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(Chest. 2006;129:558-564.)
© 2006 American College of Chest Physicians

Spirometrically Gated High-Resolution CT Findings in COPD*

Lung Attenuation vs Lung Function and Dyspnea Severity

Gianna Camiciottoli, MD; Maurizio Bartolucci, MD; Nazzarena M. Maluccio, MD; Chiara Moroni, MD; Mario Mascalchi, MD, PhD; Carlo Giuntini, MD and Massimo Pistolesi, MD

* From the Department of Critical Care, Section of Respiratory Medicine (Drs. Camiciottoli, Maluccio, and Pistolesi), Department of Clinical Pathophysiology, Section of Diagnostic Radiology (Drs. Bartolucci, Moroni, and Mascalchi), University of Florence, Florence; and the Cardio-Thoracic Department, Section of Respiratory Medicine (Dr. Giuntini), University of Pisa, Pisa, Italy.

Correspondence to: Massimo Pistolesi, MD, Department of Critical Care, Section of Respiratory Medicine, University of Florence, Florence, Italy; e-mail: massimo.pistolesi{at}unifi.it

Abstract

Study objectives: The aim of this study was to investigate the relationship between high-resolution CT (HRCT) lung attenuation measurements, acquired under spirometric control of inspiratory and expiratory lung volume, and pulmonary dysfunction as well as dyspnea severity in patients with COPD.

Patients and design: In 51 patients with COPD, we compared by linear regression, univariate and multivariate logistic regression airflow limitation (FEV1/vital capacity [VC]), hyperinflation (percentage of predicted residual volume [RV%]), parenchymal loss (percentage of predicted diffusing capacity of the lung for carbon monoxide [DLCO%]), and Medical Research Council (MRC) dyspnea scale with relative area with attenuation values < – 950 HU at 90% of VC [RAI950] and < – 910 HU at 10% of VC, respectively, and with mean lung attenuation measured at the same levels of VC (mean CT lung density at 10% of VC, and mean CT lung density at 90% of VC [MeanCTEXP]).

Results: All HRCT attenuation measurements were significantly related with functional abnormalities and dyspnea severity. In multivariate logistic models, with 1 indicating worse changes in dichotomous outcome variables, MeanCTEXP independently predicted FEV1/VC (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.11 to 0.56), RV% (OR, 0.57; 95% CI, 0.42 to 0.77), and MRC dyspnea scale (OR, 0.63; 95% CI, 0.48 to 0.82), while RAI950 independently predicted DLCO% (OR, 1.90; 95% CI, 1.37 to 2.65).

Conclusions: Spirometrically gated measurements of HRCT lung attenuation reflect differently functional changes and dyspnea perception in COPD. Inspiratory measurements assess the extent of emphysematous tissue loss, and expiratory measurements may reflect airflow limitation and lung hyperinflation with attendant dyspnea perception. Pulmonary dysfunction in COPD cannot be assessed by a single modality of lung attenuation measurement.

Key Words: COPD • diagnostic imaging • high-resolution CT imaging techniques in COPD • lung attenuation • respiratory function tests

COPD is a heterogeneous disorder in which airflow limitation results from emphysematous destructive changes of the terminal airways and/or inflammatory changes and remodeling of the conductive airways.1 Detection by thin-section high-resolution CT (HRCT) of lung areas with abnormally low attenuation is considered the most accurate means to assess in vivo the extent and the severity of emphysema in patients with COPD.2 However, inflammatory narrowing of the conductive airways may determine severe airflow limitation also in patients with no or trivial emphysema.34 Accordingly, low attenuation areas on HRCT have been found to be more related with the loss of lung surface area, as reflected by the reduction in diffusing capacity, rather than with the degree of airways obstruction.356789101112

Lack of standardization of the procedures for HRCT acquisition and interpretation further complicate the process of unraveling the relationship between HRCT data and lung function derangement in patients with COPD. However, several studies have provided information on how to standardize the acquisition and the interpretation of HRCT in COPD. First, a spirometrically gated HRCT technique has been developed to scan the chest at predefined lung volumes in order to avoid the influence of the level of lung inflation during scanning on HRCT attenuation measurements.13 Second, the percentage of lung area with attenuation values < – 950 Hounsfield units (HU) during inspiration and < – 910 HU during expiration have been proposed as the HRCT attenuation thresholds that best quantify in vivo the morphometric extent of emphysema.51415 Third, quantitative HRCT data have been shown to provide a more consistent and less biased assessment of disease extent than subjective visual analysis and scoring.1617 The present study was aimed at establishing the relationship in COPD of four standardized HRCT lung attenuation measurements obtained at spirometrically controlled levels of inspiration and expiration with pulmonary dysfunction, as reflected by functional indexes of obstruction, hyperinflation, and parenchymal loss, as well as with the severity of dyspnea.

