Arm artifact, which is type of streak artifact frequently observed in computed tomography (CT) images of polytrauma patients at the arms-down positioning, are known to degrade the image quality. The existing streak artifact reduction algorithms are not effective for arm artifact, as they have not been designed for this purpose. The effects of the latest iterative reconstruction techniques (IRs), which are effective for noise and streak artifact reductions, have not been evaluated for the arm-artifact reduction. In this study, we developed a novel reconstruction algorithm for arm-artifact reduction using an arm-induced noise filtering of the projection data. A phantom resembling a standard adult abdomen with two arms was scanned using a 16-row CT scanner, and then the projection data was downloaded. The proposed algorithm consisted of an arm recognition step after the conventional reconstruction and arm-induced noise filtering (frequency split and attenuation-dependent filtering) of the projection data. The artifact reduction capabilities and image blurring as a side effect of the filtering were compared with those of the latest three IRs (IR1, IR2, and IR3). The proposed algorithm and IR1 significantly reduced the artifacts by 89.4% and 83.5%, respectively. The other two IRs were not effective in terms of arm-artifact reduction. In contrast to IR1 that yielded an apparent image blurring combined with a different noise texture, the proposed algorithm mostly suppressed the image blurring. The proposed algorithm, designed for an arm-artifact reduction, was effective and it is expected to improve the image quality of abdominal CT examinations at the arms-down positioning.