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(Chest. 2002;122:271S-275S.)
© 2002 American College of Chest Physicians

Quantitative Assessment of Airway Remodeling Using High-Resolution CT*

Yasutaka Nakano, MD, PhD; Nestor L. Müller, MD PhD; Gregory G. King, MD, PhD; Akio Niimi, MD, PhD; Steven E. Kalloger, BSc; Michiaki Mishima, MD, PhD and Peter D. Paré, MD

* From the University of British Columbia, McDonald Research Laboratories/iCAPTURE Center (Drs. Nakano, Mr. Kalloger, and Paré), St. Paul’s Hospital, Vancouver, BC, Canada; Department of Radiology, Vancouver Hospital and Health Sciences Center (Dr. Müller), University of British Columbia, Vancouver, BC, Canada; Institute of Respiratory Medicine (Dr. King), University of Sydney, Sydney, NSW, Australia; Department of Respiratory Medicine, Graduate School of Medicine (Drs. Mishima and Niimi), Kyoto University, Kyoto, Japan.

Correspondence to: Peter D. Paré, MD, The University of British Columbia, McDonald Research Laboratories/iCAPTURE Center, St. Paul’s Hospital, Room 292, 1081 Burrard Street, Vancouver, BC, V6Z 1Y6 Canada; e-mail: ppare{at}mrl ubc.ca


   Abstract
TOP
 Abstract
Introduction
 References
 
Asthma and COPD are the most prevalent of lung diseases and contribute an enormous burden of morbidity in North America and globally. In both conditions, inflammation leads to airway remodeling, which contributes to airway narrowing. To date, airway remodeling has only been assessed using histological examination of airways. However, it may now be possible to assess and quantify the extent of airway remodeling in vivo using high-resolution CT (HRCT). The aim of this article is to review the use of HRCT in the investigation of airway remodeling. A number of investigators have reported techniques to make measurements of airway dimensions using CT and an increasing number of quantitative methods are being developed. Using these techniques, airway dimensions have been examined in patients with asthma and COPD. In patients with asthma, the airway wall area was increased without a decrease in luminal area, whereas in patients with COPD, the airway luminal area was decreased and airway wall area was increased. The different pattern of remodeling may reflect fundamental differences in the inflammatory processes in asthma and COPD and could influence the reversibility of the narrowing. It has also been shown that, by quantifying both the extent of emphysema and of airway remodeling, CT is useful in differentiating COPD patients who have primarily parenchymal disease from those who have primarily airway pathology. With additional advances in technology, it is likely that quantitative assessment of airway wall dimensions will ultimately provide a valuable tool for the study of airway disease.

Key Words: airway remodeling • asthma • COPD • computed tomography

Asthma and COPD are the most prevalent of lung diseases, and they contribute an enormous burden of morbidity in North America and globally.1 2 Schema outlining the pathophysiologic mechanisms underlying asthma and COPD are shown in Figures 1 and 2 . In both disorders, environmental factors such as allergens, viruses, and bacteria as well as personal, occupational, and atmospheric pollution cause an exaggerated immune/inflammatory response in genetically susceptible individuals. The inflammatory response leads to airway remodeling, which contributes to airway narrowing. There is also accumulating evidence that airway remodeling plays a role in the pathogenesis of other airway diseases such as bronchiectasis, cystic fibrosis, and bronchiolitis.3 Although airway remodeling has not been precisely defined, we propose the following description: (a) Airway remodeling is characterized by changes in the composition, quantity, and organization of the cellular and molecular constituents of the airway wall. (b) Remodeling is a consequence of chronic injury and repair. (c) Remodeling may be reversible or irreversible. (d) Remodeling leads to functional changes.



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  		Figure 1.. Pathophysiologic schema for the development of asthma.

 


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  		Figure 2.. Pathophysiologic schema for the development of COPD.

 
To date, airway remodeling has been assessed using only histologic examination of airways. However, with the refinement of the precision and resolution of high-resolution CT (HRCT), it may now be possible to assess and quantify the extent of airway remodeling in vivo. In this article, we provide a brief review of recent advances in the assessment of airway disease using HRCT and some examples of the application of these techniques to the study of COPD and asthma. Diseased airways have been assessed in a qualitative or semiquantitative fashion using chest radiographic films and conventional CT. HRCT has allowed visualization of airways and parenchyma in much greater detail than conventional CT and plain radiography. HRCT has also made possible the investigation of the site and the magnitude and distribution of airway narrowing in vivo.4 Technical improvements have increased the spatial resolution of HRCT, making it theoretically possible to examine small airways. However, qualitative and semiquantitative measures of airway wall remodeling are open to subjective bias and have been shown to be less accurate.5 An increasing number of quantitative methods are being developed, and it is these studies we will review herein because of the enormous potential provided by the digital data on which this imaging modality is based.

