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Proceedings Paper

Towards reduced-preparation spectral-CT-colonography utilizing local covariance
Author(s): Rafael Wiemker; Tobias Klinder; Jörg Sabczynski; Amar Dhanantwari; Chansik An; Benjamin M. Yeh; Judy Yee
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Paper Abstract

In CT colonography (CTC), residual stool is a possible confounder in the detection of colonic polyps. While there is a clear clinical need for reduced or minimal bowel preparation for CT colonography, residual stool that is poorly tagged by oral contrast agent prevents satisfactory electronic cleansing (EC) by standard methods on conventional CT. Our study aims to answer quantitatively whether dual-layer spectral-CT allows superior discrimination of residual stool. 60 spectral CT colonography scans were obtained in clinical practice, and careful exhaustive ground truth was established by consensus reading.

Results indicate that spectral CT adds significant discrimination power, in particular when utilizing local spectral variances and covariances, which can be computed efficiently by standard Gaussian filter operations. Simple linear spectral material separation, however, is sufficient only in extended homogeneous regions. In subtle finely structured transition areas, non-linear classifiers or convolutional neural networks are required because of non-linear local multi material superposition effects.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130I (10 March 2020); doi: 10.1117/12.2549539
Show Author Affiliations
Rafael Wiemker, Philips Research Hamburg (Germany)
Tobias Klinder, Philips Research Hamburg (Germany)
Jörg Sabczynski, Philips Research Hamburg (Germany)
Amar Dhanantwari, Philips CT Clincal Science North America (United States)
Chansik An, Univ. of California, San Francisco (United States)
Benjamin M. Yeh, Univ. of California, San Francisco (United States)
Judy Yee, Montefiore Medical Ctr. (United States)
Albert Einstein College of Medicine (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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