title: Exhaled aerosol pattern discloses lung structural abnormality: a sensitivity study using computational modeling and fractal analysis.
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
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.
authors.forename: Jinxiang
authors.initials: J
authors.lastname: Xi
authors.affiliation: Science Division, Calvin College, Grand Rapids, Michigan, United States of America.
authors.forename: Xiuhua A
authors.initials: XA
authors.lastname: Si
authors.affiliation: School of Engineering and Technology, Central Michigan University, Mount Pleasant, Michigan, United States of America.
authors.forename: JongWon
authors.initials: J
authors.lastname: Kim
authors.affiliation: College of Medicine, Central Michigan University, Mount Pleasant, Michigan, United States of America.
authors.forename: Edward
authors.initials: E
authors.lastname: Mckee
authors.affiliation: Department of Mathematics, Central Michigan University, Mount Pleasant, Michigan, United States of America.
authors.forename: En-Bing
authors.initials: EB
authors.lastname: Lin
mesh_terms: D000336:Aerosols; D001249:Asthma / Q000175:diagnosis* / Q000473:pathology; D001944:Breath Tests; D001980:Bronchi / Q000473:pathology; D001984:Bronchial Neoplasms / Q000175:diagnosis*; D003198:Computer Simulation; D045853:Exhalation; D017709:Fractals; D006801:Humans; D008168:Lung / Q000473:pathology*; D008953:Models, Anatomic; D014132:Trachea / Q000473:pathology; D014134:Tracheal Neoplasms / Q000175:diagnosis*
chemical_list: D000336:Aerosols
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