A segmentation-based method for metal artifact reduction.

Rationale And Objectives: We propose a novel segmentation-based interpolation method to reduce the metal artifacts caused by surgical aneurysm clips.

Materials And Methods: Our method consists of five steps: coarse image reconstruction, metallic object segmentation, forward-projection, projection interpolation, and final image reconstruction. The major innovations are 2-fold. First, a state-of-the-art mean-shift technique in the computer vision field is used to improve the accuracy of the metallic object segmentation. Second, a feedback strategy is developed in the interpolation step to adjust the interpolated value based on the prior knowledge that the interpolated values should not be larger than the original ones. Physical phantom and real patient datasets are studied to evaluate the efficacy of our method.

Results: Compared to the state-of-the-art segmentation-based method designed previously, our method reduces the metal artifacts by 20-40% in terms of the standard deviation and provides more information for the assessment of soft tissues and osseous structures surrounding the surgical clips.

Conclusion: Mean shift technique and feedback strategy can help to improve the image quality in terms of reducing metal artifacts.