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

TL;DR

This paper introduces a novel TV-regularized CT reconstruction method that incorporates inequality constraints on sinogram data to effectively reduce metal artifacts, utilizing advanced optimization algorithms for faster convergence.

Contribution

The paper proposes a new model combining TV regularization with inequality constraints and demonstrates its effectiveness with preconditioned optimization algorithms.

Findings

Effective reduction of metal artifacts in CT images

Faster convergence using preconditioning techniques

Successful application to real and synthetic data

Abstract

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.