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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

Multifractal analysis of exhaled particle concentrations.The 3-D plots of particle concentrations are shown in (a), (b), (c), (d). Comparison of the multifractal spectra among the four models are shown in (e) for the entire region and in (f) for the selected region of interest (ROI).
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pone-0104682-g008: Multifractal analysis of exhaled particle concentrations.The 3-D plots of particle concentrations are shown in (a), (b), (c), (d). Comparison of the multifractal spectra among the four models are shown in (e) for the entire region and in (f) for the selected region of interest (ROI).

Mentions: The multifractal spectra for the gray-sale images of the exhaled aerosol concentration profiles are shown in Fig. 8. The aerosol concentration images are first shown as the 3-D plots (Figs. 8a–d), which exhibit very different patterns among the four models. Considering the entire-region analysis (Fig. 8e), a small geometric deviation such as bronchial tumor (Model C) leads to a similar profile as that of the control case, while large geometric variations leads to spectra profiles that are much different from the control, which is consistent with Fig. 5. For the selected ROI, the spectra are more symmetrical than those of the entire region. The ROI-based spectra also have a smaller range of f(α) as well as a narrower range of α compared to those of the entire image suggesting lower multifractality of the ROI images. Particularly, the ROI-based spectrum for Model B has the smallest ranges of f(α) and α (Fig. 8f), which also has the smallest monofractal dimension (Fig. 5c) and largest lacunarity (Fig. 6b). This is in line with results in previous studies [20], [47] that a pattern with a more asymmetric spectrum and a narrower range of α generally has higher density and lower lacunarity. Examples of such patterns include soils with massive structures and low porosity [47] and vascular beds with high complexity and lower emptiness [20]. In this study, the exhaled aerosol profiles of the entire region are more complex and heterogeneous than that of the ROI. Apparent differences in the ROI spectra are also observed among the four models (Fig. 8f), lending further evidence that multifractal analysis might be adequate in identifying the geometry-associated aerosol variations.


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)

Multifractal analysis of exhaled particle concentrations.The 3-D plots of particle concentrations are shown in (a), (b), (c), (d). Comparison of the multifractal spectra among the four models are shown in (e) for the entire region and in (f) for the selected region of interest (ROI).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104682-g008: Multifractal analysis of exhaled particle concentrations.The 3-D plots of particle concentrations are shown in (a), (b), (c), (d). Comparison of the multifractal spectra among the four models are shown in (e) for the entire region and in (f) for the selected region of interest (ROI).
Mentions: The multifractal spectra for the gray-sale images of the exhaled aerosol concentration profiles are shown in Fig. 8. The aerosol concentration images are first shown as the 3-D plots (Figs. 8a–d), which exhibit very different patterns among the four models. Considering the entire-region analysis (Fig. 8e), a small geometric deviation such as bronchial tumor (Model C) leads to a similar profile as that of the control case, while large geometric variations leads to spectra profiles that are much different from the control, which is consistent with Fig. 5. For the selected ROI, the spectra are more symmetrical than those of the entire region. The ROI-based spectra also have a smaller range of f(α) as well as a narrower range of α compared to those of the entire image suggesting lower multifractality of the ROI images. Particularly, the ROI-based spectrum for Model B has the smallest ranges of f(α) and α (Fig. 8f), which also has the smallest monofractal dimension (Fig. 5c) and largest lacunarity (Fig. 6b). This is in line with results in previous studies [20], [47] that a pattern with a more asymmetric spectrum and a narrower range of α generally has higher density and lower lacunarity. Examples of such patterns include soils with massive structures and low porosity [47] and vascular beds with high complexity and lower emptiness [20]. In this study, the exhaled aerosol profiles of the entire region are more complex and heterogeneous than that of the ROI. Apparent differences in the ROI spectra are also observed among the four models (Fig. 8f), lending further evidence that multifractal analysis might be adequate in identifying the geometry-associated aerosol variations.

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