Automatic Quantification of Airway Dimensions in COPD: Processing Large Databases of Chest CT scans

M. Schmidt, E. van Rikxoort, O. Mets, P. de Jong, J. Kuhnigk and B. van Ginneken

Annual Meeting of the Radiological Society of North America 2012.

PURPOSE Pathological changes of the airways are strongly associated with lung function impairment in chronic obstructive pulmonary disease (COPD). A system for automatic quantification of airway dimensions in chest CT scans is presented and its performance is validated. METHOD AND MATERIALS For this study, 1113 full inspiration low dose chest CT scans (16x0.75mm, 120-140kVp, 30mAs) of male participants of a lung cancer screening trial were processed automatically. Processing starts with detection of the trachea and segmentation of the airway tree based on region growing and morphological processing. The airway segmentation is converted to a centerline-model and the 5 lobes are classified by searching for maximal subtrees in terms of volume and separation of barycenters. Cross-section image planes oriented perpendicular to the local airway direction are defined at a spacing of 1mm throughout the entire airway tree. For each of them, intensity profiles of 72 rays pointing from the center point outwards are analyzed using an intensity integration technique, which accounts for partial volume effects and allows for accurate determination of inner and outer wall boundaries. Lumen diameter and area, wall thickness and area, relative wall area and lumen perimeter are calculated per cross-section. Branching areas and positions where the detection of the boundaries failed are automatically excluded from further analysis. For each scan, an experienced radiologist was asked to accept or reject the measurements based on structured reports showing a model of the airway tree with the classified lobes and a representative selection of up to 98 airway cross-sections and the detected inner and outer wall boundaries. The reliability of the system was determined based on the overall acceptance ratio and the occurence of different processing errors that lead to a rejection of measurements. RESULTS The measurements in 1042 cases (93.6%) were rated as excellent by an experienced radiologist. The remaining cases were rejected due to problems with the trachea detection (0.7%), airway segmentation (2.6%), lobe classification (1.8%) or other reasons (1.3%). CONCLUSION Automatic quantification of airway dimensions in large chest CT scan databases is feasible with a high success rate.