Metal Artifact Correction Algorithm for CT

The presence of high density objects leads to significant artifacts in CT images. These artifacts impact the quantitative as well as qualitative accuracy of CT images. These artifacts are caused due to factors such as beam hardening, scatter, photon starvation. The ramp filter prior to standard back-projection enhances some of the artifacts. The artifacts can be reduced by using better data acquisition such as dual energy imaging, higher kVp imaging. Several software based techniques have been proposed to reduce metal artifacts that can be classified into model-based algorithms and sinogram in-painting methods. We propose an improved metal artifact correction algorithm that belongs to the category of sinogram inpainting. In the prior art, the prior images used to generate the inpainted data is created by segmenting the original or the first pass metal artifact reduced (MAR) images. We propose a multi-band filter design to generate the prior image. The original image and the first pass MAR image possess complimentary information and are combined using a multi-band filter. The combined image is then segmented to generate the final prior image. It is shown that the new approach leads to a prior that is more consistent to the original image compared to the conventional prior and hence improved inpainted data. The proposed approach is demonstrated to be superior to the conventional approach using clinical datasets. We further compare two different inpainting algorithms to replace the original corrupted sinogram samples with the forward projection of the prior, also defined as the prior data. The first approach is based on the linear baseline shift algorithm, while in the second approach the replacement step used in the normalized metal artifact correction algorithm (NMAR) is used. Both the approaches are validated using both phantom and clinical data and is demonstrated to be superior to standard interpolation based techniques.