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Proceedings Paper

Pseudo-enhancement correction for computer-aided detection in fecal-tagging CT colonography
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Paper Abstract

Fecal-tagging CT colonography (CTC) presents an opportunity to minimize colon cleansing while maintaining high diagnostic accuracy for the detection of colorectal lesions. However, the pseudo-enhancement introduced by tagging agents presents several problems for the application of computer-aided detection (CAD). We developed a correction method that minimizes pseudo-enhancement in CTC data by modeling of the pseudo-enhancement as a cumulative Gaussian energy distribution. The method was optimized by use of an anthropomorphic colon phantom, and its effect on our fully automated CAD scheme was tested by use of leave-one-patient-out evaluation on 23 clinical CTC cases with reduced colon cleansing based upon dietary fecal tagging. There were 28 colonoscopy-confirmed polyps ≥6 mm. Visual evaluation indicated that the method reduced CT attenuation of pseudo-enhanced polyps to standard soft-tissue Hounsfield unit (HU) range without affecting untagged regions. At a 90% detection sensitivity for polyps ≥6 mm, CAD yielded 8.5 false-positive (FP) detections and 3.9 FP detections per volumetric scan without and with the application of the pseudo-enhancement correction method. These results indicate that the pseudo-enhancement correction method is a potentially useful pre-processing step for automated detection of polyps in fecal-tagging CTC, and that CAD can yield a high detection sensitivity with a relatively low FP rate in CTC with patient-friendly reduced colon preparation.

Paper Details

Date Published: 29 March 2007
PDF: 8 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140A (29 March 2007); doi: 10.1117/12.710186
Show Author Affiliations
Janne Näppi, Massachusetts General Hospital and Harvard Medical School (United States)
Hiroyuki Yoshida, Massachusetts General Hospital and Harvard Medical School (United States)
Michael Zalis, Massachusetts General Hospital and Harvard Medical School (United States)
Wenli Cai, Massachusetts General Hospital and Harvard Medical School (United States)
Philippe Lefere, Stedelijk Ziekenhuis (Belgium)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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