Dual-energy material density images obtained by prereconstruction-basis material decomposition techniques offer specific tissue information, but they exhibit relatively high pixel noise. It is shown that noise in the material density images is negatively correlated and that this can be exploited for noise reduction in the two-basis material density images. The algorithm minimizes noise-related differences between pixels and their local mean values, with the constraint that monoenergetic CT values, which can be calculated from the density images, remain unchanged. Applied to the material density images, a noise reduction by factors of 2 to 5 is achieved. While quantitative results for regions of interest remain unchanged, edge effects can occur in the processed images. To suppress these, locally adaptive algorithms are presented and discussed. Results are documented by both phantom measurements and clinical examples.

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