A Re-weighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction

TL;DR

This paper introduces a novel wavelet frame based joint spatial-Radon domain CT reconstruction model with re-weighting to effectively reduce metal artifacts, improving image quality in CT scans with metal implants.

Contribution

It proposes a re-weighted joint spatial-Radon domain model that enhances artifact reduction by integrating a new weighting strategy and an efficient solution algorithm.

Findings

Achieves better CT reconstruction quality than existing methods.

Effectively reduces metal artifacts in simulated and real data.

Demonstrates convergence and efficiency of the proposed algorithm.

Abstract

High density implants such as metals often lead to serious artifacts in the reconstructed CT images which hampers the accuracy of image based diagnosis and treatment planning. In this paper, we propose a novel wavelet frame based CT image reconstruction model to reduce metal artifacts. This model is built on a joint spatial and Radon (projection) domain (JSR) image reconstruction framework with a built-in weighting and re-weighting mechanism in Radon domain to repair degraded projection data. The new weighting strategy used in the proposed model not only makes the regularization in Radon domain by wavelet frame transform more effective, but also makes the commonly assumed linear model for CT imaging a more accurate approximation of the nonlinear physical problem. The proposed model, which will be referred to as the re-weighted JSR model, combines the ideas of the recently proposed wavelet frame based JSR model \cite{Dong2013} and the normalized metal artifact reduction model \cite{meyer2010normalized}, and manages to achieve noticeably better CT reconstruction quality than both methods. To solve the proposed re-weighted JSR model, an efficient alternative iteration algorithm is proposed with guaranteed convergence. Numerical experiments on both simulated and real CT image data demonstrate the effectiveness of the re-weighted JSR model and its advantage over some of the state-of-the-art methods.