Methods and Materials

Patients
We studied 51 outpatients (5 women) with an average age of 64 years (range, 43 to 78 years) and an average smoking history of 43 pack-years (range, 5 to 80 pack-years) with COPD and mild-to-severe nonreversible airflow obstruction according to standard criteria.18 Study enrollment was based on stability of clinical conditions, the patient’s willingness to participate, and the ability to undergo spirometrically gated HRCT. The institutional review board approved the study protocol; individual informed written consent was obtained from all patients.

Clinical and Functional Evaluation
All patients underwent clinical examination and answered the Medical Research Council (MRC) questionnaire for the assessment of dyspnea severity.19 For the evaluation of lung function impairment, static and dynamic lung volumes and single-breath diffusing capacity of the lung for carbon monoxide (DLCO) were measured by a mass-flow sensor (V6200 Autobox Body Plethysmograph; SensorMedics; Yorba Linda, CA) according to standard methodology.1820 Arterial blood oxygen and carbon dioxide tensions were measured by a blood gas analyzer (ABL 730; Radiometer; Copenhagen, Denmark).

CT Examination
On the same day of the clinical and functional evaluation, patients underwent spirometrically gated HRCT scanning (Somatom Plus 4 Scanner; Siemens; Erlangen, Germany) according to the technique developed by Kalender and colleagues.13 Patients were asked to breathe through a spirometer (Micromedical Instruments; Rochester, UK) connected to the scanner and to perform reproducible vital capacity (VC) maneuvers. Subsequently, the airflow through the spirometer was interrupted at 90% and 10% of VC by a shutter that also triggered the scanner to acquire images at the level of the carina, 5 cm above the carina, and 5 cm below the carina. Scanning parameters were 1-mm collimation, 140 kilovolt peak, 146 mA, 1-s scanning time; a high-resolution algorithm (window width, 1,500 HU; window level, – 500 HU) was used for images reconstruction.21 The boundaries of the lungs in each section were determined semiautomatically by a density discriminating computer program (Pulmo CT Software; Siemens; Erlangen, Germany) as shown in Figure 1 . The HU frequency histograms of lung attenuation values computed for each section were averaged to derive mean attenuation values and percentages of lung area below predetermined thresholds.22 The following attenuation parameters were derived: mean lung attenuation at 90% of VC (MeanCTINSP); mean lung attenuation at 10% of VC (MeanCTEXP); relative area with attenuation values < – 950 HU at 90% of VC (RAI950); and relative area with attenuation values < – 910 HU at 90% of VC (RAE910).


Figure 1
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  		Figure 1. Spirometrically gated HRCT scans obtained at 90% of VC (left panel) and 10% of VC (right panel) at the carina level in a patient with mild COPD. Each image shows the lung contours and histogram curves for the right (curve B), left (curve C), and total (curve A) lung values. The Table Subrange superimposed in the upper portion of both panels refers to the attenuation values computed below a HU density threshold (– 1,000 to – 950 for the inspiratory scan and – 1,000 to – 910 for the expiratory scan); ME = mean value; AR = area; AR% = percentage area. The Table RANGE superimposed in the right portion of both panels refers to mean attenuation values in the whole range of lung density. ME = mean lung density; FW = full-width/half-maximum of the peak frequency histograms (percentage).

