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.