Rationale and Objectives
The airway tree is a primary conductive structure, and airways’ morphologic characteristics, or variations thereof, may have an impact on airflow, thereby affecting pulmonary function. The objective of this study was to investigate the correlation between airway tree architecture, as depicted on computed tomography, and pulmonary function.
Materials and Methods
A total of 548 chest computed tomographic examinations acquired on different patients at full inspiration were included in this study. The patients were enrolled in a study of chronic obstructive pulmonary disease (Specialized Center for Clinically Oriented Research) and underwent pulmonary function testing in addition to computed tomographic examinations. A fully automated airway tree segmentation algorithm was used to extract the three-dimensional airway tree from each examination. Using a skeletonization algorithm, airway tree volume–normalized architectural measures, including total airway length, branch count, and trachea length, were computed. Correlations between airway tree measurements with pulmonary function testing parameters and chronic obstructive pulmonary disease severity in terms of the Global Initiative for Obstructive Lung Disease classification were computed using Spearman’s rank correlations.
Results
Non-normalized total airway volume and trachea length were associated ( P < .01) with lung capacity measures (ie, functional residual capacity, total lung capacity, inspiratory capacity, vital capacity, residual volume, and forced expiratory vital capacity). Spearman’s correlation coefficients ranged from 0.27 to 0.55 ( P < .01). With the exception of trachea length, all normalized architecture-based measures (ie, total airway volume, total airway length, and total branch count) had statistically significant associations with the lung function measures (forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced expiratory vital capacity), and adjusted volume was associated with all three respiratory impedance measures (lung reactance at 5 Hz, lung resistance at 5 Hz, and lung resistance at 20 Hz), and adjusted branch count was associated with all respiratory impedance measures but lung resistance at 20 Hz. When normalized for lung volume, all airway architectural measures were statistically significantly associated with chronic obstructive pulmonary disease severity, with Spearman’s correlation coefficients ranging from −0.338 to −0.546 ( P < .01).
Conclusions
Despite the large variability in anatomic characteristics of the airway tree across subjects, architecture-based measures demonstrated statistically significant associations ( P < .01) with nearly all pulmonary function testing measures, as well as with disease severity.
Morphologic variations in lung anatomy are assumed to be frequently associated with changes in pulmonary function . However, the manner and extent to which changes in the lung structure affect lung function and/or are associated with early lung disease remain largely unknown. Conventional pulmonary function testing (PFT) provides a global assessment of lung function, but these measurements have limited inferential knowledge of lung structural changes and the manner in which structural changes may relate to lung disease. The airways are of particular interest in this regard because of their important role in ventilation and the potential effect airway remodeling may have on airflow obstruction. Anatomically, the airways appear as a treelike branching network of tubes that are directly exposed to airborne viruses, particulate matter, smoke, and other pollutants in the external environment. Any morphologic variations of the airway tree may affect airflow and ultimately the ability of the lungs to exchange gas. With advances in modern imaging technology, there have been a large number of noninvasive investigations of the possible relationships between in vivo airway morphologic characteristics and pulmonary function . Computed tomographic (CT) imaging is widely used in these investigations because of its noninvasive ability to visualize lung anatomy in significant detail. These studies have attempted to develop new methods for noninvasive, quantitative, and accurate assessment of pathologic conditions that affect pulmonary function and/or specific lung diseases, thus improving clinicians’ diagnostic performance.
Among the various imaging-based measured features, airway wall thickness has been reported repeatedly as a predictor of lung function measures , in particular in patients with chronic obstructive pulmonary disease (COPD). Because of challenges in accurately identifying the airway wall, airway wall measurements in early studies were largely restricted to airways perpendicular to the scanning plane. It is generally difficult to accurately measure airway wall thickness given the partial volume effects combined with the inherent CT image noise. The accurate assessment of wall thickness is also complicated by nonhomogeneous airway surroundings (background tissue) that may include adjacent airways, lung parenchyma, and blood vessels. Achenbach et al reported that airway wall morphometry could be grossly in error, in particular as related to measurements of small airways. Coxson also stated that “none of the studies published to date show ‘excellent’ correlations between lung function and CT measures of emphysema and airway wall remodeling.”
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Materials and methods
Study Population and PFT
Get Radiology Tree app to read full this article<
Table 1
Subject Demographics ( n = 548)
Parameter Value Age (y) 65.2 ± 7.0 Women 247 (45.1%) Race White 519 (94.7%) African American 25 (4.6%) Other minorities 4 (0.7%) Smoking status Current 215 (39.2%) Former 333 (60.8%) Pack-years 56.7 ± 32.5% Inhaled corticosteroid use ∗ 124 (22.6%) Oral steroid use ∗ 46 (8.4%) Statin use ∗ 219 (40.0%) FVC percentage predicted 88.9 ± 18.5% FEV 1 percentage predicted 74.2 ± 27.4 FEV 1 /FVC 61.4 ± 16.8% Diffusing capacity of the lung for carbon monoxide percentage predicted 61.4 ± 16.8% GOLD category 1 86 (15.7%) 2 143 (26.1%) 3 59 (10.8%) 4 51 (9.3%) Visual emphysema rating 0 182 (33.2%) 1 145 (26.5%) 2 74 (13.5%) 3 65 (11.9%) 4 44 (8.0%) 5 38 (6.9%)
FEV 1 , forced expiratory volume in 1 second; FVC, functional vital capacity; GOLD, Global Initiative on Obstructive Lung Disease.
