Paper
3 March 2017 Electronic cleansing for CT colonography using spectral-driven iterative reconstruction
Author Affiliations +
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Radin A. Nasirudin, Janne J. Näppi, Toru Hironaka, Rie Tachibana, and Hiroyuki Yoshida "Electronic cleansing for CT colonography using spectral-driven iterative reconstruction", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013436 (3 March 2017); https://doi.org/10.1117/12.2255678
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CITATIONS
Cited by 3 patents.
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KEYWORDS
Computer aided diagnosis and therapy

Computed tomography

Reconstruction algorithms

Virtual colonoscopy

Image quality

Colon

CT reconstruction

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