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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

Visual and quantitative comparison of exhaled aerosol fingerprints (AFPs) among the four models.The first row shows particle distributions collected at the mouth. The second row shows the particle concentration distributions, and the third shows the concentration differences relative to the normal condition.
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pone-0104682-g003: Visual and quantitative comparison of exhaled aerosol fingerprints (AFPs) among the four models.The first row shows particle distributions collected at the mouth. The second row shows the particle concentration distributions, and the third shows the concentration differences relative to the normal condition.

Mentions: The exhaled particles collect into a pattern that is unique to the lung structure and can be considered the “fingerprint” of that lung. The first row of Fig. 3 displays particle distributions collected at the mouth for an aerosol size of 1 µm and a flow rate of 30 L/min. Overall, each of the four models exhibits a pair of vortexes and an asymmetrical aerosol distribution, the latter of which may stem from the asymmetry of the right and left lungs. However, discrepancies in aerosol distributions are still apparent among the four models. Compared to Model A, Model B (tracheal carina tumor) and C (left segmental bronchial tumor) both exhibit very different patterns. First, the two vortexes and the central stripe in Models B and C are much less defined. The left vortex almost vanishes in Model B. Secondly, for Models B and C with obstructive tumors, an increased portion of aerosols are trapped in the airway due to elevated inertia impaction. However, even though the particle patterns of Models B and C look similar, careful examinations still reveals discernible differences. The presence of a carina tumor (Model B) disturbs the aerosol distribution in both the lower-left and lower-right regions, while the influence from the left segmental bronchial tumor is mainly limited to the lower-left region (top panel in Fig. 3). For Model D with two severely constricted bronchi, the exhaled aerosol profile resembles that of Model A, except for one crescent-shaped region at the upper left corner that is devoid of particles. This observation clearly corroborates the hypothesis that the exhaled aerosol distribution is the fingerprint of the lung structure, which can be used to probe structure remodeling by lung tumors and other respiratory diseases.


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)

Visual and quantitative comparison of exhaled aerosol fingerprints (AFPs) among the four models.The first row shows particle distributions collected at the mouth. The second row shows the particle concentration distributions, and the third shows the concentration differences relative to the normal condition.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104682-g003: Visual and quantitative comparison of exhaled aerosol fingerprints (AFPs) among the four models.The first row shows particle distributions collected at the mouth. The second row shows the particle concentration distributions, and the third shows the concentration differences relative to the normal condition.
Mentions: The exhaled particles collect into a pattern that is unique to the lung structure and can be considered the “fingerprint” of that lung. The first row of Fig. 3 displays particle distributions collected at the mouth for an aerosol size of 1 µm and a flow rate of 30 L/min. Overall, each of the four models exhibits a pair of vortexes and an asymmetrical aerosol distribution, the latter of which may stem from the asymmetry of the right and left lungs. However, discrepancies in aerosol distributions are still apparent among the four models. Compared to Model A, Model B (tracheal carina tumor) and C (left segmental bronchial tumor) both exhibit very different patterns. First, the two vortexes and the central stripe in Models B and C are much less defined. The left vortex almost vanishes in Model B. Secondly, for Models B and C with obstructive tumors, an increased portion of aerosols are trapped in the airway due to elevated inertia impaction. However, even though the particle patterns of Models B and C look similar, careful examinations still reveals discernible differences. The presence of a carina tumor (Model B) disturbs the aerosol distribution in both the lower-left and lower-right regions, while the influence from the left segmental bronchial tumor is mainly limited to the lower-left region (top panel in Fig. 3). For Model D with two severely constricted bronchi, the exhaled aerosol profile resembles that of Model A, except for one crescent-shaped region at the upper left corner that is devoid of particles. This observation clearly corroborates the hypothesis that the exhaled aerosol distribution is the fingerprint of the lung structure, which can be used to probe structure remodeling by lung tumors and other respiratory diseases.

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