PULMONARY NODULES - IMPROVED DETECTION WITS VASCULAR SEGMENTATION ANDEXTRACTION WITH SPIRAL CT

PURPOSE: To determine whether extraction of pulmonary vessels from com puted tomographic (CT) images with automated segmentation improves the detection of pulmonary nodules. MATERIALS AND METHODS: Simulated nodu les were superimposed on normal spiral CT images. Eight patients refer red for CT assessment of pulmonary nodules were selected for clinical evaluation Vessels were extracted from both the simulation and clinica l study with a three-dimensional seeded region-growing algorithm. Thre e experienced radiologists were asked to locate the nodules and assign a level of confidence to their findings. Sensitivity and proportion o f false-positive results per case (FPC) were calculated. Observer perf ormance was evaluated by alternate free-response receiver operating ch aracteristic analysis. RESULTS: Extraction of vascular structures from CT scans improved sensitivity from 63% to 84% in the simulation study and from 58% to 78% in the clinical study. The proportion of FPC decr eased from 52% to 24% and from 55% to 12% respectively. Radiologists p erformed Consistently better with the segmented images than with the o riginal images in both the simulation (P = .006) and the clinical (P = .0013) study. CONCLUSION: Automated vessel subtraction and extraction improves detection of pulmonary nodules.