Evaluation of surrogate data quality in sinogram-based CT metal-artifact reduction

In this work different surrogate data strategies to reduce metal artifacts in reconstructed CT images are tested. Inconsistent sinogram projection data caused by e.g. beam hardening are the origin of metal artifacts in the reconstructed images. The goal of this work is to replace this inconsistent projection data by artificially generated data. Therefore, here, two ID interpolation strategies, a directional interpolation based upon the sinogram 'flow' and a ID interpolation by means of the non-equispaced fast Fourier transform are compared to a fully 2D method based upon the idea of image inpainting. Due to the fact that the artificially generated data never perfectly fit the gap inside the projection data caused by the inconsistencies, those repaired sinogram data are reconstructed using a weighted Maximum Likelihood Expectation Maximization algorithm called λ-MLEM algorithm. In this way, the artificially generated data, still contaminated with residual inconsistencies, are weighted less during reconstruction.