 
Statistical Analysis
The relationship of the four HRCT parameters (independent variables) with the lung function variables as well as with the MRC dyspnea scale (outcome variables) was tested by linear regression and univariate logistic regression analysis.23 Multiple logistic regression analysis was used to test whether the associations between HRCT attenuation and functional parameters were maintained after adjustment for covariance between variables. The values of the independent variables were grouped within decile intervals along their whole range of variation (from the minimum to the maximum value). The values of the outcome (dependent) variables were dichotomized as 0 or 1 according to their median value. The median value was selected in order to split the distribution of the variables without arbitrariness. Since 1 was chosen to indicate worse changes, values of FEV1/VC > 43, of percentage of predicted residual volume (RV%) < 159%, of percentage of predicted DLCO (DLCO%) > 66%, and of MRC dyspnea scale < 2 were categorized as 0. This means that for each chosen interval (eg, decile) of the independent variable, the frequency of occurrence of each outcome (ie, how many times the outcome variable is above or below the median value) as well as the mean (or proportion of the values of the outcome variable above the median) was computed for each group. The HRCT attenuation covariates were entered into the multiple logistic regression analysis with the outcome variables, categorized as above, by stepwise forward deletion of the least significant variable.23 Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs) for a 10% change of the HRCT attenuation parameters. An OR of 1 indicates that the density variable was of the same magnitude in both groups; if the 95% CI for the OR does not include unity, the variable is significantly different between the two categories of functional impairment and dyspnea severity. The calibration of the logistic models was tested by the Hosmer-Lemeshow goodness-of-fit-test.24 All statistical analyses were performed using a statistical package (SPSS/PC WIN 11.5.1; SPSS; Chicago, IL).

Results

Pulmonary function data, MRC dyspnea score, and spirometrically gated HRCT attenuation measurements obtained in the 51 patients are reported in Table 1 as mean, SD, median, and range. All data showed a great range of variation, likely reflecting the wide spectrum of clinical and functional expressions of COPD in this series of patients.


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  		Table 1. Pulmonary Function, MRC Dyspnea Scale, and Spirometrically Gated HRCT Lung Attenuation Data in 51 Patients With COPD*

 
The results of linear regression analysis of selected pulmonary function data and MRC dyspnea scale with the four modalities of HRCT attenuation measurement are displayed in Figures 2345 . Although with various degrees of variability, all the HRCT parameters showed significant relationships with functional indexes of airflow limitation (FEV1/VC), hyperinflation (RV%), and loss of lung parenchyma (DLCO%), as well as with the MRC dyspnea scale. Except for arterial blood gas data, the four HRCT parameters were significantly correlated also with all the other functional parameters reported in Table 1. In general, attenuation measurements obtained during expiration showed the highest values of the regression coefficients in the prediction of functional indexes of airway obstruction, hyperinflation, and dyspnea severity. In the prediction of DLCO, inspiratory and expiratory HRCT parameters had similar values of the regression coefficients.


Figure 2
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  		Figure 2. Relationship between HRCT attenuation measurements and Tiffenau index (FEV1/VC).

 

Figure 3
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  		Figure 3. Relationship between HRCT attenuation measurements and RV%.

 

Figure 4
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  		Figure 4. Relationship between HRCT attenuation measurements and DLCO%.

 

Figure 5
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  		Figure 5. Relationship between HRCT attenuation measurements and MRC dyspnea scale classified into five categories, from 0 to 4, V indicating the increasing degrees of dyspnea: 0 = none, not troubled by shortness of breath except with strenuous exercise; 1 = slight, troubled by shortness of breath when hurrying on the level or walking up a slight hill; 2 = moderate, walks slower than people of the same age on the level because of shortness of breath; 3 = severe, stops for breath after walking approximately 100 yards or after a few minutes on the level; 4 = very severe, too breathless to leave the house or breathless when dressing or undressing.

 
As shown in Table 2 , by univariate logistic regression analysis we found that the impairment of lung functional indexes and the degree of dyspnea worsened in association with an increased extent of lung area with HRCT attenuation values below predetermined thresholds (RAI950, RAE910) and with a reduction of mean lung attenuation (MeanCTINSP, MeanCTEXP).


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  		Table 2. Univariate Logistic Regression Models for the Prediction of Different Modalities of HRCT Lung Attenuation Measurement of the Severity of Lung Function Impairment and Dyspnea*

 
The multivariate logistic regression models (Table 3 ) showed that MeanCTEXP was the best independent predictor of FEV1/VC, RV%, and MRC dyspnea scale, while RAI950 was the best independent predictor of DLCO%. The other parameters were not entered by the forward stepwise process of deletion of the least significant variables. All the logistic models provided a good calibration of the experimental data as shown by p values always > 0.05 of the Hosmer-Lemeshow goodness-of-fit test.


