Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.

Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.

<h4>Background</h4>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...

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Autores principales: Jinxiang Xi, Xiuhua A Si, JongWon Kim, Edward Mckee, En-Bing Lin
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2014
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Medicine
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Acceso en línea:https://doaj.org/article/43c6b9dd1b4a410ea3e97bfeffb03db2
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spelling oai:doaj.org-article:43c6b9dd1b4a410ea3e97bfeffb03db22021-11-25T06:05:30ZExhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.1932-620310.1371/journal.pone.0104682https://doaj.org/article/43c6b9dd1b4a410ea3e97bfeffb03db22014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25105680/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Objective and methods</h4>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.<h4>Findings</h4>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.<h4>Conclusion</h4>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.Jinxiang XiXiuhua A SiJongWon KimEdward MckeeEn-Bing LinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 8, p e104682 (2014)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Jinxiang Xi
Xiuhua A Si
JongWon Kim
Edward Mckee
En-Bing Lin
Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
description <h4>Background</h4>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.<h4>Objective and methods</h4>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.<h4>Findings</h4>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.<h4>Conclusion</h4>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.
format article
author Jinxiang Xi
Xiuhua A Si
JongWon Kim
Edward Mckee
En-Bing Lin
author_facet Jinxiang Xi
Xiuhua A Si
JongWon Kim
Edward Mckee
En-Bing Lin
author_sort Jinxiang Xi
title Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
title_short Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
title_full Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
title_fullStr Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
title_full_unstemmed Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
title_sort exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
publisher Public Library of Science (PLoS)
publishDate 2014
url https://doaj.org/article/43c6b9dd1b4a410ea3e97bfeffb03db2
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AT jongwonkim exhaledaerosolpatterndiscloseslungstructuralabnormalityasensitivitystudyusingcomputationalmodelingandfractalanalysis
AT edwardmckee exhaledaerosolpatterndiscloseslungstructuralabnormalityasensitivitystudyusingcomputationalmodelingandfractalanalysis
AT enbinglin exhaledaerosolpatterndiscloseslungstructuralabnormalityasensitivitystudyusingcomputationalmodelingandfractalanalysis
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