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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

Schematic of lung diseases and airflow dynamics.(a) Lung diseases subtypes: squamous cell cancer (SCC), adenocarcinoma (AC), large cell cancer (LCC), and small cell lung cancer (SCLC), and asthma. (b) Lung models with healthy and diseased conditions: Model A with normal airway structure, Model B with an adenocarcinoma at the carina ridge (carina tumor), Model C with a squamous cell carcinoma on a left segmental bronchus (bronchial tumor), and Model D with constricted segmental bronchi (asthma).
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pone-0104682-g001: Schematic of lung diseases and airflow dynamics.(a) Lung diseases subtypes: squamous cell cancer (SCC), adenocarcinoma (AC), large cell cancer (LCC), and small cell lung cancer (SCLC), and asthma. (b) Lung models with healthy and diseased conditions: Model A with normal airway structure, Model B with an adenocarcinoma at the carina ridge (carina tumor), Model C with a squamous cell carcinoma on a left segmental bronchus (bronchial tumor), and Model D with constricted segmental bronchi (asthma).

Mentions: In spite of these advantages, gas-signature based breath devices only measure the presence and concentration of exhaled gas chemicals. They do not provide information on where these chemicals are produced (the cancer site) or the level of airway remodeling, both of which are crucial in cancer treatment planning. The site and degree of airway remodeling can be substantially different for different lung cancers (Fig. 1a). Any alternative that can locate the malignant sites in a safer and less expensive way would be highly desirable. Currently, this information can only be obtained with the help of radiological techniques such as CT or PET. A number of studies have explored the use of aerosols as a lung diagnostic tool, such as the aerosol bolus dispersion (ABD) method [10], [11], [12]. However, the ABD method does not provide new information about the lung function compared to existing pulmonary function tests [12]. More recently, Xi et al. [13] proposed a new aerosol breath test that has the potential to detect the disease and locate its site. This method arises from persistent observations of unique deposition patterns with respect to prescribed geometry and breathing conditions [14], [15], [16]. We hypothesize that each airway structure has a signature aerosol fingerprint (AFP), as opposed to the gas fingerprint discussed before. Accordingly, any deviation from the normal pattern may indicate an abnormality inside the airway, which can be retrieved with an inverse numerical approach developed by Xi et al. [17]. The subsequent questions are: how can we quantitate the exhaled AFP patterns from different airway geometries? Will the exhaled AFPs be sensitive enough to detect airway structural changes? More importantly, how can we use this information to predict the presence and location of airway abnormalities based on samples of exhaled aerosol profiles?


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)

Schematic of lung diseases and airflow dynamics.(a) Lung diseases subtypes: squamous cell cancer (SCC), adenocarcinoma (AC), large cell cancer (LCC), and small cell lung cancer (SCLC), and asthma. (b) Lung models with healthy and diseased conditions: Model A with normal airway structure, Model B with an adenocarcinoma at the carina ridge (carina tumor), Model C with a squamous cell carcinoma on a left segmental bronchus (bronchial tumor), and Model D with constricted segmental bronchi (asthma).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104682-g001: Schematic of lung diseases and airflow dynamics.(a) Lung diseases subtypes: squamous cell cancer (SCC), adenocarcinoma (AC), large cell cancer (LCC), and small cell lung cancer (SCLC), and asthma. (b) Lung models with healthy and diseased conditions: Model A with normal airway structure, Model B with an adenocarcinoma at the carina ridge (carina tumor), Model C with a squamous cell carcinoma on a left segmental bronchus (bronchial tumor), and Model D with constricted segmental bronchi (asthma).
Mentions: In spite of these advantages, gas-signature based breath devices only measure the presence and concentration of exhaled gas chemicals. They do not provide information on where these chemicals are produced (the cancer site) or the level of airway remodeling, both of which are crucial in cancer treatment planning. The site and degree of airway remodeling can be substantially different for different lung cancers (Fig. 1a). Any alternative that can locate the malignant sites in a safer and less expensive way would be highly desirable. Currently, this information can only be obtained with the help of radiological techniques such as CT or PET. A number of studies have explored the use of aerosols as a lung diagnostic tool, such as the aerosol bolus dispersion (ABD) method [10], [11], [12]. However, the ABD method does not provide new information about the lung function compared to existing pulmonary function tests [12]. More recently, Xi et al. [13] proposed a new aerosol breath test that has the potential to detect the disease and locate its site. This method arises from persistent observations of unique deposition patterns with respect to prescribed geometry and breathing conditions [14], [15], [16]. We hypothesize that each airway structure has a signature aerosol fingerprint (AFP), as opposed to the gas fingerprint discussed before. Accordingly, any deviation from the normal pattern may indicate an abnormality inside the airway, which can be retrieved with an inverse numerical approach developed by Xi et al. [17]. The subsequent questions are: how can we quantitate the exhaled AFP patterns from different airway geometries? Will the exhaled AFPs be sensitive enough to detect airway structural changes? More importantly, how can we use this information to predict the presence and location of airway abnormalities based on samples of exhaled aerosol profiles?

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