Data are expressed as mean ± standard deviation or as number (percentage).
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Acquisition of Thin-section CT Examinations
Get Radiology Tree app to read full this article<
Measurements of Lung Airways
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Statistical Analyses
Get Radiology Tree app to read full this article<
Results
Get Radiology Tree app to read full this article<
Table 2
Airway Tree and Lung Volume Averaged Measurements
Airway Tree Measures Volume (cm 3 ) Length (cm) Branch Count Trachea Length (cm) Lung Volume (L) 57.6 ± 18.9 230.5 ± 97.7 151 ± 66 9.2 ± 1.5 5.26 ± 1.30
Data are expressed as mean ± standard deviation.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Table 3
Univariate (Spearman’s) Correlation Coefficients between Volume-normalized Airway Tree Measures and PFT Variables
PFT Variable Airway Tree Measures Volume Length Branch Count Trachea Length Lung capacity Functional residual capacity 0.453 ∗ (0.155) 0.109 (−0.323 ∗ ) 0.062 (−0.352 ∗ ) 0. 307 ∗ (−0.512 ∗ ) Total lung capacity 0.555 ∗ (−0.028) 0.191 ∗ (−0.27 ∗ ) 0.128 (−0.326 ∗ ) 0.473 ∗ (−0.555 ∗ ) Inspiratory capacity 0.453 ∗ (0.157) 0.228 ∗ (−0.029) 0.175 ∗ (−0.078) 0.302 ∗ (−0.264 ∗ ) Vital capacity 0.555 ∗ (0.223 ∗ ) 0.325 ∗ (0.009) 0.278 ∗ (−0.041) 0.394 ∗ (−0.346 ∗ ) Residual volume 0.268 ∗ (−0.306 ∗ ) −0.046 (−0.437) −0.093 (−0.461) 0.385 ∗ (−0.460 ∗ ) FVC 0.538 ∗ (0.256 ∗ ) 0.343 ∗ (0.102) 0.302 ∗ (0.062) 0.377 ∗ (−0.225 ∗ ) Lung function FEV 1 0.355 ∗ (0.412 ∗ ) 0.410 ∗ (0.386 ∗ ) 0.412 ∗ (0.372 ∗ ) 0.158 (0.066) FEV 1 /FVC −0.027 (0.412 ∗ ) 0.272 (0.532 ∗ ) 0.323 ∗ (0.553 ∗ ) −0.176 ∗ (0.380 ∗ ) Forced expiratory time from 25% to 75% −0.011 (−0.324 ∗ ) −0.261 ∗ (−0.416 ∗ ) −0.303 ∗ (−0.434 ∗ ) 0.153 (−0.352 ∗ ) Peak inspiratory flow 0.345 ∗ (0.218 ∗ ) 0.257 ∗ (0.130) 0.232 ∗ (0.103) 0.185 ∗ (−0.119) Diffusing capacity of the lung for carbon monoxide 0.188 ∗ (0.185 ∗ ) 0.155 (0.139) 0.156 (0.131) 0.113 (0.026) Respiratory impedance Lung reactance at 5 Hz 0.262 ∗ (0.379 ∗ ) 0.282 ∗ (0.302) 0.287 ∗ (0.278 ∗ ) 0.093 (−0.119) Lung resistance at 5 Hz −0.383 ∗ (−0.417 ∗ ) −0.312 ∗ (−0.291 ∗ ) −0.309 ∗ (−0.267 ∗ ) −0.165 ∗ (0.116) Lung resistance at 20 Hz −0.433 ∗ (−0.356 ∗ ) −0.254 ∗ (−0.143) −0.245 ∗ (−0.120) −0.218 ∗ (0.156)
FEV 1 , forced expiratory volume in 1 second; FVC, forced expiratory vital capacity; PFT, pulmonary function testing.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Table 4
Univariate Correlation Coefficients between Volume-normalized Airway Tree Measures and GOLD Classification
PFT Variable Airway Tree Measures Volume Length Branch Count Trachea Length GOLD classification −0.058 (−0.421 ∗ ) −0.327 ∗ (−0.533 ∗ ) −0.366 ∗ (−0.546 ∗ ) 0.090 (−0.338 ∗ )
GOLD, Global Initiative on Obstructive Lung Disease; PFT, pulmonary function testing.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Discussion
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Conclusions
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
References
1. Hogg J.C.: Lung structure and function in COPD. Int J Tuberc Lung Dis 2008; 12: pp. 467-479.
2. Murray J.F.: The structure and function of the lung. Int J Tuberc Lung Dis 2010; 14: pp. 391-396.
3. de Jong P.A., Müller N.L., Paré P.D., et. al.: Computed tomographic imaging of the airways: relationship to structure and function. Eur Respir J 2005; 26: pp. 140-152.
