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Metal-induced artifacts may cause severe problems in clinical computed tomography (CT) imaging and may impair diagnosis as well as overall image quality. Many approaches for reducing these artifacts tackle the problem by simply ignoring or interpolating the metal traces in the raw data, which results in a general information loss and additional artifacts in the corrected image. It was the objective of this study to develop an approach aiming at correcting several physical artifact sources. We have also tried to minimize the impact on spatial resolution and attempted to avoid new artifacts resulting from the correction.The algorithm works with a first volumetric reconstruction followed by threshold-based metal prostheses segmentation. The segmented metal implants are then forward projected and the resulting sinogram entries are squared and combined, followed by a second reconstruction to yield correction volumes. The resulting volumes are then combined linearly with a combination weight determined to minimize the flatness of the initial image. A directional filtering algorithm following the beam hardening correction applies a nonlinear convolution in the metal traces of the sinogram which reduces existing metal-induced noise artifacts.Phantom measurements on a polyethylene (PE) disc with different inserts and a semi-anthropomorphic hip phantom with optional bone and titanium inserts were used for evaluating the algorithm. Patient datasets containing uni- and bilateral hip endoprostheses verified the applicability and efficiency on realistic clinical cases.Deviations in CT values were reduced to below 3 HU on average. Image noise reduction of up to 70% was achieved (average noise reduction of 37%) with a more homogeneous CT value distribution in soft-tissue areas. A comparison to standard interpolation methods showed superior artifact suppression without producing artifacts caused by interpolation and without the general information loss in the close vicinity to the implants. The impact on spatial resolution was minimized as compared with interpolation algorithms.Metal artifacts caused by hip-endoprostheses were strongly reduced. Soft tissue areas and skeletal structures surrounding the implants were well restored. The correction works by postprocessing CT datasets and it is applicable to any reconstructed image without a priori knowledge.