Computer-aided detection of lung nodules: Influence of the image reconstruction kernel for computer-aided detection performance

Objective: To evaluate the relationship between a computed tomographic reconstruction kernel and the sensitivity of a computer-aided detection (CAD) system for lung nodule detection. Methods: We retrospectively studied 36 consecutive patients with no known pulmonary nodules who underwent low-dose computed tomography for lung cancer screening with 3 different reconstruction kernels (B, C, and L). All series were reviewed with a commercial CAD system for lung nodule detection. Results: The 36 scans showed 231 uncalcified nodules (170 micronodules and 61 nodules). There was little variation of sensitivities for each series (82%, 88%, and 82% for the nodules of B, C, and L, respectively). When the results of 2 series were combined, sensitivities were boosted (B + C, 89%; B + L, 95%; and C + L, 96% for the nodules). Conclusions: Sensitivity of the CAD system was influenced by the selection of the reconstruction kernel. By combining data from 2 different kernels, CAD sensitivity can be elevated without further patient radiation exposure.