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Proceedings Paper

Physical priors in virtual colonoscopy
Author(s): Hassan Rivaz; Yoshihisa Shinagawa; Jianming Liang
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Paper Abstract

Electronic colon cleansing (ECC) aims to remove the contrast agent from the CT abdominal images so that a virtual model of the colon can be constructed. Virtual colonoscopy requires either liquid or solid preparation of the colon before CT imaging. This paper has two parts to address ECC in both preparation methods. In the first part, meniscus removal in the liquid preparation is studied. The meniscus is the curve seen at the top of a liquid in response to its container. Left on the colon wall, the meniscus can decrease the sensitivity and specificity of virtual colonoscopy. We state the differential equation that governs the profile of the meniscus and propose an algorithm for calculating the boundary of the contrast agent. We compute the surface tension of the liquid-colon wall contact using in-vivo CT data. Our results show that the surface tension can be estimated with an acceptable degree of uncertainty. Such an estimate, along with the meniscus profile differential equation will be used as an a priori knowledge to aid meniscus segmentation. In the second part, we study ECC in solid preparation of colon. Since the colon is pressurized with air before acquisition of the CT images, a prior on the shape of the colon wall can be obtained. We present such prior and investigate it using patient data. We show the shape prior is held in certain parts of the colon and propose a method that uses this prior to ease pseudoenhancement correction.

Paper Details

Date Published: 27 February 2009
PDF: 12 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726019 (27 February 2009); doi: 10.1117/12.813554
Show Author Affiliations
Hassan Rivaz, Johns Hopkins Univ. (United States)
Yoshihisa Shinagawa, Siemens Medical Solutions USA, Inc. (United States)
Jianming Liang, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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