Purpose: 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, typically generated from some combination of interpolation along with other heuristics. 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 rationale is that the image produced by the iterative algorithm can inherit the desirable properties of the prior image, while also reducing secondary artifacts. Methods: 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 (PICCS). 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. Results: 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. In one case modeling severe photon starvation, the NMAR algorithm is found to provide better results. Conclusion: The proposed algorithm is effective in applying the superiorization methodology to the problem of MAR, providing better results than both NMAR and a purely total variation-based superiorization approach in nearly all cases.