Observer study of a noise suppression algorithm for computed radiography images

Lower x-ray exposures are commonly used in radiographic exams to reduce the patient radiation dose. An unwanted side effect is that the noise level increases as the exposure level is reduced. Image enhancement techniques increasing image contrast, such as sharpening and dynamic range compression tend to increase the appearance of noise. A Gaussian filter-based noise suppression algorithm using an adaptive soft threshold has been designed to reduce the noise appearance in low-exposure images. The advantage of this technique is that the algorithm is signal-dependent, and therefore will only impact image areas with low signal-to-noise ratio. Computed radiography images captured with lower exposure levels were collected from clinical sites, and used as controls in an observer study. The noise suppression algorithm was applied to each of the control images to generate test images. Hardcopy printed film versions of control and test images were presented side-by-side on a film alternator to six radiologists. The radiologists were asked to rate the control and test images using a 9-point diagnostic quality rating scale and a 7-point delta-preference rating scale. The results showed that the algorithm reduced noise appearance, which was preferred, while preserving the diagnostic image quality. This paper describes the noise suppression algorithm and reports on the results of the observer study.