Metal Artifact Reduction in CT Images through Sinogram Data Inpainting

Abstract

When metallic implants are present within the human body, they frequently introduce metallic artifacts into X-ray CT images. These artifacts can lead to significant distortions, obscuring critical information and potentially degrading the quality of the CT images, thereby impacting diagnostic accuracy for clinicians. In recent years, there has been extensive research aimed at mitigating the challenges posed by metallic artifacts, resulting in the development of multiple solutions to address this issue. In this study, we present an efficient approach for artifact removal. Our method involves utilizing the image reconstructed from a sinogram affected by artifacts to generate a synthesized sinogram, deviating from the conventional acquisition of sinogram data. The key stages of our approach encompass segmentation, sinogram gap-filling, and subsequent image enhancement. To achieve rapid segmentation, we employed a K-means classification method. For the retrieval of missing data, we utilized an interpolation algorithm based on a penalized least squares method. In the final phase of image reconstruction enhancement, we implemented an advanced contrast equalization technique to restore image intensities to their inherent dynamic range. Through rigorous verification using both simulated and clinical data, our method consistently demonstrates a remarkable improvement in image quality.