Metal artefact reduction in digital tomosynthesis using component decomposition

We propose a technique for metal artefact reduction in digital tomosynthesis reconstruction. Although the problem was addressed earlier in the literature, we suggest another approach, which is, in our opinion, simpler, and easier to implement. It is a two-stage algorithm. At the first stage, attenuation images are segmented by decomposing their intensity distributions into gaussian-like components. Statistical information contained in each component is used for pixel classification. Components corresponding to metallic objects are identified, and a pixel threshold value separating regions occupied by metal objects from the rest of the image is found. Based on this value, at the second stage, a smooth mapping of image intensity is applied. This makes dense regions transparent, resulting in the artefact reduction in reconstruction. The methodology is demonstrated by several examples.