American Journal of Respiratory and Critical Care Medicine

The fundamental importance of calibration for any measuring device is indisputable, but computed tomography (CT) calibration in longitudinal lung densitometry studies is largely unexplored. Although the validity of CT as a measure of emphysema has been confirmed in cross-sectional studies, there are limited data on long-term reproducibility, and this is critically important for validating its use as an outcome measure in therapeutic trials. A general understanding of the strengths and pitfalls of CT densitometry is critical for physicians reviewing the published literature using this methodology. In our study of 57 patients with alpha-1 antitrypsin deficiency (phenotype PiZ), progression of voxel index determined from three successive annual scans acquired with a fully calibrated scanner was intimately associated with changes in CT air densitometry, sampled from patient images. Images were therefore reanalyzed, using a correction technique validated in phantom studies that adjusted for changes in measured air density, and the reliability of the voxel index as a measure of emphysema progression was improved. Comparison of adjusted voxel index thresholds indicated the optimum threshold was –950 Hounsfield units. Internal air calibration is therefore critical in longitudinal and multicenter lung densitometry studies of emphysema and incorporation of a correction factor is essential for quantitative image analysis.

Projected estimates of the global burden of chronic obstructive pulmonary disease have led to recognition of the need for the development of novel therapies to modify disease progression. In routine clinical practice, physiologic measures such as diffusion capacity and airflow obstruction are traditionally employed as surrogate measures of emphysema severity. However, practical difficulties arising from the limitations of these conventional methods (1) necessitate the development of alternative outcome measures for use in clinical trials (2).

In contrast to chronic obstructive pulmonary disease, which is defined in physiologic terms, emphysema is defined pathologically (3). Nevertheless, it may now be diagnosed accurately and quantified noninvasively by computed tomography (CT). The loss of lung tissue associated with emphysema can be measured as a reduction in lung density by CT, using various parameters derived from the frequency distribution histogram of lung voxels. The “voxel index” (VI) method calculates the proportion of low-attenuation lung voxels within a desired range, and although various voxel index thresholds have been employed and validated against pathology (47), it is likely that no single threshold is appropriate universally.

The fundamental importance of calibration in any measuring device is indisputable, yet few studies have addressed the issue of CT calibration in lung densitometry. Short-term reproducibility has been demonstrated (810), but there are limited published data on long-term reproducibility (11). It is recognized that X-ray tube ageing and replacement may introduce data acquisition errors (11), but although accurate scanner calibration is mandatory, procedures of standardization have yet to be defined. Previous studies have used water phantoms alone for quality assurance (4, 8), but in a cross-sectional study to evaluate conformity between scanners, Kemerink and coworkers (12) demonstrated that although water densitometry was consistent, the Hounsfield number for air varied between scanners. A method was proposed for correcting the CT numbers for a scanner with poor air calibration and its potential use as a means of standardizing densitometry was described (12). However, although standardization has been recognized to be crucially important (13), the influence of air calibration errors on lung densitometry studies in a clinical setting has yet to be addressed systematically. Such information is crucial to determine the validity and hence interpretation of observational and interventional studies.

The provisional assessment of CT progression in a sample of patients attending our program had unexpectedly suggested an increase in lung density (consistent with resolution of emphysema) in a limited number of serial annual scans acquired in the early months of 2000 (L. Dowson, personal communication). The current study was therefore undertaken to review the consistency of CT densitometry for the measurement of emphysema progression in a cohort of patients with alpha-1 antitrypsin deficiency (AATD) and to standardize densitometry measurements by adjusting for any identifiable scanner errors. Validation of the methodology was performed in studies with an anthropomorphic phantom. In addition, the opportunity was taken to review data from the Dutch–Danish controlled trial (14) relating to scanner calibration.

Some of the results of these studies have been reported previously in the form of an abstract (15).

