Figure 1.
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).
Table 1.
The location and size of airway models with benign and malign conditions.
Figure 2.
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.
Figure 3.
Visual and quantitative comparison of exhaled aerosol fingerprints (AFPs) among the four models.
The first row shows particle distributions collected at the mouth. The second row shows the particle concentration distributions, and the third shows the concentration differences relative to the normal condition.
Figure 4.
Statistical analysis of exhaled particle distributions at different directions: (a) horizontal, (b) vertical, (c) radial, and (d) circumferential (rose plot).
The patterns of exhaled particles among the four models can be distinguished by comparing the spatial distributions of particles in two mutually orthogonal directions.
Figure 5.
Fractal analysis of exhaled particle distributions using Box Counting method.
Calculation of fractal dimension (FD) of Model A using regression analysis is exemplified in (a). FDs FDs (±SD, n = 5) for the four models are shown in (b) and (c) for the entire image and selected region of interest (ROI), respectively. Significance indicated by *(p<0.05) and **(p<0.01). (d) shows the local FD distribution on a normalized caliber size of 1/6×1/6. The color code was based on the fractal dimension ratio β(i) = FD(i)/FD(A), i = B, C and D. The color pattern is unique to each airway abnormality.
Figure 6.
Comparison of lacunarity values FDs (±SD, n = 5) among the four models for (a) entire region, and (b) selected region of interest (ROI).
Significance indicated by *(p<0.05) and **(p<0.01).
Figure 7.
Spectra of generalized dimensions Dq versus q among the four models for (a) the entire region and (b) the selected region of interest (ROI).
Figure 8.
Multifractal analysis of exhaled particle concentrations.
The 3-D plots of particle concentrations are shown in (a), (b), (c), (d). Comparison of the multifractal spectra among the four models are shown in (e) for the entire region and in (f) for the selected region of interest (ROI).
Figure 9.
Exhaled aerosol fingerprints (AFPs) for asthma with increasing severity.
(a): constricted segmental bronchi 3 and 4 with increasing severities. The constriction levels are shown in (c). The exhaled particle distribution is shown in (b) while the concentration distribution is shown in (d). Fractal analysis FDs (±SD, n = 5) for the entire image and selected ROI is shown in (e) in terms of fractal dimension, lacunarity, and multigractal spectra.