  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.