Data records of 57 patients with AATD (PiZ) were selected from among those attending our program (see the online supplement) to include all subjects who had completed three consecutive annual assessments over 2 years spanning the months of concern in early 2000 (see Introduction). Alpha-1 antitrypsin level and phenotype were verified by immunoassay and isoelectric focusing, respectively, using a dried finger-prick blood spot (Heredilab, Salt Lake City, UT). The program was approved by the local research ethics committee, and all subjects gave written informed consent.

Annual CT images were acquired on a General Electric Prospeed scanner (General Electric Medical Systems, Milwaukee, WI), using a high-resolution protocol (120 kVp [kilovolt peak], 200 mA · s [dose rate], 1-mm collimation, bone reconstruction algorithm) on inspiration and expiration in the supine position as described previously (16). The voxel indices (VI) (−910 Hounsfield units [HU]) were calculated for upper (level of the aortic arch) and lower (level of the inferior pulmonary veins) zone images, using the manufacturer's Density Mask program.

The methodology for the current study comprised two steps before the analysis of patient data with an additional phantom study to validate the technique employed for the patient data.

  1. Error identification. Consistency of lung densitometry was explored by plotting annual VI change against scan date. Air measurements acquired retrospectively from patient images, using region of interest (ROI) densitometry plus weekly quality assurance records of water and bone phantom measurements, were related to date. Both data sets were assessed for discontinuity to identify any coincident events.

  2. See Dirksen and coworkers (14), and the online supplement, for an analysis of the Dutch–Danish controlled trial.

  3. Development of a correction method. Air, water, and bone density values were compared to characterize the behavior of the scanner over time across a wide range of density values. A correction equation was developed to adjust lung densitometry for air calibration errors by predicting the potential influence of these three measures on the VI threshold.

Validation Studies Using a Lung Phantom
Correction technique for longitudinal data.

An anthropomorphic lung phantom was scanned, using the same protocol as in the clinical study, using a Marconi MxView scanner (Philips Medical Systems, Andover, MA) with the facility to adjust the CT number for air. Lung densitometry was calculated for a single slice, using Pulmo-CMS software (MEDIS [Medical Imaging Systems], Leiden, The Netherlands) at different calibration settings without the threshold adjustment and applying the correction equation to the data to determine their efficacy.

Correction technique for cross-sectional multicenter data.

Calibration and conformity of six third-generation multislice scanners (two General Electric Lightspeed scanners, one Philips MxView, one Philips MX 8000, one Toshiba Asteion, and one Siemens Sensation 16) were explored, using the dog phantom. The scanning protocol employed was that proposed for future clinical densitometric studies (140 kVp; 40 mA; 5-mm collimation; pitch, 1.5; 2.5-mm increment; field of view, 500 mm; and a smooth reconstruction filter) (17). Air calibration measurements were acquired as described above and surrogate measures of blood density were derived from ROI measurements of the phantom heart.

Whole lung VI of −910 HU was calculated for each series with and without the use of an internal calibration procedure and the effect on standardization was assessed by the coefficient of variation.

Analysis of Clinical Images: Employing the Validated Correction Technique

Upper-zone inspiratory images obtained previously were reanalyzed, using the “adjusted” threshold of −910 HU. Comparison was made of the annual VI progression rate, using uncorrected and adjusted thresholds for three groups of consecutive annual scans: (1) scans predating the discontinuity, (2) scans spanning the discontinuity, and (3) scans following the discontinuity.

In addition, the VI was calculated for corrected images, using 20-HU threshold increments from −870 to −950 HU and comparison of progression over 2 years was performed.

Statistical Analysis

Data were analyzed with a statistical software package (Statistical Package for the Social Sciences [SPSS] version 10.0.5; SPSS, Chicago, IL). Air calibration was assessed with discontinuity analysis and regression slopes were compared with the F test. The rate of CT progression in the three subgroups of scan pairs was compared by Kruskal–Wallis test and in the two subgroups of scan pairs, before and after the discontinuity, by Mann–Whitney test. Comparison of the baseline and Year 2 scans at the different thresholds was done with a Wilcoxon signed rank test for paired data. Differences were considered significant at p < 0.05.