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  		Table 3. Multivariate Logistic Regression Models for the Prediction of Different Modalities of HRCT Lung Attenuation Measurement of the Severity of Lung Function Impairment and Dyspnea*

 
Discussion

This study provides evidence that all the four HRCT modalities of lung attenuation measurement obtained under spirometric control of the level of lung inflation are significantly related to lung function parameters of airflow limitation, lung hyperinflation, and loss of lung parenchyma, as well as to the severity of dyspnea. The measurement of mean lung attenuation on expiratory sections (MeanCTEXP) is the HRCT parameter that, more than others, reflects the heterogeneity of lung dysfunction underlying the health status in this series of patients with COPD. However, the measurement of RAI950 is the HRCT parameter predicting more accurately the extent of emphysematous destructive changes of the terminal airways.

Despite numerous and extensive investigations, the morphologic evaluation of COPD by HRCT has not as yet been fully standardized.25 The present study aimed to settle some of the many questions about how best to use HRCT for the combined imaging and functional assessment of COPD.

Several variables pertaining to HRCT data acquisition may significantly interfere with accurate and reproducible lung attenuation measurements to be compared with functional data. The level of lung inflation, the number of slices, and the phase of the respiratory cycle could be the most important sources of variability in the course of HRCT data acquisition in COPD patients.

Because the relative proportion between air and tissue is the major determinant of lung attenuation, different x-ray attenuation values can result from the same HRCT slice scanned at different inflation volumes. Thus, the lack of accurate control of lung volume during data acquisition may influence the correlation between attenuation parameters and lung function in COPD. We have shown that in patients with COPD, the attenuation measurements derived from three spirometrically gated and ungated HRCT slices acquired at the same anatomic levels differed significantly.21 It then appears that the control of lung volume during scanning time by spirometric gating may represent a significant step toward the optimization of HRCT lung attenuation evaluation in COPD. It should be mentioned, however, that concomitant changes in intravascular and extravascular lung liquid volume secondary to left-heart dysfunction may significantly affect the assessment of HRCT lung attenuation.

Another area of uncertainty is the number of HRCT slices to be acquired to adequately sample the heterogeneous distribution of lung pathologic changes in COPD. Mishima and coworkers,26 comparing the results obtained by 2, 3, 5, and 10 ungated slices in 30 patients with COPD, found that for the overall lung density evaluation in emphysema, 3- and 10-slice acquisition protocols showed sufficient agreement. Similar results were obtained by Orlandi and colleagues27 with spirometrically gated scans. In this connection, given that the acquisition of a greater number of slices implies a higher radiation dose and longer acquisition times in patients with airflow limitation and dyspnea, for the purpose of the present study we limited the HRCT analysis to one slice in the upper, middle, and lower lung fields. With the advancement of CT scan technology, a low-dose volumetric acquisition would possibly provide an accurate and repeatable assessment of mean lung attenuation and derived quantitative parameters.27

As to the phase of the respiratory cycle to be selected during HRCT data acquisition, the present study confirms, with a standardized HRCT acquisition protocol, previous observations by Gevenois and coworkers showing that the inspiratory HRCT scan is accurate in depicting the extent of emphysema,514 while the expiratory scan reflects more closely hyperinflation and air trapping.15 Emphysema, however, is not the sole and major determinant of airflow limitation in COPD, and its anatomic extension can only partially predict the severity of the functional changes.367 That the extension of emphysema cannot be equated to the severity of functional alterations in COPD has been clearly evidenced by Mishima and coworkers10 who found that an accurate index of parenchymal loss, derived from fractal analysis of terminal airspace geometry and closely related to the extension of low attenuation areas on the inspiratory CT, was not correlated with functional indexes of airway obstruction and hyperinflation. Nakano and colleagues411 showed that pulmonary function abnormalities are more accurately predicted by multivariate regression with HRCT measurements of both extent of low attenuation areas and airway wall thickening than by univariate regression with low attenuation areas. The need of combining two different HRCT measurements, reflecting independently conductive airways and parenchymal pathologic changes, may further indicate that the integral effect on lung attenuation of both inflammatory and destructive tissue changes cannot be simply assessed by measuring the relative area of the lungs with attenuation values below a predetermined threshold.