4. Orlandi I., Moroni C., Camiciottoli G., et. al.: Chronic obstructive pulmonary disease: thin-section CT measurement of airway wall thickness and lung attenuation. Radiology 2005; 234: pp. 604-610.
5. Hasegawa M., Nasuhara Y., Onodera Y., et. al.: Airflow limitation and airway dimensions in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2006; 173: pp. 1309-1315.
6. Achenbach T., Weinheimer O., Biedermann A., et. al.: MDCT assessment of airway wall thickness in COPD patients using a new method: correlations with pulmonary function tests. Eur Radiol 2008; 18: pp. 2731-2738.
7. Arakawa H., Fujimoto K., Fukushima Y., et. al.: Thin-section CT imaging that correlates with pulmonary function tests in obstructive airway disease. Eur J Radiol 2011; 80: pp. e157-e163.
8. Nakano Y., Muro S., Sakai H., et. al.: Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. Am J Respir Crit Care Med 2000; 162: pp. 1102-1108.
9. Grydeland T.B., Dirksen A., Coxson H.O., et. al.: Quantitative computed tomography measures of emphysema and airway wall thickness are related to respiratory symptoms. Am J Respir Crit Care Med 2010; 181: pp. 353-359.
10. Kosciuch J., Krenke R., Gorska K., et. al.: Relationship between airway wall thickness assessed by high-resolution computed tomography and lung function in patients with asthma and chronic obstructive pulmonary disease. J Physiol Pharmacol 2009; 60: pp. 71-76.
11. Coxson H.O., Lam S.: Quantitative assessment of the airway wall using computed tomography and optical coherence tomography. Proc Am Thorac Soc 2009; 6: pp. 439-443.
12. Coxson H.O.: Quantitative computed tomography assessment of airway wall dimensions: current status and potential applications for phenotyping chronic obstructive pulmonary disease. Proc Am Thorac Soc 2008; 5: pp. 940-945.
13. McDonough J.E., Yuan R., Suzuki M., et. al.: Small-airway obstruction and emphysema in chronic obstructive pulmonary disease. N Engl J Med 2011; 365: pp. 1567-1575.
14. Diaz A.A., Valim C., Yamashiro T., et. al.: Airway count and emphysema assessed by chest CT imaging predicts clinical outcome in smokers. Chest 2010; 138: pp. 880-887.
15. Leader J.K., Rogers R.M., Fuhrman C.R., et. al.: Size and morphology of the trachea before and after lung volume reduction surgery. AJR Am J Roentgenol 2004; 183: pp. 315-321.
16. Handa T., Nagai S., Hirai T., et. al.: Computed tomography analysis of airway dimensions and lung density in patients with sarcoidosis. Respiration 2009; 77: pp. 273-281.
17. Pu J., Fuhrman C., Good W.F., et. al.: A differential geometric approach to automated segmentation of human airway tree. IEEE Trans Med Imaging 2011; 30: pp. 266-278.
18. Graham M.W., Gibbs J.D., Cornish D.C., et. al.: Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy. IEEE Trans Med Imaging 2010; 29: pp. 982-997.
19. Fabijańska A.: Two-pass region growing algorithm for segmenting airway tree from MDCT chest scans. Comput Med Imaging Graph 2009; 33: pp. 537-546.
20. Tschirren J., Hoffman E.A., McLennan G., et. al.: Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans. IEEE Trans Med Imaging 2005; 24: pp. 1529-1539.
21. Pu J., Gu S., Liu S., et. al.: CT based computerized identification and analysis of human airways: a review. Med Phys 2012; 39: pp. 2603-2616.
22. Cornea N.D., Silver D., Yuan X., et. al.: Computing hierarchical curve-skeletons of 3D objects. Vis Comput 2005; 21: pp. 945-955.
23. Mannino D.M., Doherty D.E., Sonia Buist A.: Global Initiative on Obstructive Lung Disease (GOLD) classification of lung disease and mortality: findings from the Atherosclerosis Risk in Communities (ARIC) study. Respir Med 2006; 100: pp. 115-122.
24. Montaudon M., Lederlin M., Reich S., et. al.: Bronchial measurements in patients with asthma: comparison of quantitative thin-section CT findings with those in healthy subjects and correlation with pathologic findings. Radiology 2009; 253: pp. 844-853.
25. Leader J.K., Coxson H.O., Zheng B., et. al.: The relation between lung function decline and airway count. Am J Respir Crit Care Med 2009; 179: pp. A5576.
26. Weibel E.R.: Morphometry of the Human Lung.1963.Academic PressNew York