Baseline Characteristics

The baseline lung function and voxel indices (uncorrected threshold, −910 HU; expressed as percentage) at baseline are shown in Table 1

TABLE 1. Baseline characteristics


Absolute Value*

% Predicted*
Age, yr5750 (46–61)NA
FEV1, L571.34 (0.97–2.08)42 (30–67)
VC, L573.82 (3.12–4.49)98 (90–127)
RV, L572.66 (2.06–3.44)135 (100–170)
DLCO, mmol/min/kPa575.90 (4.62–7.64)63 (47–84)
DLCO/VA, L mmol/min/kPa/L571.03 (0.73–1.31)67 (46–83)
UZI voxel index, –910 HU5734.2 (21.6–44.6)NA
UZE voxel index, –910 HU5719.0 (7.7–34.2)NA
LZI voxel index, –910 HU5755.3 (33.2–64.5)NA
LZE voxel index, –910 HU
47.0 (26.4–57)

*Median (interquartile range).

Postdual bronchodilation with nebulized salbutamol (5 mg) and ipratropium bromide (500 μg).

Definition of abbreviations: DLCO = single-breath diffusing capacity of the lung for carbon monoxide (gas transfer); DLCO/VA = DLCO normalized per liter alveolar volume (transfer coefficient); HU = Hounsfield unit; LZE = lower-zone expiratory high-resolution computed tomography (HRCT) image; LZI = lower-zone inspiratory HRCT image; NA = not applicable; RV = residual volume; UZE = upper-zone expiratory HRCT image; UZI = upper-zone inspiratory HRCT image.


Error Identification–Discontinuity Analysis

A plot of annual VI change against scan date showed a trend occurring in March 2000 of reversed VI progression, implying disease improvement (Figure 1A)

. The best fitting model for voxel index progression placed this discontinuity (see the online supplement) just before March 6, 2000 and produced an estimated shift of 4.5% (p < 0.001). Quality assurance records of scanner calibration showed that measurements using the manufacturer's water phantom were well within the manufacturer's tolerance of 0 ± 10 HU, but a concomitant shift in air density measured from patient images was observed (Figure 2A). Regression analysis of the air calibration data indicated discontinuity with a positive shift in air number that occurred between the observation on March 6 and the following observation on March 11, 2000 (p < 0.001) (Figure 2A). In addition, an opposing drift of decreasing air number also existed throughout the study period, described by the gradient b in the equation y = a + bx1 + cx2 (see the online supplement). There was no difference between the gradient of drift before and after the discontinuity (p = 0.77) and, after elimination of the discontinuity, the overall gradient drift was significantly different from zero (p < 0.001) (Figure 2B).

Measurement of blood density in the descending thoracic aorta did not identify any time-related trend (data not shown).

Dutch–Danish Study

A similar drift but no obvious discontinuity was seen by analyzing ROI air measurements in the Dutch patients included in the Dutch–Danish study of augmentation therapy (14). The individual patient data are shown in Figure 3


Development of a Correction Method

Changes in air, water, and bone density values indicated that the effect of an increase in air number would be to increase proportionally the Hounsfield number of those voxels with a density intermediate between water and air (see Figure 4A)

. Therefore, correction for air calibration errors on VI threshold could be achieved with the following formula: corrected threshold = (air calibration value − water calibration value /−1,000) × index threshold (see Figure 4B).

Validation Studies with Lung Phantom
Correction technique for longitudinal data.

The distribution of voxels with the dog lung phantom was similar to that seen in patients with AATD (data not shown), suggesting that it was a good model for emphysematous lung. Adjusting the scanner air calibration (see Methods) resulted in a proportional shift in the lung voxel distribution histogram and marked changes to the voxel index (−910 HU) as predicted from the studies described above. Application of the correction technique reproduced the same results at each calibration setting (see Table 2)

TABLE 2. Single image voxel index at a threshold of −910 hu for dog lung phantom

Adjusted Air Calibration

−1,000 HU
−975 HU
−950 HU
−925 HU
−900 HU
Uncorrected VI30.27 7.91 1.05 0.08 0.00
Corrected VI

Definition of abbreviations: NA = not applicable; VI = voxel index.

