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Automated Lung Volumetry from Routine Thoracic CT Scans

Rationale and Objectives

Today, lung volumes can be easily calculated from chest computed tomography (CT) scans. Modern postprocessing workstations allow automated volume measurement of data sets acquired. However, there are challenges in the use of lung volume as an indicator of pulmonary disease when it is obtained from routine CT. Intra-individual variation and methodologic aspects have to be considered. Our goal was to assess the reliability of volumetric measurements in routine CT lung scans.

Materials and Methods

Forty adult cancer patients whose lungs were unaffected by the disease underwent routine chest CT scans in 3-month intervals, resulting in a total number of 302 chest CT scans. Lung volume was calculated by automatic volumetry software. On average of 7.2 CT scans were successfully evaluable per patient (range 2–15). Intra-individual changes were assessed.

Results

In the set of patients investigated, lung volume was approximately normally distributed, with a mean of 5283 cm 3 (standard deviation = 947 cm 3 , skewness = −0.34, and curtosis = 0.16). Between different scans in one and the same patient the median intra-individual standard deviation in lung volume was 853 cm 3 (16% of the mean lung volume).

Conclusions

Automatic lung segmentation of routine chest CT scans allows a technically stable estimation of lung volume. However, substantial intra-individual variations have to be considered. A median intra-individual deviation of 16% in lung volume between different routine scans was found.

Computed tomography (CT) is a routine tool in clinical practice. CT imaging has been shown superior to plain imaging and magnetic resonance imaging in assessment of lung disease .

State-of-the-art postprocessing workstations allow automated lung-volume measurement from CT data sets. These measurements are of interest in assessing pulmonary conditions that involve alterations in lung volume . Emphysema or chronic allergic asthma can cause an increase in lung volume, cystic fibrosis or sclerodermia can reduce lung volume. In these diseases, lung volume and its time are appropriate markers of disease activity and disease progression .

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Materials and methods

Subjects

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Procedures and Techniques

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Data Collection and Validation

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Figure 1, Display of the automated volumetry software used (Philips Extended Brilliance Workspace, Philips Medical Systems, Netherlands). Counterclockwise: three-dimensional reconstruction of the segmented lungs, representative CT scan slice, and results of lung volume calculation.

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Statistical Tests

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Results

Study Group

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Segmentation

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Lung Volumes

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Figure 2, Estimation of density. The graph shows the frequency (y-axis) of lung volumes in the 272 chest CT scans at hand. Each calculated lung volume is represented by one point of the continuous line . The dotted line represents a normal curve distribution.

Table 1

Calculated Lung Volumes (Philips Extended Brilliance Workspace, Philips Medical Systems, Netherlands)

Lung Volume Total (cm 3 ) Right Lung (cm 3 ) Left Lung (cm 3 ) Mean 5283 2869 2414 Standard deviation 947 506 480 Median 5457 2962 2468

Figure 3, Descriptive statistics of lung volumes in patients 1–8. The first eight patients are shown as a representative (pseudorandom) selection. (a) Boxplots of lung volumes in patients 1–8. Boxes show quartiles, whiskers show ranges, and the central line the median. (b) Lung volume in patients 1–8 over time.

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Discussion

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Conclusions

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Acknowledgments

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