Regression-based sinogram replacement for CT metal artifact reduction

In x-ray computed tomography (CT) scanning, the presence of metal objects gives rise to artifacts. Although dual-energy CT scanning and decomposition can reduce metal artifacts, in practice, the decomposition is unstable in the presence of noise and yields residual and new artifacts. A common practice in metal artifact reduction (MAR) algorithms is to use a prior-image as a guide to estimating the underlying data that are corrupted by the metal. We have developed a method in which one prior-image can be used to correct the various sinograms generated by dual-energy decomposition. We applied our method to data acquired on a commercial CT scanner. Compared to the uncorrected images, the MAR images have superior uniformity in known uniform regions while preserving edges, and better visual definition of structures.