Table shows the effects of changing air calibration and the corrected value achieved through application of the formula presented in text.


Correction technique for cross-sectional multicenter data.

Interscanner variability for air densitometry was greater than for blood densitometry and there was wide discrepancy in the measurement of dog lung voxel indices (VI, −910 HU). There was a significant reduction in this discrepancy by standardizing for air calibration, using the method described here and used in the main study, but the use of blood calibration did not reduce variability (see Table 3)

TABLE 3. Mean, standard deviation, and coefficient of variation for blood and air density values measured on six different scanners

VI, −910 HU

Blood Density (HU)
Air Density (HU)
No Calibration
Blood Calibration
Air Calibration
Standard deviation 5.36   13.73 8.95 9.27 3.33
Coefficient of variation

Voxel index values (VI, −910 HU) for whole dog lung are shown, demonstrating the influence of standardization on scanner variability by calibrating on the basis of either blood or air densitometry. The latter resulted in an almost threefold reduction in variability.


CT Progression: Application of the Correction Method

The observed reversal in annual voxel index progression that occurred in the patient cohort was eliminated on use of the correction technique (see Figure 1B). For the uncorrected data, the progression rate for scan pairs spanning the discontinuity was significantly different (p < 0.001) from that of the other two groups, in which the scan pair either predated or postdated the discontinuity (see Figure 5A)

and suggests an overall improvement over this time period. This difference was abolished when the corrected data for the three groups were compared (p = 0.13) (see Figure 5B). However, there was no significant difference in VI progression rate for scan pairs obtained either before or after the discontinuity in both the uncorrected (p = 0.92) and corrected data (p = 0.72) (see Figures 5A and 5B).

Adjusting the analysis threshold for air calibration also improved the detection of progression over 2 years as shown by a comparison of Z statistic (see Figure 6)

. Progression of the voxel index at the uncorrected threshold of −910 HU was statistically significant only for the upper-zone inspiratory scans (see Figure 6A) as shown previously (8). However, after adjustment with correction for air calibration, significant progression at the threshold of −910 HU was confirmed in both upper- and lower-zone scans (see Figure 6B). The annual VI progression rates at a corrected threshold of −910 HU are shown in Table 4

TABLE 4. Annual progression rates of single image voxel index at a threshold of −910 hu, adjusted for changes in air calibration with data previously reported and included for comparison*


Median Adjusted Rate (IQR)

Mean Annual Rate Reported Previously (SE)
UZI1.67 (–0.23 to 3.28)2.82 (0.55)
LZI0.61 (–1.63 to 2.72)1.03 (0.56)
UZE0.58 (–0.50 to 2.98)1.67 (0.38)
1.17 (–0.29 to 3.85)
1.66 (0.77)

*See Reference 8.

n = 57.

n = 43.

Definition of abbreviations: IQR = interquartile range; LZE = lower-zone expiratory; LZI = lower-zone inspiratory; SE = standard error; UZE = upper-zone expiratory; UZI = upper-zone inspiratory.

, expressed as median and interquartile range, with data from the previous study (8) for comparison.

The upper-zone voxel index showed significant progression at all thresholds used, with marginal statistical superiority of the inspiratory images over the expiratory images (Figure 6A). Furthermore, in the lower-zone scans the corrected expiratory images now also showed significant progression whatever the threshold and the corrected inspiratory images demonstrated significant progression at thresholds of −950, –930, and −910 HU (Figure 6B). Although all thresholds showed highly significant progression of VI, the Z statistic was best for −950 HU, suggesting it is the most appropriate for assessing progression among the patients studied here.

The current study demonstrates the importance of precise scanner and image calibration in longitudinal lung densitometry studies for the measurement of emphysema progression. We found that voxel index progression in a cohort of patients with AATD was associated with changes in air density measurements obtained retrospectively from patient images. A clear shift occurred in air calibration in March 2000 that produced an obvious reversal in voxel index progression that not only aroused our initial suspicion of an error, but also allowed us to causally relate the two events. An isolated drift in air calibration would probably have remained undetected without specific observation using an ROI calibration and the effect on VI progression would have been one of overestimation. No significant difference was found between the rate of air calibration drift before and after March 2000 and this is reflected in the comparable rates of apparent VI progression during these periods.

