Quantitative Computed Tomography of the Lungs and Airways in Healthy Nonsmoking Adults

Abstract

Objectives The purposes of this study are to evaluate the normal range of quantitative CT (QCT) measures of lung attenuation and airway parameters measurements in healthy non-smoking adults and to identify sources of variation in those measures and possible means to adjust for them. Materials and Methods Within the COPDGene® study, 92 healthy non-Hispanic White non-smokers [29 male, 63 female, mean age 62.7 (SD 9.0), BMI 28.1 (SD 5.1)] underwent volumetric CT at full inspiration and at the end of a normal expiration. On QCT analysis (Pulmonary Workstation 2, VIDA Diagnostics), inspiratory low attenuation areas were defined as lung tissue with attenuation values ≤ −950 Hounsfield Units (HU) on inspiratory CT (LAAI-950). Expiratory low attenuation areas were defined as lung tissue ≤ −856 HU on expiratory CT (LAAE-856). We used simple linear regression to determine the impact of age and gender on QCT parameters and multiple regression to assess the additional impact of total lung capacity and functional residual capacity measured by CT (TLCCT and FRCCT), scanner type, and mean tracheal air attenuation. Airways were evaluated using measures of airway wall thickness (AWT), inner luminal area (ILA), wall area percent (WA%) and standardized thickness of an airway with inner perimeter of 10mm (Pi10). Results Mean %LAAI-950 was 2.0 (SD 2.7), and mean %LAAE-856 was 9.2 (SD 6.8). Mean %LAAI-950 was 3.6 (SD 3.2) % in men, compared with 1.3 (SD 2.0) in women (P<0.001). The %LAAI-950 did not change significantly with age (P=0.08) or BMI (P=0.52). %LAAE-856 did not show any independent relationship with age (P=0.33), gender (P=0.70), or BMI (P=0.32). On multivariate analysis, %LAAI-950 showed a direct relationship to TLCCT (P=0.002) and an inverse relationship to mean tracheal air attenuation (P=0.003), and %LAAE-856 was related to age (P=0.001), FRCCT (P=0.007) and scanner type (P<0.001). Multivariate analysis of segmental airways showed that ILA and WA% were significantly related to TLCCT (P<0.001) and age (0.006). WA% was also associated with gender (P=0.05), axial pixel size (P=0.03) and slice interval (P=0.04). Lastly, AWT is strongly influenced by axial pixel size (P<0.001). Conclusions Although the attenuation characteristics of normal lung differ by age and gender, these differences do not persist on multivariate analysis. Potential sources of variation in measurement of attenuation-based quantitative CT parameters include depth of inspiration/expiration, and scanner type. Tracheal air attenuation may partially correct variation due to scanner type. Sources of variation in QCT airway measurements may include age, gender, BMI, depth of inspiration, and spatial resolution.

Pulmonary Abnormalities and Carotid Atherosclerosis in Ex-Smokers without Airflow Limitation

“…No signifi cant diff erences were observed for VWV (p = 1.0), IMT (p = 0.11), ADC (p = 0.20) or FEV 1 (p = 0.89). For ex-smokers, CT mean RA 950 (1.5 ± 1.4%), airway wall thickness at an internal perimeter of 10 mm (Pi10, 4.1 ± 0.17 mm), WA% (61 ± 2%), and LA (17 ± 11 mm 2 ) were within normal range as previously published (49,50).…”

“…It is also worth noting that for the healthy ex-smokers evaluated here, there was no CT www.copdjournal.com evidence of emphysema or airways disease and such values were in agreement with those reported in healthy non-smokers in the COPDGene study (49,50). Th is fi nding underscores the sensitivity of 3 He MRI ventilation measurements for detecting functional abnormalities that may not be apparent using CT or spirometry.…”

Progress in Imaging COPD, 2004 - 2014

“…A study of 92 healthy individuals aged between 45 and 80 showed that inspired lung volume was an important determinant of LAA -950 and of several airway parameters. 68 Although the parameters also differed by age and sex, these differences did not persist when adjusted for inspired lung volume. Larger studies of healthy individuals are being performed, with the goal of developing prediction equations.…”

Pulmonary MRI morphometry modeling of airspace enlargement in chronic obstructive pulmonary disease and alpha‐1 antitrypsin deficiency

“…Importantly, estimates may also be generated for the mean linear intercept (L m ) and surface-to-volume ratio (S/V) (16), both welldescribed in the lung stereology literature (17). Previous work also established the relationship of lung MRI and histological measurements (16,(18)(19)(20), and evaluated emphysema patients (16,21), rodent models (18,20), lung cancer (22), and lung aging (23,24). In contrast, a singlecompartment model, as shown in Figure 1b, was also previously used to explain fluid diffusion in porous media (25) and to model 129 Xe (26), 19 F (27), and simulated inhaled-gas diffusion (28) in emphysematous tissue (29).…”