A number of investigators have reported techniques to make measurements of airway dimensions using CT. In most of these studies a visual assessment was used.6 7 8 9 10 11 12 Visual assessment is time-consuming and, as mentioned above, results in the potential for observer bias and considerable intra- or interobserver error. Because CT data are based on the variable absorption of radiographs by tissue, which is measured by Hounsfield units (HU), a more direct and objective method to measure airway dimensions is preferable. There are several quantitative methods reported that used CT data directly. McNitt-Gray and coworkers13 tested an analysis method in which a threshold number was used to detect the airway luminal area (Ai); all pixels with values below this threshold were designated as lumen. They found that a threshold value of -500 HU yielded the most accurate measurements of the lumen of a bronchial phantom. This threshold is consistent with the findings of other studies.12 14 Wood and coworkers15 made quantitative measurements of airway wall and luminal areas from spiral CT data. The first step in their analysis was to convert the asymmetric CT voxels into cubic dimensions (isotropic voxels). The voxels were converted to approximately 0.4 x 0.4 x 0.4-mm voxels by interpolation in the longitudinal axis. This manipulation allowed the images to be reconstructed in any orientation. They then defined the central axis of the airway and reconstructed the airway lumen in a plane perpendicular to this axis. This analysis technique overcomes the major limitation to the use of HRCT in quantitative analysis, which is that accurate or true Ai and airway wall area (Aaw) can be measured only from airways that are oriented approximately perpendicular to the plane of scanning.

Amirav and colleagues16 developed an operator-independent algorithm to measure the airway lumen. Their algorithm is an edge detection method based on the "full width at half maximum" principle. They first estimated the luminal perimeter by a hand-drawn line that was then smoothed repeatedly. Multiple lines were then generated perpendicular to the smoothed perimeter, radiating outward away from the lumen into the airway wall and parenchyma. The profile of HU along this line had a minimum in the lumen and a maximum in the soft tissue. The middle value (half maximum) was calculated, and the point on the hand-drawn line was then moved to the half maximum point. This was repeated for all points radiating from the hand-drawn line that now defined the airway luminal perimeter. The advantages of this method are that it is relatively operator-independent and very fast. Nakano et al17 improved this method and developed a computer-assisted automated method to measure Ai, wall thickness, and Aaw. Reinhardt and colleagues18 developed a maximum-likelihood method to estimate the airway inner and outer radius. King and colleagues5 reported an automated CT image analysis algorithm (CT airway morphometry) to measure Ai, Aaw, and airway angle of orientation. In this method, luminal area was defined according to an airway-size–dependent threshold value, and total Aaw was determined using an entirely novel algorithm based on the principle of score-guided erosion. However, almost all reports to date have been based on identifying cross-sections of airways that appear to be round. Only Wood et al15 and King et al5 considered, and corrected for, the orientation of angled airways.

Using these HRCT techniques, investigators have assessed airway dimensions in humans. Boulet et al8 used an electronic caliper on a CT image, which was displayed at a fixed window and a fixed level to measure airway dimensions manually. They measured airway dimensions in asthmatic subjects and nonasthmatic subjects and expressed the results as the ratio of wall thickness to outer diameter. They found a negative correlation between the thickening of the intermediate stem bronchus and the provocative dose of methacholine causing a 20% fall in FEV1 (PD20). Although there was no correlation with the FEV1, these results suggest either that structural changes in the large airways are an important determinant of airway hyperresponsiveness or that the changes measured in the airway wall of the central airways reflect similar changes throughout the whole bronchial tree. Okazawa and colleagues6 measured the change in airway wall and luminal area in six asthmatic subjects and six normal subjects after methacholine challenge using HRCT. They used a validated quantitative image analysis method12 and calculated WA% (WA% = Aaw/[Aaw + Ai] x 100). There were similar decreases in FEV1 after methacholine challenges in asthmatic subjects (31%) and in normal subjects (37%), and the distribution of airway narrowing was similar in the two groups. The greatest decrease in luminal area was ~ 40% and occurred in 2- to 4-mm airways in both asthmatic and normal subjects. They also found that Aaw decreased in the normal subjects after methacholine challenge but did not change in asthmatic subjects. Awadh and colleagues7 studied three groups of asthmatic subjects and a control group: asthmatic subjects who had a history of a previous life-threatening attack, asthmatics who were taking 1,000 µg/d or more of inhaled corticosteroid, and asthmatics who were taking < 1,000 µg/d. HRCT was performed using 1-mm slices, and airway dimensions were measured using both the ratio of wall thickness to outer diameter and WA%. In asthmatic subjects who had histories of life-threatening attacks and asthmatic subjects who were taking > 1,000 µg/d of inhaled corticosteroid, the mean airway wall thickness was similar, but it was greater than the mean airway wall thickness of both the control subjects and the asthmatic subjects requiring < 1,000 µg/d of inhaled corticosteroid.

More recently, two papers have been published in which the authors used HRCT and analyzed the airway wall and luminal dimensions of the right upper lobe apical segmental bronchus to examine their relationship with clinical indexes in asthma and COPD.10 17 Although the upper lobe apical segmental bronchus is a large airway, it has been shown that the airway dimension of this bronchus correlates well with that of the smaller airways found in CT scans.17 Niimi and coworkers10 analyzed 81 asthmatic patients (13 mild persistent, 39 moderate persistent, 22 severe persistent, and 7 intermittent) and compared them with 28 healthy volunteers. They found that the Aaw was increased in patients with asthma without a decrease in Ai (Table 1 ). Aaw correlated positively with the duration and clinical severity of asthma, while WA% was negatively related to FEV1 (% predicted), FEV1/FVC (%), and forced expiratory flow over the middle hall of the vital capacity (% predicted).


