HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) All Versions of this Article: 2361041288v1 236/1/295    most recent Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kim, J.-S. Articles by Bae, K. T. Search for Related Content PubMed PubMed Citation Articles by Kim, J.-S. Articles by Bae, K. T.

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

This article has been cited by other articles:

				F. Girvin and J. P. Ko Pulmonary Nodules: Detection, Assessment, and CAD Am. J. Roentgenol., October 1, 2008; 191(4): 1057 - 1069. [Abstract] [Full Text] [PDF]

				R. Iezzi, A. R. Cotroneo, A. Filippone, F. Di Fabio, M. Santoro, and M. L. Storto MDCT Angiography in Abdominal Aortic Aneurysm Treated with Endovascular Repair: Diagnostic Impact of Slice Thickness on Detection of Endoleaks Am. J. Roentgenol., December 1, 2007; 189(6): 1414 - 1420. [Abstract] [Full Text] [PDF]

				P. Peloschek, J. Sailer, M. Weber, C. J. Herold, M. Prokop, and C. Schaefer-Prokop Pulmonary Nodules: Sensitivity of Maximum Intensity Projection versus That of Volume Rendering of 3D Multidetector CT Data Radiology, May 1, 2007; 243(2): 561 - 569. [Abstract] [Full Text] [PDF]