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

Jinxiang Xi, Xiuhua A. Si, JongWon Kim, Edward Mckee, En Bing Lin

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere104682
JournalPLoS One
Volume9
Issue number8
DOIs
StatePublished - Aug 8 2014
Externally publishedYes

Fingerprint

Fractals
aerosols
Aerosols
lungs
Lung
Dermatoglyphics
Pulmonary diseases
respiratory tract diseases
asthma
Spatial distribution
Tumors
Asthma
spatial distribution
breath tests
Pulmonary Ventilation
neoplasms
Breath Tests
fractal dimensions
Normal Distribution
Physiology

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Exhaled aerosol pattern discloses lung structural abnormality : A sensitivity study using computational modeling and fractal analysis. / Xi, Jinxiang; Si, Xiuhua A.; Kim, JongWon; Mckee, Edward; Lin, En Bing.

In: PLoS One, Vol. 9, No. 8, e104682, 08.08.2014.

Research output: Contribution to journalArticle

Xi, Jinxiang ; Si, Xiuhua A. ; Kim, JongWon ; Mckee, Edward ; Lin, En Bing. / Exhaled aerosol pattern discloses lung structural abnormality : A sensitivity study using computational modeling and fractal analysis. In: PLoS One. 2014 ; Vol. 9, No. 8.
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