Elsevier

Academic Radiology

Volume 20, Issue 5, May 2013, Pages 537-545
Academic Radiology

Original investigation
Computed Tomography Density Histogram Analysis to Evaluate Pulmonary Emphysema in Ex-smokers

https://doi.org/10.1016/j.acra.2012.11.010Get rights and content

Rationale and Objectives

High-resolution computed tomography (CT) measurements of emphysema typically use Hounsfield unit (HU) density histogram thresholds or observer scores based on regions of low x-ray attenuation. Our objective was to develop an automated measurement of emphysema using principal component analysis (PCA) of the CT density histogram.

Materials and Methods

Ninety-seven ex-smokers, including 53 subjects with chronic obstructive pulmonary disease (COPD) and 44 asymptomatic subjects (AEs), provided written informed consent to imaging as well as plethysmography and spirometry. We applied PCA to the CT density histogram to generate whole lung and regional density histogram principal components including the first and second components and the sum of both principal components (density histogram principal component score [DHPCS]). Significant relationships for DHPCS with single HU thresholds, pulmonary function measurements, an expert's emphysema score, and hyperpolarized 3He magnetic resonance imaging apparent diffusion coefficients (ADCs) were determined using linear regression and Pearson coefficients. Receiver operator characteristics analysis was performed using forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) as the independent diagnostic.

Results

There was a significant difference (P < .0001) between AE and COPD subjects for DHPCS; FEV1/FVC; diffusing capacity of lung for carbon monoxide%predicted; attenuation values below −950, −910, and −856 HU; and 3He ADCs. There were significant correlations for DHPCS with FEV1/FVC (r = −0.85, P < .0001); diffusing capacity of lung for carbon monoxide%predicted (r = −0.67, P < .0001); attenuation values below −950/−910/−856 HU (r = 0.93/0.96/0.76, P < .0001); and 3He ADCs (r = 0.85, P < .0001). Receiver operator characteristics analysis showed a 91% classification rate for DHPCS.

Conclusions

We generated an automated emphysema score using PCA of the CT density histogram with a 91% COPD classification rate that showed strong and significant correlations with pulmonary function tests, single HU thresholds, and 3He magnetic resonance imaging ADCs.

Section snippets

Study Subjects

Ex-smokers were enrolled from the general population and a local tertiary health care center, as previously described 30, 31 with a smoking history of at least 10 pack-years. COPD subjects were categorized according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria 32, 33. All subjects provided written informed consent to the study protocol approved by the local research ethics board and Health Canada, and the study was compliant with the Personal Information

Results

Demographic characteristics are provided in Table 1 for 97 ex-smokers including 44 AE subjects (n = 28 men, mean ± SD age 70 ± 8 years, range 50–85 years) and 53 subjects with COPD (n = 37 men, mean ± SD age 71 ± 9 years, range 48–87 years). Spirometry and plethysmography measurements acquired a few minutes before imaging are also shown and reflect the inclusion criteria for COPD and AE subjects. COPD ex-smokers included 11 subjects with GOLD stage I COPD, 25 subjects with stage II COPD, 13

Discussion

Until very recently, automated methods for the quantification of emphysema have been based on single HU thresholds of the CT density histogram. To address some of the limitations of this robust and straightforward approach, more complex and texture-based methods have been devised and applied to a number of pulmonary conditions 12, 13, 14, 15, 16, 17. This important previous work has broadened our understanding of the information content within thoracic CT, but there is still no consensus about

Acknowledgments

A Owrangi gratefully acknowledges support from the Western Graduate Research Fund provided by The University of Western Ontario (London Canada). Dr Parraga acknowledges salary support from the Canadian Institutes of Health Research New Investigator Award. We also thank S. Halko, S. McKay for clinical coordination, and T. Szekeres for MRI of research subjects.

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    Contract Grant Sponsor: Canadian Institutes of Health Research (grants MOP 97748, MOP 106437, and FRN 97687).

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