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Proceedings Paper

Low-dose dual-energy electronic cleansing for fecal-tagging CT Colonography
Author(s): Wenli Cai; Da Zhang; June-Goo Lee; Hiroyuki Yoshida
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Paper Abstract

Dual-energy electronic cleansing (DE-EC) provides a promising means for cleansing the tagged fecal materials in fecaltagging CT colonography (CTC). However, the increased radiation dose due to the double exposures in dual-energy CTC (DE-CTC) scanning is a major limitation for the use of DE-EC in clinical practice. The purpose of this study was to develop and evaluate a low-dose DE-EC scheme in fecal-tagging DE-CTC. In this study, a custom-made anthropomorphic colon phantom, which was filled with simulated tagged materials by non-ionic iodinated contrast agent (Omnipaque iohexol, GE Healthcare), was scanned by a dual-source CT scanner (SOMATON Definition Flash, Siemens Healthcare) at two photon energies: 80 kVp and 140 kVp with nine different tube current settings ranging from 12 to 74 mAs for 140 kVp, and then reconstructed by soft-tissue reconstruction kernel (B30f). The DE-CTC images were subjected to a low-dose DE-EC scheme. First, our image-space DE-CTC denoising filter was applied for reduction of image noise. Then, the noise-reduced images were processed by a virtual lumen tagging method for reduction of partial volume effect and tagging inhomogeneity. The results were compared with the registered CTC images of native phantom without fillings. Preliminary results showed that our low-dose DE-EC scheme achieved the cleansing ratios, defined by the proportion of the cleansed voxels in the tagging mask, between 93.18% (12 mAs) and 96.62% (74 mAs). Also, the soft-tissue preservation ratios, defined by the proportion of the persevered voxels in the soft-tissue mask, were maintained in the range between 94.67% and 96.41%.

Paper Details

Date Published: 18 March 2013
PDF: 9 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700W (18 March 2013); doi: 10.1117/12.2008115
Show Author Affiliations
Wenli Cai, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Da Zhang, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
June-Goo Lee, Univ. of Pittsburgh (United States)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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