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  		Table 1.. Large Airway Dimensions in Asthma Measured Using HRCT*

 
Nakano and colleagues17 developed customized software to measure the dimensions of the apical segmental bronchus in 114 smokers (94 COPD patients and 20 asymptomatic control subjects).They found that the Ai was smaller and WA% was bigger in COPD compared with asymptomatic control subjects (Table 2 ). They tested whether the WA% added value to the prediction of pulmonary function tests beyond a HRCT estimate of the severity of emphysema (percentage of low attenuation area/total lung area x 100; [LAA%]). Although both WA% and LAA% correlated with measurements of lung function, the combination of WA% and LAA% improved the estimate of pulmonary function abnormalities. In a multivariate model, they found that they could more accurately predict FEV1, FVC, FEV1/FVC, and peak expiratory flow, but not diffusing capacity of the lung for carbon monoxide, when both the estimate of airway wall thickening and the extent of low attenuation areas were included in a statistical model (Table 3 ). They also found that they could divide COPD patients into groups who had predominant loss of lung attenuation or thickening and narrowing of the apical segmental bronchus using LAA% and WA% (Fig 3 ). Although many subjects had both decreased lung attenuation consistent with emphysema and airway wall thickening, there were individuals with similar degrees of obstruction whose abnormalities appeared to be predominantly the results of airway remodeling and others in whom abnormalities appeared to be predominantly related to the loss of lung parenchyma. Interestingly, Nakano et al17 found that the luminal area was related to FEV1, while Niimi et al10 failed to find any relationship of the luminal area with the severity of asthma. The different pattern of remodeling shown by these two studies may reflect fundamental differences in the inflammatory processes in asthma and COPD and could influence the reversibility of the narrowing.


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  		Table 2.. Large Airway Dimensions in COPD Measured Using HRCT*

 

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  		Table 3.. Correlation Coefficients (r values) of Univariate and Stepwise Multiple Regression Analyses for Pulmonary Function Tests*

 


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  		Figure 3.. Relationship between WA% and extent of emphysema (LAA%) in 94 COPD patients and 20 asymptomatic smokers. Horizontal line shows the mean + 2SD of LAA% of the asymptomatic smokers. Vertical line shows the mean + 2SD of WA% of the asymptomatic smokers. Using these cutoff values, COPD patients can be divided into groups; airway remodeling-dominant group (high WA% and low LAA%), emphysema-dominant group (low WA% and high LAA%), and a mixed group (high WA% and high LAA%).

 
Despite the effort of many investigators, there are still many questions to be answered before the HRCT assessment of airway dimensions can be used as a research or clinical tool to study airway disease. The issues that require further study include the influence of reconstruction algorithm, field of view, and imaging parameters such as scanning amperage, scanning voltage, and helical scanning. The new generation of multi-slice CT scanners have just started to be used, but they promise to make the measurements faster and more accurate.19 With additional advances in technology, it is likely that quantitative assessment of airway wall dimensions will ultimately provide a valuable tool for the study of airway disease.


   Footnotes
 
Supported by a grant from the National Heart, Lung, and Blood Institute/National Institutes of Health (HL-64068) (to Dr. Paré).

Abbreviations: Aaw = airway wall area; Ai = airway luminal area; HRCT = high-resolution CT; HU = Hounsfield units; LAA% = low attenuation area/total lung area x 100; WA% = Aaw/(Aaw + Ai) x 100


   References
TOP
 Abstract
 Introduction
 References
 

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  5. King, GG, Müller, NL, Whittall, KP, et al An analysis algorithm for measuring airway lumen and wall areas from high-resolution computed tomographic data. Am J Respir Crit Care Med 2000;161,574-580[Abstract/Free Full Text]
  6. Okazawa, M, Müller, N, McNamara, AE, et al Human airway narrowing measured using high resolution computed tomography. Am J Respir Crit Care Med 1996;154,1557-1562[Abstract]
  7. Awadh, N, Müller, NL, Park, CS, et al Airway wall thickness in patients with near fatal asthma and control groups: assessment with high resolution computed tomographic scanning. Thorax 1998;53,248-253[Abstract/Free Full Text]
  8. Boulet, L, Belanger, M, Carrier, G Airway responsiveness and bronchial-wall thickness in asthma with or without fixed airflow obstruction. Am J Respir Crit Care Med 1995;152,865-871[Abstract]
  9. Boulet, LP, Turcotte, H, Carrier, G, et al Increased maximal airway response to methacholine during seasonal allergic rhinitis in nonasthmatic subjects: relationships with airway wall thickness and inflammation. Eur Respir J 1995;8,913-921[Abstract]
  10. Niimi, A, Matsumoto, H, Amitani, R, et al Airway wall thickness in asthma assessed by computed tomography: relation to clinical indices. Am J Respir Crit Care Med 2000;162,1518-1523[Abstract/Free Full Text]
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