Characterization and suppression of edge and aliasing artefacts in iterative x-ray CT reconstruction

For the purpose of obtaining x-ray tomographic images, statistical reconstruction (SR) provides a general framework with possible advantages over analytical algorithms such as filtered backprojection (FBP) in terms of flexibility, resolution, contrast and image noise. However, SR images may be seriously affected by some artefacts that are not present in FBP images. These artefacts appear as aliasing patterns and as severe overshoots in the areas of sharp intensity transitions ('edge artefacts'). We characterize this inherent property of iterative reconstructions and hypothesize how discretization errors during reconstruction contribute to the formation of the artefacts. An adequate solution to the problem is to perform the reconstructions on an image grid that is finer than that typically employed for FBP reconstruction, followed by a downsampling of the resulting image to a granularity normally used for display. Furthermore, it is shown that such a procedure is much more effective than post-filtering of the reconstructions. Resulting SR images have superior noise-resolution trade-off compared to FBP, which may facilitate dose reduction during CT examinations.