Abstract: Objective: We propose a method to effectively suppress the effect of intracranial metal artifacts on postoperative review, a metal artifact correction algorithm based on prior imaging. Method: The CT image compensating for the intracranial metal artifact is obtained by adaptive filtering, threshold segmentation, and K mean clustering. In projecting the trajectory of metallic matter, the interpolation correction of the original image's projection data is obtained using the prior image as a reference, and the image is fused with the metal-containing material only image to produce the final corrected image. To verify the efficacy of this algorithm in suppressing CT metal artifacts, artifact correction was performed using CT images of simulated isolated metal artifacts and intracranial metal artifacts. Results: Compared with linear interpolation and the ADN algorithms, this algorithm achieves the highest structure similarity and peak signal-to-noise ratio and lowest root mean square error in corrected images. Two senior doctors subjectively scored this algorithm higher than the linear interpolation and ADN algorithms for removing CT metal artifacts. Conclusion: This algorithm effectively suppresses metal artifacts while preserving image edge information.