Computational analysis of thoracic multidetector row HRCT for segmentation and quantification of small airway air trapping and emphysema in obstructive pulmonary disease.

RATIONALE AND OBJECTIVES

Obstructive pulmonary disease phenotypes are related to variable combinations of emphysema and small-airway disease, the latter manifested as air trapping (AT) on imaging. The investigators propose a method to extract AT information quantitatively from thoracic multi-detector row high-resolution computed tomography (HRCT), validated by pulmonary function testing (PFT) correlation.

MATERIALS AND METHODS

Seventeen patients with obstructive pulmonary disease who underwent HRCT and PFT within a 3-day interval were retrospectively identified. Thin-section volumetric HRCT in inspiration and expiration was registered and analyzed using custom-made software. Nonaerated regions of lung were segmented through exclusion of voxels > -50 Hounsfield units (HU); emphysematous areas were segmented as voxels < -950 HU on inspiratory images. Small-airway AT volume (ATV) was segmented as regions of lung voxels whose attenuation values increased by less than a specified change threshold (set from 5 to 300 HU in 25-HU increments) between inspiration and expiration. Inspiratory and expiratory total segmented lung volumes, emphysema volume (EV), and ATV for each threshold were subsequently calculated and correlated with PFT parameters.

RESULTS

A strong positive correlation was obtained between total segmented lung volume in inspiration and total lung capacity (r = 0.83). A strong negative correlation (r = -0.80) was obtained between EV and the ratio between forced expiratory volume in 1 second and forced vital capacity. Stronger negative correlation with forced expiratory volume in 1 second/forced vital capacity (r = -0.85) was demonstrated when ATV (threshold, 50 HU) was added to EV, indicating improved quantification of total AT to predict obstructive disease severity. A moderately strong positive correlation between ATV and residual volume was observed, with a maximum r value of 0.72 (threshold, 25 HU), greater than that between EV and residual volume (r = 0.58). The benefit of ATV quantification was greater in a subgroup of patients with negligible emphysema compared to patients with moderate to severe emphysema.

CONCLUSIONS

Small-airway AT segmentation in conjunction with emphysema segmentation through computer-assisted methodologies may provide better correlations with key PFT parameters, suggesting that the quantification of emphysema-related and small airway-related components of AT from thoracic HRCT has great potential to elucidate phenotypic differences in patients with chronic obstructive pulmonary disease.