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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

Comparison of lacunarity values FDs (±SD, n = 5) among the four models for (a) entire region, and (b) selected region of interest (ROI).Significance indicated by *(p<0.05) and **(p<0.01).
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pone-0104682-g006: Comparison of lacunarity values FDs (±SD, n = 5) among the four models for (a) entire region, and (b) selected region of interest (ROI).Significance indicated by *(p<0.05) and **(p<0.01).

Mentions: In general, measures of lacunarity correspond to visual impressions of uniformity, where low lacunarity implies homogeneity and high lacunarity implies heterogeneity. From Fig. 6a, the differences of lacunarity among the four models are more pronounced than those of fractal dimensions for both the entire sample and selected region of interest (ROI). Specifically, the lacunarity of Model B differs significantly (P<0.01) from the control case (Model A) even though their fractal dimensions are similar. As discussed before, fractal dimension and lacunarity are statistical indexes of complexity and heterogeneity, respectively, and do not necessarily correlate to each other. Knowing lacunarity helps to separate exhaled aerosol images with close fractal dimensions. Comparing ROI-based images in Fig. 6b, Model B (carinar tumor) has the largest lacunarity (Fig. 6b) and the smallest fractal dimension (Fig. 5b) among the four models while the variations among the other three models are insignificant. This suggests a strong correlation between the carina tumor to large variations of fractal dimension and lacunarity in the ROI, which will be further analyzed using multifractal spectrum analysis in the following section.


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)

Comparison of lacunarity values FDs (±SD, n = 5) among the four models for (a) entire region, and (b) selected region of interest (ROI).Significance indicated by *(p<0.05) and **(p<0.01).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104682-g006: Comparison of lacunarity values FDs (±SD, n = 5) among the four models for (a) entire region, and (b) selected region of interest (ROI).Significance indicated by *(p<0.05) and **(p<0.01).
Mentions: In general, measures of lacunarity correspond to visual impressions of uniformity, where low lacunarity implies homogeneity and high lacunarity implies heterogeneity. From Fig. 6a, the differences of lacunarity among the four models are more pronounced than those of fractal dimensions for both the entire sample and selected region of interest (ROI). Specifically, the lacunarity of Model B differs significantly (P<0.01) from the control case (Model A) even though their fractal dimensions are similar. As discussed before, fractal dimension and lacunarity are statistical indexes of complexity and heterogeneity, respectively, and do not necessarily correlate to each other. Knowing lacunarity helps to separate exhaled aerosol images with close fractal dimensions. Comparing ROI-based images in Fig. 6b, Model B (carinar tumor) has the largest lacunarity (Fig. 6b) and the smallest fractal dimension (Fig. 5b) among the four models while the variations among the other three models are insignificant. This suggests a strong correlation between the carina tumor to large variations of fractal dimension and lacunarity in the ROI, which will be further analyzed using multifractal spectrum analysis in the following section.

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