Efficient Metal Artifact Reduction Method Based on Improved Total Variation Regularization

Metal implants produce strong artifacts in reconstructed computed tomography images and thus severely reduce image quality. This study proposes a method for metal artifact reduction that combines modified compressed sensing reconstruction and the sinogram inpainting method. The procedure starts with total variation reconstruction to obtain an initial image. Then, the image is recovered using improved total-variation-based regularization. This process produces an artifact-free background image. The missing information in the original sinogram is complemented using the forward projection of the background image. Consequently, the transitions between the original sinogram and artificial sinogram are smoothed by re-matching the baseline. The algorithm is validated using simulations and phantom data. Results show that streaks are eliminated and shadows are significantly reduced.

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