A prior-based metal artifact reduction algorithm for x-ray CT

In computed tomography (CT), metal objects in the scanning filed are accompanied by physical phenomenon that causes projections to be inconsistent. These inconsistencies produce bright and dark shadows or streaks in analytically reconstructed images. Interpolation-based metal artifact reduction (MAR) algorithms usually replace the inconsistent projection data by estimating surrogate data based on the surrounding uncorrupted projections. In such cases, secondary artifacts will be generated when the data estimates are inaccurate. Therefore, better projection estimation is critical. This paper proposes an image post-processing strategy to create an intermediate image, named the prior image and better estimates of the surrogate data by forward projecting this prior image. The proposed method consists of three steps based on the forward projection MAR framework. First, metallic implants in the uncorrected images are segmented using a Markov random field model (MRF). Then a prior image is generated via an edge-preserving filter and a recovery procedure of the adjacent anatomical structures. Finally, the projection is completed via forward projecting this prior image and the corrected image is reconstructed by the filtered backprojection (FBP) method. Studies on both phantom and clinical data are carried out to verify the performance of the proposed method. The comparisons with other previous MAR algorithms demonstrate that the proposed MAR method performs better in metal artifact suppression and anatomical structure preservation.