Metal artifact reduction using L1 and non-local penalties with iterative sinogram correction

Metal artifact reduction is a challenging issue in CT reconstruction. Due to insufficient measurements after passing through the metal object, the break down of the inconsistency in attenuation sinogram results in severe steak artifacts in the reconstructed image. In this project, we propose a metal artifact reduction method using l1 norma and non-local penalties with iterative sinogram correction, where the 3D in-painting algorithm is iteratively used to estimate the sinogram in the iteratively updated metal regions. Metal and non-metal images using l1 norm and non-local penalties are reconstructed separately. The split Bregmann algorithm and the generalized non-local formula were applied to solve the optimization problems associated with l1 norm and non-local penalties. Both body phantom simulations and real dental CT experiments verify that the proposed method can significantly reduce the metal artifacts and provide more clear details of the image structure.

Flowchart of the iterative sinogram correction.

Iterative reconstruction using (a) l1 and (b) non-local penalties for metal and non-metal images, and (c) final image is the sum of both images.

K. Kim, J. C. Ye, G. E. Fakhri and Q. Li, Metal artifact reduction using L1 and non-local penalties with iterative sinogram correction, The Third International Conference on Image Formation in X-Ray Computed Tomography, Salt Lake City, Utah, USA, June, 2014.