The expiratory scan can also predict more accurately than the inspiratory scan the severity of dyspnea perception. The relationship between HRCT attenuation data and the MRC dyspnea scale observed in this study adds to the results obtained by Dowson and colleagues,28 who showed that the health status of patients with {alpha}1-antitrypsin deficiency, as defined by the St. George respiratory questionnaire, was better correlated with expiratory rather than with inspiratory HRCT attenuation data. Furthermore, Dawkins and colleagues29 have shown that expiratory HRCT attenuation measurements are the best independent predictors of mortality in patients with {alpha}1-antitrypsin deficiency. The enhancement of airflow limitation and air trapping obtained through prolonged, complete expiration may explain why the expiratory rather than the inspiratory HRCT parameters correlate better with dyspnea perception in this series of patients with COPD in whom emphysematous destructive changes of the terminal airspaces can be associated with inflammatory remodeling of the conductive airways. While the inspiratory HRCT parameters can measure the relative extent of emphysema, the expiratory parameters may reflect the amount of air trapped that, at any given level of emphysema extent, may greatly interfere with the health status of the patient.

In conclusion, the results of the present study, obtained with the highest degree of standardization attainable during HRCT data acquisition, show that different modalities of lung attenuation measurement reflect different functional changes underlying COPD. HRCT attenuation parameters obtained during inspiration can be used to assess the extent of parenchymal destructive changes compatible with emphysema. To obtain a more complete evaluation of the morphologic counterpart of the functional changes interfering with the health status of COPD patients, the inspiratory HRCT attenuation data should be complemented with attenuation measurements obtained at full expiration. Differentiation of x-ray low attenuation resulting from emphysema from that resulting from air trapping cannot be obtained by any single physical measurement of lung density. Evaluation of only inspiratory measurements or only expiratory measurements cannot provide a complete analysis of the dysfunction present in COPD. Both provide valuable data regarding the complexity of the disease, and they should be utilized concomitantly.

Footnotes

Abbreviations: CI = confidence interval; DLCO = diffusing capacity of the lung for carbon monoxide; DLCO% = percentage of predicted diffusing capacity of the lung for carbon monoxide; HRCT = high-resolution CT; HU = Hounsfield unit; MeanCTEXP = mean CT lung density at 10% of VC; MeanCTINSP = mean CT lung density at 90% of VC; MRC = Medical Research Council; OR = odds ratio; RAE910 = relative area with attenuation values < – 910 HU at 10% of VC; RAI950 = relative area with attenuation values < – 950 HU at 90% of vital capacity; RV% = percentage of predicted residual volume; VC = vital capacity

Supported by grants of the Departments of Critical Care and Clinical Pathophysiology of the University of Florence.

This work was performed at Section of Respiratory Medicine and at Section of Diagnostic Radiology, University of Florence, Florence, Italy.

Received for publication January 1, 2005. Accepted for publication July 22, 2005.

