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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

Fractal analysis of exhaled particle distributions using Box Counting method.Calculation of fractal dimension (FD) of Model A using regression analysis is exemplified in (a). FDs FDs (±SD, n = 5) for the four models are shown in (b) and (c) for the entire image and selected region of interest (ROI), respectively. Significance indicated by *(p<0.05) and **(p<0.01). (d) shows the local FD distribution on a normalized caliber size of 1/6×1/6. The color code was based on the fractal dimension ratio β(i) = FD(i)/FD(A), i = B, C and D. The color pattern is unique to each airway abnormality.
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pone-0104682-g005: Fractal analysis of exhaled particle distributions using Box Counting method.Calculation of fractal dimension (FD) of Model A using regression analysis is exemplified in (a). FDs FDs (±SD, n = 5) for the four models are shown in (b) and (c) for the entire image and selected region of interest (ROI), respectively. Significance indicated by *(p<0.05) and **(p<0.01). (d) shows the local FD distribution on a normalized caliber size of 1/6×1/6. The color code was based on the fractal dimension ratio β(i) = FD(i)/FD(A), i = B, C and D. The color pattern is unique to each airway abnormality.

Mentions: Monofractal analysis of exhaled aerosols using the box counting method is shown in Fig. 5 for the four models. We consider the fractal dimensions from two perspectives: in the entire sample image and in the selected region of interest (ROI), as illustrated in Fig. 5a. The correlation factor of data linear regression is 0.978 for the entire region, indicating that the particle distribution exhibits a statistically fractal feature (Fig. 5a). The local distribution also exhibits a fractal feature (R2 = 0.964), except that it has a smaller fractal dimension (FDROI = 1.274) and is less complex than that of the entire region (FDEntire = 1.4423).


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)

Fractal analysis of exhaled particle distributions using Box Counting method.Calculation of fractal dimension (FD) of Model A using regression analysis is exemplified in (a). FDs FDs (±SD, n = 5) for the four models are shown in (b) and (c) for the entire image and selected region of interest (ROI), respectively. Significance indicated by *(p<0.05) and **(p<0.01). (d) shows the local FD distribution on a normalized caliber size of 1/6×1/6. The color code was based on the fractal dimension ratio β(i) = FD(i)/FD(A), i = B, C and D. The color pattern is unique to each airway abnormality.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104682-g005: Fractal analysis of exhaled particle distributions using Box Counting method.Calculation of fractal dimension (FD) of Model A using regression analysis is exemplified in (a). FDs FDs (±SD, n = 5) for the four models are shown in (b) and (c) for the entire image and selected region of interest (ROI), respectively. Significance indicated by *(p<0.05) and **(p<0.01). (d) shows the local FD distribution on a normalized caliber size of 1/6×1/6. The color code was based on the fractal dimension ratio β(i) = FD(i)/FD(A), i = B, C and D. The color pattern is unique to each airway abnormality.
Mentions: Monofractal analysis of exhaled aerosols using the box counting method is shown in Fig. 5 for the four models. We consider the fractal dimensions from two perspectives: in the entire sample image and in the selected region of interest (ROI), as illustrated in Fig. 5a. The correlation factor of data linear regression is 0.978 for the entire region, indicating that the particle distribution exhibits a statistically fractal feature (Fig. 5a). The local distribution also exhibits a fractal feature (R2 = 0.964), except that it has a smaller fractal dimension (FDROI = 1.274) and is less complex than that of the entire region (FDEntire = 1.4423).

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