Photon counting computed tomography (PCCT) acquires multiple images of different energy ranges from a single computed tomography (CT) scan. It provides us with spatial and spectral information not available from conventional CT, making it possible for further analysis in the metal artifacts reduction (MAR). This study aims to develop a method to reduce metal artifacts in PCCT using material decomposition. We use the normalized MAR (NMAR) method and calibration data to obtain the initial basis material images. We correct the image of soft tissue using the NMAR and then correct the image of hard tissue by performing least squares fitting with virtual monochromatic images (VMIs). The artifact-reduced material images are reverted to the bin-wise images (CT images for each energy bin) and then employed as the improved prior images for the NMAR method to obtain the final MAR results: The metal artifact-reduced bin-wise CT images. In simulation results, the proposed method showed promising results, reducing metal artifacts compared to the original NMAR method applied to bin-wise images. For example, it reduced the root mean squared error values by an average of 6.3% for a dual-energy case. The proposed method also reproduced noticeable improvements in the table-top PCCT experiments compared to the conventional NMAR.
