  Metal artifact reduction (MAR) is a challenging problem in computed
tomography (CT) imaging. A popular class of MAR methods replace sinogram
measurements that are corrupted by metal with artificial data. While these
``projection completion'' approaches are successful in eliminating severe
artifacts, secondary artifacts may be introduced by the artificial data. In
this paper, we propose an approach which uses projection completion to generate
a prior image, which is then incorporated into an iterative reconstruction
algorithm based on the superiorization framework. The prior image is
reconstructed using normalized metal artifact reduction (NMAR), a popular
projection completion approach. The iterative algorithm is a modified version
of the simultaneous algebraic reconstruction technique (SART), which reduces
artifacts by incorporating a polyenergetic forward model, least-squares
weighting, and superiorization. The penalty function used for superiorization
is a weighted average between a total variation (TV) term and a term promoting
similarity with the prior image, similar to penalty functions used in prior
image constrained compressive sensing. Because the prior is largely free of
severe metal artifacts, these artifacts are discouraged from arising during
iterative reconstruction; additionally, because the iterative approach uses the
original projection data, it is able to recover information that is lost during
the NMAR process. We perform numerical experiments modeling a simple geometric
object, as well as several more realistic scenarios such as metal pins,
bilateral hip implants, and dental fillings placed within an anatomical
phantom. The proposed iterative algorithm is largely successful at eliminating
severe metal artifacts as well as secondary artifacts introduced by the NMAR
process, especially lost edges of bone structures in the neighborhood of the
metal regions.
