High-Density Object Removal from Projection Images using Low-Frequency-Based Object Masking

Abstract. High-density objects, like catheters, pacemakers or even contrast agent-filled vessels, cause characteristic streak artifacts in computed tomography (CT). Similar to metal artifacts, these streaks can be reduced by removing the dense object using segmentation and interpolation. First, we compare state-of-the-art interpolation methods like linear, spline and higher-order methods to the Healing Brush technique. Second, a new method is presented, that extracts a low-frequency model of the dense object and restores the decomposed X-ray intensity of the remaining tissue. This method is henceforth called Subtract-and-Shift. Compared to standard interpolation methods, it retains the measured structure that is superimposed and dominated by the dense object. The extracted structure is then used to replace the segmented pixel intensities of the object. The introduced method is compared to state-of-the-art interpolation methods using in-vivo data. First preliminary results show that Subtract-and-Shift can be superior to these interpolation methods.