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Automated Detection of Pulmonary Nodules on CT Images: Effect of Section Thickness and Reconstruction Interval—Initial Results¹

¹ From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, St Louis, MO 63110. Received July 23, 2004; revision requested September 29; revision received October 23; accepted December 10. J.H.K. supported by a postdoctoral fellowship program from the Korea Science and Engineering Foundation (KOSEF). Address correspondence to K.T.B. (e-mail: baet{at}mir.wustl.edu).

Institutional review board approval was obtained. Informed patient consent was not required. Study was compliant with HIPAA. Performance of an automated pulmonary nodule detection program was evaluated on multi–detector row CT images that were acquired once but reconstructed retrospectively at different section thicknesses and reconstruction intervals. From raw CT data in 10 patients with pulmonary nodules, three sets of CT images were reconstructed separately in each patient by selecting two section thickness and reconstruction combinations, respectively: thin group, 1 and 1 mm; overlap group, 5 and 1 mm; and thick group, 5 and 5 mm. Nodules 3 mm in diameter and larger were detected in each group (thin group, 126 nodules; overlap group, 121 nodules; and thick group, 114 nodules) by means of consensus of two radiologists. Findings were used as the reference standard for evaluation of the computer-aided detection (CAD) program. Sensitivity and number of false-positive findings per patient by CAD were: thin group, 95.2% (120 of 126 nodules) and 5.4 findings; overlap group, 94.2% (114 of 121 nodules) and 9.7 findings; and thick group, 88.6% (101 of 114 nodules) and 23.6 findings, indicating that nodule detection degraded with increase in section thickness but improved substantially with a small reconstruction interval.

© RSNA, 2005

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