Using correlative properties of neighboring pixels to improve gray-white differentiation in pediatric head CT images

Abstract

BACKGROUND AND PURPOSE: A lower radiation dose can have a detrimental effect on the quality of head CT images. The aim of this study performed in a pediatric population was to test whether an image-processing algorithm (Correlative Image Enhancement) based on the correlation among intensities of neighboring pixels can improve gray-white differentiation in head CTs. MATERIALSANDMETHODS: Sixty baseline head CT images with normal findings obtained from scans of 30 children were processed using Correlative Image Enhancement to produce corresponding enhanced images. Gray-white differentiation in baseline and enhanced images was assessed quantitatively by calculating the contrast-to-noise ratio and conspicuity in equivalent ROIs in gray and white matter. Two masked readers rated the images for visibility of gray-white differentiation on a 5-point Likert scale. Differences in both quantitative and qualitative measures of gray-white differentiation between baseline and enhanced images were tested for statistical significance. P values < .05 were considered significant. RESULTS: Image processing resulted in improvement in the contrast-to-noise ratio (from 1.86 0.94 to 2.26 1.00, < .01). CONCLUSIONS: Image processing using Correlative Image Enhancement had a beneficial effect on quantitative measures of gray-white differentiation. This translated into improved perception of gray-white differentiation by readers. Further studies are needed to assess the effect of such image processing on the detection of disease processes using head CTs.