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Proceedings Paper

Electronic cleansing for CT colonography using spectral-driven iterative reconstruction
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Paper Abstract

Dual-energy computed tomography is used increasingly in CT colonography (CTC). The combination of computer-aided detection (CADe) and dual-energy CTC (DE-CTC) has high clinical value, because it can detect clinically significant colonic lesions automatically at higher accuracy than does conventional single-energy CTC. While CADe has demonstrated its ability to detect small polyps, its performance is highly dependent on several factors, including the quality of CTC images and electronic cleansing (EC) of the images. The presence of artifacts such as beam hardening and image noise in ultra-low-dose CTC can produce incorrectly cleansed colon images that severely degrade the detection performance of CTC for small polyps. Also, CADe methods are very dependent on the quality of input images and the information about different tissues in the colon. In this work, we developed a novel method to calculate EC images using spectral information from DE-CTC data. First, the ultra-low dose dual-energy projection data obtained from a CT scanner are decomposed into two materials, soft tissue and the orally administered fecal-tagging contrast agent, to detect the location and intensity of the contrast agent. Next, the images are iteratively reconstructed while gradually removing the presence of tagged materials from the images. Our preliminary qualitative results show that the method can cleanse the contrast agent and tagged materials correctly from DE-CTC images without affecting the appearance of surrounding tissue.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013436 (3 March 2017); doi: 10.1117/12.2255678
Show Author Affiliations
Radin A. Nasirudin, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Janne J. Näppi, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Toru Hironaka, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Rie Tachibana, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Institute of National Colleges of Technology (Japan)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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