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

ObjectivesThe purposes of this study were to evaluate the reference range of quantitative computed tomography (QCT) measures of lung attenuation and airway parameter measurements in healthy nonsmoking adults and to identify sources of variation in those measures and possible means to adjust for them. Materials and MethodsWithin the COPDGene study, 92 healthy non-Hispanic white nonsmokers (29 men, 63 women; mean [SD] age, 62.7 [9.0] years; mean [SD] body mass index [BMI], 28.1 [5.1] kg/m2) underwent volumetric computed tomography (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 or less on inspiratory CT (LAAI-950). Expiratory low-attenuation areas were defined as lung tissue −856 Hounsfield units or less on expiratory CT (LAAE-856). We used simple linear regression to determine the impact of age and sex 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, inner luminal area, wall area percentage (WA%), and standardized thickness of an airway with inner perimeter of 10 mm (Pi10). ResultsMean (SD) %LAAI-950 was 2.0% (2.7%), and mean (SD) %LAAE-856 was 9.2% (6.8%). Mean (SD) %LAAI-950 was 3.6% (3.2%) in men, compared with 1.3% (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), sex (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 inner luminal area and WA% were significantly related to TLCCT (P < 0.001) and age (0.006). Moreover, WA% was associated with sex (P = 0.05), axial pixel size (P = 0.03), and slice interval (P = 0.04). Lastly, airway wall thickness was strongly influenced by axial pixel size (P < 0.001). ConclusionsAlthough the attenuation characteristics of normal lung differ by age and sex, these differences do not persist on multivariate analysis. Potential sources of variation in measurement of attenuation-based QCT parameters include depth of inspiration/expiration and scanner type. Tracheal air attenuation may partially correct variation because of scanner type. Sources of variation in QCT airway measurements may include age, sex, BMI, depth of inspiration, and spatial resolution.

[1]  L. Fasano,et al.  Spiral CT evaluation of pulmonary emphysema using a low-dose technique. , 2002, La Radiologia medica.

[2]  Ye Xu,et al.  MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies , 2006, IEEE Transactions on Medical Imaging.

[3]  Richard Beasley,et al.  Utility of lung density measurements in the diagnosis of emphysema. , 2007, Respiratory medicine.

[4]  H. Sostman,et al.  Radiologic evaluation of emphysema in patients with chronic obstructive pulmonary disease. Chest radiography versus high resolution computed tomography. , 1995, American journal of respiratory and critical care medicine.

[5]  J. Stocks,et al.  Reference values for residual volume, functional residual capacity and total lung capacity. ATS Workshop on Lung Volume Measurements. Official Statement of The European Respiratory Society. , 1995, The European respiratory journal.

[6]  David A Lynch,et al.  Quantitative Computed Tomography in Chronic Obstructive Pulmonary Disease , 2013, Journal of thoracic imaging.

[7]  P. Gevenois,et al.  The effects of age, sex, lung size, and hyperinflation on CT lung densitometry. , 1996, AJR. American journal of roentgenology.

[8]  James S Babb,et al.  Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom. , 2007, Radiology.

[9]  E. Milne.  Quantitative chest computed tomography as a means of predicting exercise performance in severe emphysema. , 1996, Academic radiology.

[10]  Raúl San José Estépar,et al.  Gender Differences of Airway Dimensions in Anatomically Matched Sites on CT in Smokers , 2011, COPD.

[11]  J L Hankinson,et al.  Spirometric reference values from a sample of the general U.S. population. , 1999, American journal of respiratory and critical care medicine.

[12]  E Kohda,et al.  Longitudinal follow-up study of smoking-induced lung density changes by high-resolution computed tomography. , 2000, American journal of respiratory and critical care medicine.

[13]  E. Marchiori,et al.  CT quantification of emphysema in young subjects with no recognizable chest disease. , 2009, AJR. American journal of roentgenology.

[14]  J. Clausen,et al.  Quantitation of emphysema with three-dimensional CT densitometry: comparison with two-dimensional analysis, visual emphysema scores, and pulmonary function test results. , 1999, Radiology.

[15]  E. Regan,et al.  Genetic Epidemiology of COPD (COPDGene) Study Design , 2011, COPD.

[16]  J. Seo,et al.  Quantitative Assessment of Emphysema, Air Trapping, and Airway Thickening on Computed Tomography , 2008, Lung.

[17]  Asger Dirksen,et al.  Quantitative CT: Associations between Emphysema, Airway Wall Thickness and Body Composition in COPD , 2011, Pulmonary medicine.

[18]  Johan H C Reiber,et al.  Variability in Densitometric Assessment of Pulmonary Emphysema With Computed Tomography , 2005, Investigative radiology.

[19]  R. Gillum.  Chronic obstructive pulmonary disease in blacks and whites: pulmonary function norms and risk factors. , 1991, Journal of the National Medical Association.

[20]  Y. Yamashita,et al.  Assessment of lung volumes in pulmonary emphysema using multidetector helical CT: comparison with pulmonary function tests. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[21]  Kunihiro Yagihashi,et al.  Quantitative Assessment of Peripheral Airway Obstruction on Paired Expiratory/Inspiratory Thin-section Computed Tomography in Chronic Obstructive Pulmonary Disease With Emphysema , 2007, Journal of computer assisted tomography.

[22]  Milan Sonka,et al.  Quantitative analysis of pulmonary airway tree structures , 2006, Comput. Biol. Medicine.

[23]  A T Redpath,et al.  Parenchymal emphysema measured by CT lung density correlates with lung function in patients with bullous disease. , 1993, The European respiratory journal.

[24]  Asger Dirksen,et al.  Monitoring the progress of emphysema by repeat computed tomography scans with focus on noise reduction. , 2008, Proceedings of the American Thoracic Society.

[25]  Jan Stolk,et al.  Influence of calibration on densitometric studies of emphysema progression using computed tomography. , 2004, American journal of respiratory and critical care medicine.

[26]  Yasutaka Nakano,et al.  The prediction of small airway dimensions using computed tomography. , 2005, American journal of respiratory and critical care medicine.

[27]  P. Paré,et al.  A quantification of the lung surface area in emphysema using computed tomography. , 1999, American journal of respiratory and critical care medicine.

[28]  E. Gelfand,et al.  Quantitative computed tomography detects peripheral airway disease in asthmatic children , 2005, Pediatric pulmonology.

[29]  Nicola A Hanania,et al.  Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes. , 2011, Radiology.

[30]  P. Paré,et al.  Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. , 2000, American journal of respiratory and critical care medicine.

[31]  Geoffrey McLennan,et al.  State of the Art. A structural and functional assessment of the lung via multidetector-row computed tomography: phenotyping chronic obstructive pulmonary disease. , 2006, Proceedings of the American Thoracic Society.

[32]  H. Coxson,et al.  Quantitative Computed Tomography of Chronic Obstructive Pulmonary Disease1 , 2005 .