The aim of this study was to assess the ability of metal artifact reduction (MAR) algorithm in restoring the CT image quality while correcting the tissue density information for the accurate estimation of the absorbed dose. A phantom filled with titanium (low-Z metal) and Cerrobend (high-Z metal) inserts was used for this purpose. The MAR algorithm was applied to phantom's CT dataset. Static intensity-modulated radiation therapy (IMRT) plans, including five beam angles, were designed and optimized on the uncorrected images to deliver 10 Gy on the simulated target. Monte Carlo dose calculation was computed on uncorrected, corrected, and ground truth image datasets. It was firstly verified that MAR methodology was able to correct HU errors due to the metal presence. In the worst situation (high-Z phantom), the image difference, uncorrected ground truth and corrected ground truth, went from -4.4 ± 118.8 HU to 0.4 ± 10.8 HU, respectively. Secondly, it was observed that the impact of dose errors estimation depends on the atomic number of the metal: low-Z inserts do not produce significant dose inaccuracies, while high-Z implants substantially influence the computation of the absorbed dose. In this latter case, dose errors in the PTV region were up to 23.56% (9.72% mean value) when comparing the uncorrected vs. the ground truth dataset. After MAR correction, errors dropped to 0.11% (0.10% mean value). In conclusion, it was assessed that the new MAR algorithm is able to restore image quality without distorting mass density information, thus producing a more accurate dose estimation.