The underlying cause of the changes to our scanner calibration remains uncertain despite careful inspection of the service records. Although the most likely cause for our observations is the replacement of the X-ray tube and detectors, which occurred in January 2000, no definite explanation can be offered for the calibration errors. Importantly, the changes in measured air density occurred despite the apparent stability of CT calibration when defined by the CT numbers for air and water obtained during regular routine scanner maintenance and from the weekly quality assurance measurements using the manufacturer's water phantom. A similar finding has previously been reported in a longitudinal study documenting changes in measurements of aortic blood density, where full routine scanner calibration was documented monthly (11). Measurements obtained from patient images therefore provide essential calibration information in addition to routine procedures in which densitometry is applied to simple water phantoms or air measurements obtained with an empty gantry.

The scanner used in our study was a third-generation single-slice helical CT. Although technological improvements and the incorporation of features such as daily autocalibration into modern scanners may improve the accuracy and precision of lung densitometry studies, these procedures ensure only a value of −1,000 HU for air with an empty gantry, which may engender its own problems. For instance, using the technique described in the current study, we have identified a similar drift in air calibration in other scanners, including the Philips Tomoscan SR7000 scanner (see Figure 3) that was used in the Dutch–Danish trial of alpha-1 antitrypsin augmentation therapy (14). These errors occurred in spite of the regular and frequent procedure of CT number adjustment for air calibration that was performed with an empty gantry. The drift is likely to have influenced the consistency of lung densitometry because we have demonstrated a close association between these measures and VI changes. This provides further evidence of the importance of including an internal calibration procedure to adjust for potential errors in air densitometry in longitudinal studies of emphysema. The error correction described in our study overcame the problem of drift in air calibration, providing a more accurate measure of emphysema progression. Our results confirm that our previous finding of highest VI progression rate in the upper-zone inspiratory images (8) remains valid after adjustment of scanner calibration errors. Furthermore, adjustment for air calibration resulted in improved variability and the detection of statistically significant progression in all images, even at the threshold of −910 HU used in our original study (8). Consequently, this correction method has now been incorporated in the analysis software being used for our clinical trials and includes calibration of the entire image data before lung segmentation and histogram analysis (18).

Adjustment of lung densitometry to correct for errors in air calibration provides a mechanism to improve scanner consistency in longitudinal studies and scanner comparability in multicenter studies as validated in the phantom studies. The voxel distribution histograms for the dog lung phantom compared well with those from patients with emphysema, confirming that the phantom was an appropriate model to validate the correction method. Although the scanning protocols differed between clinical and phantom studies, it was necessary in the multicenter phantom study to use a whole lung volume scanning protocol and whole lung densitometry to reduce sampling errors.

The multiscanner phantom study confirms the findings of Kemerink and coworkers (12) that consistency of water calibration between scanners coexists with great variability in air calibration. It is not surprising, as the density of blood is close to that of water, that interscanner variability in lung densitometry is not improved by an internal calibration method that incorporates blood density values alone whereas variability is improved when adjusting for differences in air calibration. However, the application of blood calibration may be of greater importance for lung densitometry studies of conditions such as pulmonary fibrosis, which produce changes in tissues of greater density than those affected by the emphysematous process (19).

