CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping power maps for the treatment. However, the presence of metal implants introduces severe striking artifacts in the reconstructed results, affecting the diagnostic accuracy and treatment performance. In order to reduce the metal artifact in DECT, we introduce a metal artifact reduction scheme for iterative DECT algorithms. The corrupt data is substituted with an estimation in each iteration. We utilize normalized metal artifact reduction (NMAR) composed with image domain decomposition to initialize the algorithm and speed up the convergence. A joint statistic DECT algorithm, dual-energy alternating minimization (DEAM), with the proposed scheme is tested on experimental and clinical data acquired on a Philips Big Bore scanner. We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without O-MAR. The visualization and quantitative analysis show that DEAM with the proposed method has the best performance in reducing striking artifacts caused by metallic objects.