No abstract

In this study we compared various defect pixel correction methods for reducing artifact appearance within projection images used for computed tomography (CT) reconstructions. Defect pixel correction algorithms were examined with respect to their artifact behaviour within planar projection images as well as in volumetric CT reconstructions. We investigated four algorithms: nearest neighbour, linear and adaptive linear interpolation, and a frequency-selective spectral-domain approach. To characterise the quality of each algorithm in planar image data, we inserted line defects of varying widths and orientations into images. The structure preservation of each algorithm was analysed by corrupting and correcting the image of a slit phantom pattern and by evaluating its line spread function (LSF). The noise preservation was assessed by interpolating corrupted flat images and estimating the noise power spectrum (NPS) of the interpolated region. For the volumetric investigations, we examined the structure and noise preservation within a structured aluminium foam, a mid-contrast cone-beam phantom and a homogeneous Polyurethane (PUR) cylinder. The frequency-selective algorithm showed the best structure and noise preservation for planar data of the correction methods tested. For volumetric data it still showed the best noise preservation, whereas the structure preservation was outperformed by the linear interpolation. The frequency-selective spectral-domain approach in the correction of line defects is recommended for planar image data, but its abilities within high-contrast volumes are restricted. In that case, the application of a simple linear interpolation might be the better choice to correct line defects within projection images used for CT.