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Journal List > J Korean Soc Radiol > v.67(5) > 1087183

Oh, Chae, Seo, Jung, Oh, and Lee: CT Densitometry of the Lung in Healthy Nonsmokers with Normal Pulmonary Function

Abstract

Purpose

To investigate the upper normal limit of low attenuation area% in healthy nonsmokers.

Materials and Methods

A total of 36 nonsmokers with normal pulmonary function test underwent a CT scan. Six thresholds (-980 - -930 HU) on inspiration CT and two thresholds (-950 and -910 HU) on expiration CT were used for obtaining low attenuation area%. The mean lung density was obtained on both inspiration CT and expiration CT. Descriptive statistics of low attenuation area% and the mean lung density, evaluation of difference of low attenuation area% and the mean lung density in both sex and age groups, analysis of the relationship between demographic information and CT parameters were performed.

Results

Upper normal limit for low attenuation area% was 12.96% on inspiration CT (-950 HU) and 9.48% on expiration CT (-910 HU). Upper normal limit for the mean lung density was -837.58 HU on inspiration CT and 686.82 HU on expiration CT. Low attenuation area% and the mean lung density showed no significant differences in both sex and age groups. Body mass index (BMI) was negatively correlated with low attenuation area% on inspiration CT (-950 HU, r = -0.398, p = 0.016) and positively correlated with the mean lung density on inspiration CT (r = 0.539, p = 0.001) and expiration CT (r = 0.432, p = 0.009). Age and body surface area were not correlated with low attenuation area% or the mean lung density.

Conclusion

Low attenuation area% on CT densitometry of the lung could be found in healthy nonsmokers with normal pulmonary function, and showed negative association with BMI. Reference values, such as range and upper normal limit for low attenuation area% in healthy subjects could be helpful in quantitative analysis and follow up of early emphysema, using CT densitometry of the lung.

Figures and Tables

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Fig. 1

Box and whisker plot of low attenuation area (%) along each thresholds on inspiratory CT (A) and expiratory CT (B).

Note.-central line = median, ends of boxes = interquartile boundaries, whiskers = data point closest to inner fence (1.5 × interquartile range), • = mild outlier, * = severe outlier, HU = Hounsfield unit, LAA (%) = low attenuation area %

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Fig. 2

Example of quantitative densitometry in a patient with mild emphysema and a healthy subject.

A. A 54-year-old male who have mild degree of centrilobular emphysema. A coronal CT image (window level: -750 HU, width: 1500 HU) shows some areas of centrilobular emphysema in both upper lobes. Low attenuation area (%) is shown on a color-coded image demonstrating pixels having CT density below the threshold -950 HU and the value was 4.01%.
B. A 59-year-old male who is one of healthy nonsmokers in this study. Emphysema is not evident on a coronal CT image (window level: -750 HU, width: 1500 HU). However, on a color-coded image, there are color-coded areas representing low attenuation area at -950 HU which value was 13.45%.

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Fig. 3

Correlation between low attenuation area (%) at -950 HU on inspiration and body mass index.

Note.-BMI = body mass index, LAA (%) = low attenuation area %

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Table 1

Demographic Information of Subjects

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Note.-SD = standard deviation

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Table 2

Results of CT Densitometry in All Subjects

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Note.-LAAe = low attenuation area on expiratory image, LAAi = low attenuation area on inspiratory image, LVi = inspiratory lung volume, MLDe = mean lung density on expiratory image, MLDi = mean lung density on inspiratory image, SD = standard deviation

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Table 3

Results of Correlation Analysis between CT Densitometry and Demographic Information

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Note.-*Statistically significant result.

BSA = body surface area, BMI = body mass index, LAAe = low attenuation area on expiratory image, LAAi = low attenuation area on inspiratory image, LVi = inspiratory lung volume, MLDe = mean lung density on expiratory image, MLDi = mean lung density on inspiratory image, SD = standard deviation

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Notes

This work was supported by the grants of the Korean Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (A040153, A084305).

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