A hybrid metal artifact reduction algorithm for x-ray CT

Purpose: Presence of metal artifacts is a major reason of degradation of computed tomography image quality and there is still no standard solution to this issue. A class of recently investigated metal artifact reduction (MAR) methods based on forward projection of a prior image that is artifact-free to replace the metal affected projection data have shown promising results. However, usually it is hard to get a good prior image which is close to the true image without artifacts. This work aims at creating a good prior image so that the forward projection can replace the metal affected projection data well. Methods: The proposed method consists of four steps based on the forward projection MAR framework. First, metal implants in the reconstructed image are segmented and the corresponding metal traces in the projection domain are identified. Then the prior image is obtained by two steps. A processed precorrected image is generated as an initial prior image first and then in the next step it is used as the initial image of the iterative reconstruction from the unaffected projection data to generate a better prior image. In order to deal with severe artifacts, the iteration incorporates the total variation minimization constraint as well as a novel constraint which forces the soft tissue region near metal to be as flat as possible. Finally, the projection is completed using forward projection of the prior image and the corrected image is reconstructed by FBP. A linear interpolation MAR method and two recently reported forward projection based methods are performed simultaneously for comparison. Results: The proposed method shows outstanding performance on both phantoms and patients datasets. This approach can reduce artifacts dramatically and restore tissue structures near metal to a large extent. Unlike competing MAR methods, it can effectively prevent introduction of new artifacts and false structures. Moreover, the proposed method has the lowest RMSE in regions of both soft tissue and bone tissue among the corrected images and is ranked as the best method for evaluation, by radiologists. Conclusions: Both subjective and quantitative evaluations of the results demonstrate the superior performance of the proposed algorithm, compared to that of the competing methods. This method offers a remarkable improvement of the image quality.