References

  1. . American Thoracic Society. (1995) Standards for the diagnosis and care of patients with chronic obstructive lung disease. Am J Respir Crit Care Med 152(Suppl),S77-S83[Medline]
  2. Thurlbeck, WM, Müller, NL Emphysema: definition, imaging, and quantification. AJR Am J Roentgenol 1994;163,1017-1025[Abstract/Free Full Text]
  3. Gelb, AF, Schein, M, Kuei, J, et al Limited contribution of emphysema in advanced chronic obstructive pulmonary disease. Am Rev Respir Dis 1993;147,1157-1161[ISI][Medline]
  4. Nakano, Y, Müller, NL, King, GG, et al Quantitative assessment of airway remodeling using high-resolution CT. Chest 2002;122(Suppl),271S-275S[Abstract/Free Full Text]
  5. Gevenois, PA, De Vuyst, P, de Maertelaer, V, et al Comparison of computed density and microscopic morphometry in pulmonary emphysema. Am J Respir Crit Care Med 1996;154,187-192[Abstract]
  6. Gelb, AF, Hogg, JC, Müller, NL, et al Contribution of emphysema and small airways in COPD. Chest 1996;109,353-359[Abstract/Free Full Text]
  7. Gelb, AF, Zamel, N, Hogg, JC, et al Pseudophysiologic emphysema resulting from severe small-airways disease. Am J Respir Crit Care Med 1998;158,815-819[Abstract/Free Full Text]
  8. Smit, HJM, Golding, RP, Schramel, FMNH, et al Lung attenuation measurements in healthy young adults. Respiration 2003;70,143-148[CrossRef][ISI][Medline]
  9. Smith, HJM, Golding, RP, Schramel, FMNH, et al Lung density measurements in spontaneous pneumothorax demonstrate airtrapping. Chest 2004;125,2083-2090[Abstract/Free Full Text]
  10. Mishima, M, Hirai, T, Itoh, H, et al Complexity of terminal airspace geometry assessed by lung computed tomography in normal subjects and patients with chronic obstructive pulmonary disease. Proc Natl Acad Sci U S A 1999;96,8829-8834[Abstract/Free Full Text]
  11. 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;162,1102-1108[Abstract/Free Full Text]
  12. Baldi, S, Miniati, M, Bellina, CR, et al Relationship between extent of pulmonary emphysema by high-resolution computed tomography and lung elastic recoil in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2001;164,585-589[Abstract/Free Full Text]
  13. Kalender, WA, Rienmüller, R, Seissler, W, et al Measurement of pulmonary parenchymal attenuation: use of spirometric gating with quantitative CT. Radiology 1990;175,265-268[Abstract/Free Full Text]
  14. Gevenois, PA, de Maertelaer, V, De Vuyst, P Comparison of computed density and macroscopic morphometry in pulmonary emphysema. Am J Respir Crit Care Med 1995;152,653-657[Abstract]
  15. Gevenois, PA, De Vuyst, P, Sy, M, et al Pulmonary emphysema: quantitative CT during expiration. Radiology 1996;199,825-829[Abstract/Free Full Text]
  16. Bankier, AA, de Maertelaer, V, Keyzer, C, et al Pulmonary emphysema: subjective visual grading versus objective quantification with macroscopic morphometry and thin-section CT densitometry. Radiology 1999;211,851-858[Abstract/Free Full Text]
  17. Müller, NL, Coxson, H Chronic obstructive pulmonary disease: 4. Imaging the lungs in patients with chronic obstructive pulmonary disease. Thorax 2002;57,982-985[Abstract/Free Full Text]
  18. American Thoracic Society.. Lung function testing: selection of reference values and interpretative strategies. Am Rev Respir Dis 1991;144,1202-1208[ISI][Medline]
  19. Fletcher, CM, Elmes, PC, Wood, CH The significance of respiratory symptoms and the diagnosis of chronic bronchitis in a working populations. BMJ 1959;1,257-266[ISI]
  20. American Thoracic Society.. Single breath carbon monoxide diffusing capacity (transfer factor): recommendations for a standard technique. Am J Respir Crit Care Med 1995;152,2185-2198[ISI][Medline]
  21. Moroni, C, Mascalchi, M, Camiciottoli, G, et al Comparison of spirometric-gated and -ungated HRCT in COPD. J Comput Assist Tomogr 2003;27,375-379[CrossRef][ISI][Medline]
  22. Kalender, WA, Fichte, H, Bautz, W, et al Semiautomatic evaluation procedures for quantitative CT of the lung. J Comput Assist Tomogr 1991;15,248-255[ISI][Medline]
  23. Altman, DG Practical statistics for medical research 1st ed. 1999,351-358 Chapman & Hall/CRC. Boca Raton, FL:
  24. Hosmer, DW, Lemeshow, S Applied logistic regression. 1989,135-175 John Wiley & Sons. New York, NY:
  25. Cosio, MG, Snider, GL Chest computed tomography: is it ready for major studies of chronic obstructive pulmonary disease? Eur Respir J 2001;17,1062-1064[Free Full Text]
  26. Mishima, M, Itoh, H, Sakai, H, et al Optimized scanning conditions of high resolution CT in the follow-up of pulmonary emphysema. J Comput Assist Tomogr 1999;23,380-384[CrossRef][ISI][Medline]
  27. Orlandi, I, Moroni, C, Camiciottoli, G, et al Spirometric-gated computed tomography quantitative evaluation of lung emphysema in chronic obstructive pulmonary disease: a comparison of 3 techniques. J Comput Assist Tomogr 2004;28,437-442[CrossRef][ISI][Medline]
  28. Dowson, LJ, Guest, PJ, Hill, SL, et al High resolution computed tomography scanning in {alpha}1-antitrypsin deficiency: relationship to lung function and health status. Eur Respir J 2001;17,1097-1104[Abstract/Free Full Text]
  29. Dawkins, PA, Dowson, LJ, Guest, PJ, et al Predictors of mortality in {alpha}1–antitrypsin deficiency. Thorax 2003;58,1020-1026[Abstract/Free Full Text]




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