TV-regularized CT Reconstruction and Metal Artifact Reduction Using Inequality Constraints with Preconditioning
This addresses metal artifacts in medical CT imaging, which is an incremental improvement over existing regularization methods.
The researchers tackled metal artifact reduction in X-ray CT reconstruction by developing a novel model combining TV regularization with inequality constraints on sinogram data affected by metal, achieving faster convergence through preconditioning and demonstrating feasibility on real and synthetic data.
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.