title: Computational analysis of thoracic multidetector row HRCT for segmentation and quantification of small airway air trapping and emphysema in obstructive pulmonary disease.
abstract: OBJECTIVE
  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.
  
  
  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.
authors: Department of Radiology, Hospital of the University of Pennsylvania, University of Pennsylvania School of Medicine, Philadelphia, 19104, USA. eduardo.barbosa@uphs.upenn.edu
authors: Eduardo Mortani
authors: EM
authors: Barbosa
authors: Gang
authors: G
authors: Song
authors: Nicholas
authors: N
authors: Tustison
authors: Maryl
authors: M
authors: Kreider
authors: James C
authors: JC
authors: Gee
authors: Warren B
authors: WB
authors: Gefter
authors: Drew A
authors: DA
authors: Torigian
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging* / Q000503:physiopathology; D011656:Pulmonary Emphysema / Q000000981:diagnostic imaging* / Q000503:physiopathology; D011857:Radiographic Image Interpretation, Computer-Assisted; D013902:Radiography, Thoracic / Q000379:methods*; D012129:Respiratory Function Tests; D012189:Retrospective Studies; D012984:Software; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*