Good correlation has been reported between quantitative CT and other measures of emphysema both for the voxel index method (6, 7, 2024) and the percentile method (22). Gevenois and coworkers have shown that the optimal VI threshold (using a high-resolution protocol) is −950 HU in cross-sectional studies comparing CT with both macroscopic (20) and microscopic morphometry (6). In the current study, several VI threshold values were used because it was considered that sensitivity to the changes of emphysema progression would be threshold dependent and, although the correction method was expected to improve variability, it was recognized that the effect could also be to change the average VI threshold to one less sensitive than −910 HU. There are limited data on the measurement of emphysema progression by serial quantitative CT, but in a study of 22 patients with severe AATD over 2–4 years Dirksen and coworkers (4) showed that the optimum VI threshold for whole lung analysis was −930 HU. The data presented here on two different single lung slices confirm that −930 HU was able to detect progression with a high degree of statistical significance, as were the other thresholds. The threshold that appeared statistically the best for progression using this high-resolution protocol (as determined by the Z statistic) was −950 HU, although the practicality of validating this in a longitudinal study using pathology is preclusive. Nevertheless, the good correlation of this threshold with pathology in cross-sectional studies (6, 20) suggests that the changes in our study (at least on inspiratory scans that would not be influenced by changes in air trapping, as in expiratory scans) also reflect progression of emphysema.

Notwithstanding the errors resulting from variability in sampling and inspiratory level, the single-slice protocol employed here is sensitive to emphysema progression and also limits radiation exposure. Volume scanning protocols may prove to be more reproducible and sensitive to progression and the development of low radiation dose protocols, in combination with faster scanning of the chest, currently facilitates the use of volume scans in ongoing clinical trials. These newer protocols allow correction for differences in inspiration level between scans, which may improve consistency further in longitudinal studies and also enable the retrieval of single slices from whole lung image sets to identify regions in the lung with a better response to treatment for emphysema.

In summary, air calibration is crucial to the consistency of lung densitometry studies in emphysema, where tissue is replaced by air. Air number measurements obtained from patient images are more relevant to lung densitometry than standard quality assurance values and may be used in a simple correction process that adjusts for any air calibration errors. Although this process may also be applicable as the first step toward the standardization of lung densitometry between centers, further studies are needed. Details of calibration method are essential to the interpretation of lung densitometry, and quality assurance procedures for densitometry studies should include the technique of air calibration described here. These data must be reported in longitudinal studies of disease progression and when direct comparison is made between densitometry performed on different scanners to ensure validity.

The authors thank KCARE (Kings College Hospital, London) for the loan of the lung phantom and Derek Tarrant for technical support with the use of the Marconi MxView scanner. R. A. Stockley and J. Stolk are members of AIR (the Alpha-1 International Registry, and thank the other council members of AIR for valuable discussions on the results of this study.

