Metal artifacts reduction for tomosynthesis

Abstract

In tomosynthesis imaging, two kinds of metal artifacts will influence diagnosis: undershooting and ripple. In this paper we propose a novel metal artifact reduction (MAR) algorithm to reduce the both of these effects. First, the raw projection data are analyzed and metal areas are identified through segmentation. Then the metal areas are filled with an interpolated value based on the neighborhood background (non-segmented) pixels. The filled regions and metal regions are then reconstructed separately with Filtered Backprojection(FBP). Lastly, the two reconstruction results are combined together to get the final artifacts-free images. Phantom and clinical images are evaluated using qualitative and quantitative methods which demonstrate the algorithms effectiveness.