CLIMAR: classified linear interpolation based metal artifact reduction for severe metal artifact reduction in x-ray CT imaging

Abstract In x-ray CT imaging, the existence of metal in the imaging field of view deteriorates the quality of the reconstructed image. This is because rays penetrating dense metal implants are highly corrupted, causing huge inconsistency between projection data. The result appears as strong artifacts such as black and white streaks on the reconstructed image disturbing correct diagnosis. For several decades, there have been various trials to reduce metal artifacts for better image quality. As the computing power of computer processors became more powerful, more complex algorithms with improved performance have been introduced. For instance, the initially developed metal artifact reduction (MAR) algorithms based on simple sinogram interpolation were combined with computationally expensive iterative reconstruction techniques to pursue better image quality. Recently, even machine learning based techniques have been introduced, which require huge amounts of computations for training. In this paper, we introduce an image based novel MAR algorithm in which severe metal artifacts such as black shadings are detected by the proposed method in a straightforward manner based on a linear interpolation. To do that, a new concept of metal artifact classification is devised using linear interpolation in the virtual projection domain. The proposed method reduces severe artifacts very quickly and effectively and has good performance to keep the detailed body structure preserved. Results of qualitative and quantitative comparisons with other representative algorithms such as LIMAR and NMAR support the excellence of the proposed algorithm. Thanks to the nature of reducing artifacts in the image itself and its low computational cost, the proposed algorithm can function as an initial image generator for other MAR algorithms, as well as being integrated in the modalities under limited computation power such as mobile CT scanners.