1. Burrows B. A clinical trial of efficacy of antiproteolytic therapy: can it be done? Am Rev Respir Dis 1983;127:S42–S43.
2. Croxton TL, Weinmann GG, Senior RM, Wise RA, Crapo JD, Buist AS. Clinical research in chronic obstructive pulmonary disease: needs and opportunities. Am J Respir Crit Care Med 2003;167:1142–1149.
3. Snider GL, Kleinerman J, Thurlbeck WM, Bengali ZH. The definition of emphysema: report of a National Heart, Lung, and Blood Institute, Division of Lung Diseases Workshop. Am Rev Respir Dis 1985;132:182–185.
4. Dirksen A, Friis M, Olesen KP, Skovgaard LT, Sorensen K. Progress of emphysema in severe α1-antitrypsin deficiency as assessed by annual CT. Acta Radiol 1997;38:826–832.
5. Gevenois PA, Zanen J, de Maertelaer V, De Vuyst P, Dumortier P, Yernault JC. Macroscopic assessment of pulmonary emphysema by image analysis. J Clin Pathol 1995;48:318–322.
6. Gevenois PA, De Vuyst P, de Maertelaer V, Zanen J, Jacobovitz D, Cosio MG, Yernault JC. Comparison of computed density and microscopic morphometry in pulmonary emphysema. Am J Respir Crit Care Med 1996;154:187–192.
7. Müller NL, Staples CA, Miller RR, Abboud RT. Density mask: an objective method to quantitate emphysema using computed tomography. Chest 1988;94:782–787.
8. Dowson LJ, Guest PJ, Stockley RA. Longitudinal changes in physiological, radiological, and health status measurements in α1-antitrypsin deficiency and factors associated with decline. Am J Respir Crit Care Med 2001;164:1805–1809.
9. Gierada DS, Yusen RD, Pilgram TK, Crouch L, Slone RM, Bae KT, Lefrak SS, Cooper JD. Repeatability of quantitative CT indexes of emphysema in patients evaluated for lung volume reduction surgery. Radiology 2001;220:448–454.
10. Kemerink GJ, Lamers RJ, Thelissen GR, van Engelshoven JM. CT densitometry of the lungs: scanner performance. J Comput Assist Tomogr 1996;20:24–33.
11. Stoel BC, Vrooman HA, Stolk J, Reiber JH. Sources of error in lung densitometry with CT. Invest Radiol 1999;34:303–309.
12. Kemerink GJ, Lamers RJ, Thelissen GR, van Engelshoven JM. Scanner conformity in CT densitometry of the lungs. Radiology 1995;197:749–752.
13. Morgan MD. Detection and quantification of pulmonary emphysema by computed tomography: a window of opportunity. Thorax 1992;47:1001–1004.
14. Dirksen A, Dijkman JH, Madsen F, Stoel BC, Hutchison DC, Ulrik CS, Skovgaard LT, Kok-Jensen A, Rudolphus A, Seersholm N, et al. A randomized clinical trial of α1-antitrypsin augmentation therapy. Am J Respir Crit Care Med 1999;160:1468–1472.
15. Parr DG, Stoel BC, Stolk J, Stockley RA. Lung densitometry: achieving standardization by overcoming variability in scanner performance [abstract]. Am J Respir Crit Care Med 2004;169:A881.
16. Dowson LJ, Newall C, Guest PJ, Hill SL, Stockley RA. Exercise capacity predicts health status in α1-antitrypsin deficiency. Am J Respir Crit Care Med 2001;163:936–941.
17. Stolk J, Ng WH, Bakker ME, Reiber JH, Rabe KF, Putter H, Stoel BC. Correlation between annual change in health status and computer tomography derived lung density in subjects with α1-antitrypsin deficiency. Thorax 2003;58:1027–1030.
18. Stoel BC, Bakker ME, Stolk J, Dirksen A, Stockley RA, Piitulainen E, Russi EW, Reiber JH. Comparison of the sensitivities of 5 different computed tomography scanners for the assessment of the progression of pulmonary emphysema: a phantom study. Invest Radiol 2004;39:1–7.
19. Best AC, Lynch AM, Bozic CM, Miller D, Grunwald GK, Lynch DA. Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. Radiology 2003;228:407–414.
20. Gevenois PA, de Maertelaer V, De Vuyst P, Zanen J, Yernault JC. Comparison of computed density and macroscopic morphometry in pulmonary emphysema. Am J Respir Crit Care Med 1995;152:653–657.
21. Gevenois PA, De Vuyst P, Sy M, Scillia P, Chaminade L, de Maertelaer V, Zanen J, Yernault JC. Pulmonary emphysema: quantitative CT during expiration. Radiology 1996;199:825–829.
22. Gould GA, MacNee W, McLean A, Warren PM, Redpath A, Best JJ, Lamb D, Flenley DC. CT measurements of lung density in life can quantitate distal airspace enlargement: an essential defining feature of human emphysema. Am Rev Respir Dis 1988;137:380–392.
23. Hayhurst MD, MacNee W, Flenley DC, Wright D, McLean A, Lamb D, Wightman AJ, Best J. Diagnosis of pulmonary emphysema by computerised tomography. Lancet 1984;2:320–322.
24. Kuwano K, Matsuba K, Ikeda T, Murakami J, Araki A, Nishitani H, Ishida T, Yasumoto K, Shigematsu N. The diagnosis of mild emphysema: correlation of computed tomography and pathology scores. Am Rev Respir Dis 1990;141:169–178.
Correspondence and requests for reprints should be addressed to R. A. Stockley, M.D., F.R.C.P., D.Sc., Lung Investigation Unit, First Floor, Nuffield House, Queen Elizabeth Hospital, Birmingham B15 2TH, UK. E-mail:


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