TV-regularized CT Reconstruction and Metal Artifact Reduction Using Inequality Constraints with Preconditioning

Total variation(TV) regularization is applied to X-Ray computed tomography(CT) in an effort to reduce metal artifacts. Tikhonov regularization with $L^2$ data fidelity term and total variation regularization is augmented in this novel model by inequality constraints on sinogram data affected by metal to model errors caused by metal. The formulated problem is discretized and solved using the Chambolle-Pock algorithm. Faster convergence is achieved using preconditioning in a Douglas-Rachford spitting method as well as Advanced Direction Method of Multipliers(ADMM). The methods are applied to real and synthetic data demonstrating feasibility of the model to reduce metal artifacts. Technical details of CT data used and its processing are given in the appendix.

Sinogram constrained TV-minimization for metal artifact reduction in CT

A new method for reducing metal artifacts in X-ray computed tomography (CT) images is presented. It bases on the solution of a convex optimization problem with inequality constraints on the sinogram, and total variation regularization for the reconstructed image. The Chambolle-Pock algorithm is used to numerically solve the discretized version of the optimization problem. As proof of concept we present and discuss numerical results for synthetic data.