It is well known that the respiratory motion during computed tomography (CT) causes artifacts that can mimic disease and could lead to mis-diagnosis. In this paper, we present an adaptive weighting scheme for motion artifacts suppression. The weighting scheme is based on the observation that the motion artifacts are caused by the inconsistency in the projection data set at the beginning and end of a scan. In general, the larger the discrepancy between the projections in these two segments, the more pronounced the artifacts. We make use of the redundant data samples in a CT scan and try to minimize the contributions of these views to the final image. By incorporating the information obtained from the external patient motion measurements, the amount of suppression can be tailored to the data set to achieve the best compromise between the patient motion artifacts and the image noise. This method has been applied to real patient scans and its advantages have been demonstrated.