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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 expiratory flow fields among the four models of A (Normal), B (carina tumor), C (bronchial tumor), and D (asthma).The presence of an airway obstruction disturbs the exhaled airflow field which will further distort the trajectories of entrained particles and gives rise to different exhaled aerosol profiles. The characteristics of flow distortions depend on the location and size of the airway obstructions.
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pone-0104682-g002: Comparison of expiratory flow fields among the four models of A (Normal), B (carina tumor), C (bronchial tumor), and D (asthma).The presence of an airway obstruction disturbs the exhaled airflow field which will further distort the trajectories of entrained particles and gives rise to different exhaled aerosol profiles. The characteristics of flow distortions depend on the location and size of the airway obstructions.

Mentions: Figure 2 shows the comparison of expiratory airflows among the four models. The presence of an airway obstruction noticeably alters the airflow field near the diseased site as shown by the distorted streamlines and velocity distributions (top panel in Fig. 2). The variation of the velocity field is further visualized using the cross-sectional particle distributions (middle panel) close to the carina (Slice A–A’). The tracer particles have a diameter of 1 µm and closely follow the airflow. It is observed that both the location and size of the airway obstruction influence the exhaled flows, which give rise to different expiratory particle patterns. The lower panel of Fig. 2 shows the velocity distributions at Slice A–A’ of the four models in both horizontal (Z) and transverse (Y) directions. Compared to the control case (Model A), the most dramatic difference is noted in Model B (carina tumor) which has the largest tumor size and is closest to the sampling plane A–A’. In contrast, Model C (segmental bronchial tumor) gives very similar velocity profiles due to its smaller tumor size and larger distance from Slice A–A’. However, this similar airflow does not necessarily imply similar particle profiles, which depends on both local airflows and upstream particle histories [44]. The time-integrative nature of the particle behaviors can be seen clearly by comparing Model A and C in terms of their similar velocity profiles and different particle distributions at Slice A–A’ (Fig. 2). Lower velocities are observed in Model D (Fig. 2) due to the severely constricted segmental bronchus and associated higher flow resistances. There is a spot that is devoid of particles in the top right corner of Model D, which is presumably caused by the two constricted bronchus. The difference in airflows gradually diminishes as they move towards the mouth; however, the particle profiles are still different due to their time-integrative properties.


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 expiratory flow fields among the four models of A (Normal), B (carina tumor), C (bronchial tumor), and D (asthma).The presence of an airway obstruction disturbs the exhaled airflow field which will further distort the trajectories of entrained particles and gives rise to different exhaled aerosol profiles. The characteristics of flow distortions depend on the location and size of the airway obstructions.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104682-g002: Comparison of expiratory flow fields among the four models of A (Normal), B (carina tumor), C (bronchial tumor), and D (asthma).The presence of an airway obstruction disturbs the exhaled airflow field which will further distort the trajectories of entrained particles and gives rise to different exhaled aerosol profiles. The characteristics of flow distortions depend on the location and size of the airway obstructions.
Mentions: Figure 2 shows the comparison of expiratory airflows among the four models. The presence of an airway obstruction noticeably alters the airflow field near the diseased site as shown by the distorted streamlines and velocity distributions (top panel in Fig. 2). The variation of the velocity field is further visualized using the cross-sectional particle distributions (middle panel) close to the carina (Slice A–A’). The tracer particles have a diameter of 1 µm and closely follow the airflow. It is observed that both the location and size of the airway obstruction influence the exhaled flows, which give rise to different expiratory particle patterns. The lower panel of Fig. 2 shows the velocity distributions at Slice A–A’ of the four models in both horizontal (Z) and transverse (Y) directions. Compared to the control case (Model A), the most dramatic difference is noted in Model B (carina tumor) which has the largest tumor size and is closest to the sampling plane A–A’. In contrast, Model C (segmental bronchial tumor) gives very similar velocity profiles due to its smaller tumor size and larger distance from Slice A–A’. However, this similar airflow does not necessarily imply similar particle profiles, which depends on both local airflows and upstream particle histories [44]. The time-integrative nature of the particle behaviors can be seen clearly by comparing Model A and C in terms of their similar velocity profiles and different particle distributions at Slice A–A’ (Fig. 2). Lower velocities are observed in Model D (Fig. 2) due to the severely constricted segmental bronchus and associated higher flow resistances. There is a spot that is devoid of particles in the top right corner of Model D, which is presumably caused by the two constricted bronchus. The difference in airflows gradually diminishes as they move towards the mouth; however, the particle profiles are still different due to their time-integrative properties.

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