  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.
