Limits...
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.

Show MeSH

Related in: MedlinePlus

Exhaled aerosol fingerprints (AFPs) for asthma with increasing severity.(a): constricted segmental bronchi 3 and 4 with increasing severities. The constriction levels are shown in (c). The exhaled particle distribution is shown in (b) while the concentration distribution is shown in (d). Fractal analysis FDs (±SD, n = 5) for the entire image and selected ROI is shown in (e) in terms of fractal dimension, lacunarity, and multigractal spectra.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4126729&req=5

pone-0104682-g009: Exhaled aerosol fingerprints (AFPs) for asthma with increasing severity.(a): constricted segmental bronchi 3 and 4 with increasing severities. The constriction levels are shown in (c). The exhaled particle distribution is shown in (b) while the concentration distribution is shown in (d). Fractal analysis FDs (±SD, n = 5) for the entire image and selected ROI is shown in (e) in terms of fractal dimension, lacunarity, and multigractal spectra.

Mentions: To test whether the exhaled AFPs are sensitive enough to distinguish the pathologic states of respiratory diseases, four levels of airway constrictions (D0, D1, D2, D3) caused by asthma have been considered, as illustrated in Figs. 9a and 9c. Exhaled particle distributions are shown in Fig. 9b. It is noted that the crescent-shaped void at the upper-left corner becomes more obvious with increasing severities. To further test the sensitivity of the aerosol voids to the disease severity, particles are released only from the ROI and their exhaled locations are plotted in red (Fig. 9A). For the zero-level constriction (D0), red particles are observed enclosing the region that is otherwise aerosol-void for asthmatic scenarios (D1–3). With increasing severity, red particle contours shrink progressively in space (Fig. 9B), with drastically elevated concentration in certain regions (solid arrow) and decreased concentration in other regions (hollow arrow) (Fig. 9D), reflecting the asthma condition at the ROI. As a result, these tagged particles could not only be used to evaluate the severity of airway constriction, but also to discover the location of the disease.


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)

Exhaled aerosol fingerprints (AFPs) for asthma with increasing severity.(a): constricted segmental bronchi 3 and 4 with increasing severities. The constriction levels are shown in (c). The exhaled particle distribution is shown in (b) while the concentration distribution is shown in (d). Fractal analysis FDs (±SD, n = 5) for the entire image and selected ROI is shown in (e) in terms of fractal dimension, lacunarity, and multigractal spectra.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104682-g009: Exhaled aerosol fingerprints (AFPs) for asthma with increasing severity.(a): constricted segmental bronchi 3 and 4 with increasing severities. The constriction levels are shown in (c). The exhaled particle distribution is shown in (b) while the concentration distribution is shown in (d). Fractal analysis FDs (±SD, n = 5) for the entire image and selected ROI is shown in (e) in terms of fractal dimension, lacunarity, and multigractal spectra.
Mentions: To test whether the exhaled AFPs are sensitive enough to distinguish the pathologic states of respiratory diseases, four levels of airway constrictions (D0, D1, D2, D3) caused by asthma have been considered, as illustrated in Figs. 9a and 9c. Exhaled particle distributions are shown in Fig. 9b. It is noted that the crescent-shaped void at the upper-left corner becomes more obvious with increasing severities. To further test the sensitivity of the aerosol voids to the disease severity, particles are released only from the ROI and their exhaled locations are plotted in red (Fig. 9A). For the zero-level constriction (D0), red particles are observed enclosing the region that is otherwise aerosol-void for asthmatic scenarios (D1–3). With increasing severity, red particle contours shrink progressively in space (Fig. 9B), with drastically elevated concentration in certain regions (solid arrow) and decreased concentration in other regions (hollow arrow) (Fig. 9D), reflecting the asthma condition at the ROI. As a result, these tagged particles could not only be used to evaluate the severity of airway constriction, but also to discover the location of the disease.

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