled aerosol patterns, also called aerosol fingerprints, provide clues to the health of the lung and can be used to detect disease-modified airway structures. The key is how to decode the exhaled aerosol fingerprints and retrieve the lung structural information for a non-invasive identification of respiratory diseases.

Objective and methods: In this study, a CFD-fractal analysis method was developed to quantify exhaled aerosol fingerprints and applied it to one benign and three malign conditions: a tracheal carina tumor, a bronchial tumor, and asthma. Respirations of tracer aerosols of 1 µm at a flow rate of 30 L/min were simulated, with exhaled distributions recorded at the mouth. Large eddy simulations and a Lagrangian tracking approach were used to simulate respiratory airflows and aerosol dynamics. Aerosol morphometric measures such as concentration disparity, spatial distributions, and fractal analysis were applied to distinguish various exhaled aerosol patterns.

Findings: Utilizing physiology-based modeling, we demonstrated substantial differences in exhaled aerosol distributions among normal and pathological airways, which were suggestive of the disease location and extent. With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest. Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum. Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma.

Conclusion: Aerosol-fingerprint-based breath tests disclose clues about the site and severity of lung diseases and appear to be sensitive enough to be a practical tool for diagnosis and prognosis of respiratory diseases with structural abnormalities.

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Related in: MedlinePlus

Statistical analysis of exhaled particle distributions at different directions: (a) horizontal, (b) vertical, (c) radial, and (d) circumferential (rose plot).The patterns of exhaled particles among the four models can be distinguished by comparing the spatial distributions of particles in two mutually orthogonal directions.
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pone-0104682-g004: Statistical analysis of exhaled particle distributions at different directions: (a) horizontal, (b) vertical, (c) radial, and (d) circumferential (rose plot).The patterns of exhaled particles among the four models can be distinguished by comparing the spatial distributions of particles in two mutually orthogonal directions.

Mentions: Figure 4 shows the statistical distributions of exhaled particles in different directions. Taking Fig. 4a as an example, each point hereof represents the probability that the exhaled particles could be found at a specified horizontal distance x/X. In this example, the AFP image has been evenly divided into 50 bins along the horizontal direction. The number of particles in each bin is counted and normalized by the total exhaled particle numbers and the area of the bin, yielding the probability of particle distribution (%/mm2) at x/X. This is equivalent to scanning the AFP image in x direction with a scan resolution of D/50, with D being the diameter of the image. In order to quantify the spatial characteristics of the exhaled particle patterns, the images are scanned in four directions: horizontal, vertical, radial, and circumferential (rose plot). Generally, each airway model considered in this study exhibits a unique profile of spatial distribution probabilities, and therefore is applicable to supplement the classification of airway anomalies. Considering Figs. 4a and 4b, two spikes are observed for Model D (asthma) at x/X≈0.2 (Fig. 4a) and z/Z≈0.65 (Fig. 4b), which collectively point to the hot spot located at the normalized Cartesian coordinate (0.2, 0.65) as shown in Fig. 3 (concentration distribution D). The same hot spot also manifest itself as a spike in Fig. 4c at r/R≈0.7, and in Fig. 4d at θ≈70°. This indicates that directional particle distribution is a sensitive index of spatial pattern which could possibly be quantified with two mutually orthogonal directions.


Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.

Xi J, Si XA, Kim J, Mckee E, Lin EB - PLoS ONE (2014)

Statistical analysis of exhaled particle distributions at different directions: (a) horizontal, (b) vertical, (c) radial, and (d) circumferential (rose plot).The patterns of exhaled particles among the four models can be distinguished by comparing the spatial distributions of particles in two mutually orthogonal directions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126729&req=5

pone-0104682-g004: Statistical analysis of exhaled particle distributions at different directions: (a) horizontal, (b) vertical, (c) radial, and (d) circumferential (rose plot).The patterns of exhaled particles among the four models can be distinguished by comparing the spatial distributions of particles in two mutually orthogonal directions.
Mentions: Figure 4 shows the statistical distributions of exhaled particles in different directions. Taking Fig. 4a as an example, each point hereof represents the probability that the exhaled particles could be found at a specified horizontal distance x/X. In this example, the AFP image has been evenly divided into 50 bins along the horizontal direction. The number of particles in each bin is counted and normalized by the total exhaled particle numbers and the area of the bin, yielding the probability of particle distribution (%/mm2) at x/X. This is equivalent to scanning the AFP image in x direction with a scan resolution of D/50, with D being the diameter of the image. In order to quantify the spatial characteristics of the exhaled particle patterns, the images are scanned in four directions: horizontal, vertical, radial, and circumferential (rose plot). Generally, each airway model considered in this study exhibits a unique profile of spatial distribution probabilities, and therefore is applicable to supplement the classification of airway anomalies. Considering Figs. 4a and 4b, two spikes are observed for Model D (asthma) at x/X≈0.2 (Fig. 4a) and z/Z≈0.65 (Fig. 4b), which collectively point to the hot spot located at the normalized Cartesian coordinate (0.2, 0.65) as shown in Fig. 3 (concentration distribution D). The same hot spot also manifest itself as a spike in Fig. 4c at r/R≈0.7, and in Fig. 4d at θ≈70°. This indicates that directional particle distribution is a sensitive index of spatial pattern which could possibly be quantified with two mutually orthogonal directions.

Bottom Line: With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest.Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum.Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.

ABSTRACT

Background: Exhaled aerosol patterns, also called aerosol fingerprints, provide clues to the health of the lung and can be used to detect disease-modified airway structures. The key is how to decode the exhaled aerosol fingerprints and retrieve the lung structural information for a non-invasive identification of respiratory diseases.

Objective and methods: In this study, a CFD-fractal analysis method was developed to quantify exhaled aerosol fingerprints and applied it to one benign and three malign conditions: a tracheal carina tumor, a bronchial tumor, and asthma. Respirations of tracer aerosols of 1 µm at a flow rate of 30 L/min were simulated, with exhaled distributions recorded at the mouth. Large eddy simulations and a Lagrangian tracking approach were used to simulate respiratory airflows and aerosol dynamics. Aerosol morphometric measures such as concentration disparity, spatial distributions, and fractal analysis were applied to distinguish various exhaled aerosol patterns.

Findings: Utilizing physiology-based modeling, we demonstrated substantial differences in exhaled aerosol distributions among normal and pathological airways, which were suggestive of the disease location and extent. With fractal analysis, we also demonstrated that exhaled aerosol patterns exhibited fractal behavior in both the entire image and selected regions of interest. Each exhaled aerosol fingerprint exhibited distinct pattern parameters such as spatial probability, fractal dimension, lacunarity, and multifractal spectrum. Furthermore, a correlation of the diseased location and exhaled aerosol spatial distribution was established for asthma.

Conclusion: Aerosol-fingerprint-based breath tests disclose clues about the site and severity of lung diseases and appear to be sensitive enough to be a practical tool for diagnosis and prognosis of respiratory diseases with structural abnormalities.

Show MeSH
Related in: MedlinePlus