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Proceedings Paper

Noise/dose reduction and image improvements in screening virtual colonoscopy with tube currents of 20 mAs with nonlinear Gaussian filter chains
Author(s): Georg-Friedemann Rust; Volker Aurich; Maximilian Reiser
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Paper Abstract

Purpose: To evaluate a filter method to extract noise from 20mAs Computed Tomography (CT) data for virtual colonoscopy screening. Method: Nonlinear Gaussian filter chains (NLGF) applied to CT datasets were used to extract noise. To test the efficiency of NLGF a simulation of different ellipsoidal shells with different levels of noise were used. Phantom studies were performed using a multidetector CT (tube currents 10 to 140mAs). 15 patients at high risk for colon cancer underwent a virtual colonoscopy examination (140mAs) and conventional colonoscopy. Different noise levels were added to each CT raw dataset (analog to 40 and 20mAs scans). Virtual endoscopic fly-throughs were performed and rated by two radiologists (image quality). Results: NLGF were able to extract image noise while preserving image structures down to signal--to--noise ratio levels of 0.5. The phantom studies (perspex bars, simulated polyps) were reconstructed without relevant changes between 20 and 140mAs. There were no significant differences between the endoscopic fly-throughs of 140 and 20mAs examinations (2 readers). Conclusion: NLGF is a promising preprocessing method for effective noise reduction in CT datasets. Edges are preserved as well as accentuated in high contrast images.

Paper Details

Date Published: 24 April 2002
PDF: 12 pages
Proc. SPIE 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images, (24 April 2002); doi: 10.1117/12.463582
Show Author Affiliations
Georg-Friedemann Rust, Ludwig-Maximilians-Univ. Munich (Germany)
Volker Aurich, Heinrich-Heine-Univ. Duesseldorf (Germany)
Maximilian Reiser, Ludwig-Maximilians-Univ. Munich (Germany)


Published in SPIE Proceedings Vol. 4683:
Medical Imaging 2002: Physiology and Function from Multidimensional Images
Anne V. Clough; Chin-Tu Chen, Editor(s)

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