Digital Tomosynthesis (DT) is an x-ray limited-angle imaging technique. An accurate image reconstruction in
tomosynthesis is a challenging task due to the violation of the tomographic sufficiency conditions. A classical
"shift-and-add" algorithm (or simple backprojection) suffers from blurring artifacts, produced by structures
located above and below the plane of interest. The artifact problem becomes even more prominent in the presence
of materials and tissues with a high x-ray attenuation, such as bones, microcalcifications or metal. The focus of
the current work is on reduction of ghosting artifacts produced by bones in the musculoskeletal tomosynthesis.
A novel dissimilarity concept and a modified backprojection with an adaptive spatially dependent weighting
scheme (ωBP) are proposed. Simulated data of software phantom, a structured hardware phantom and a
human hand raw-data acquired with a Siemens Mammomat Inspiration tomosynthesis system were reconstructed
using conventional backprojection algorithm and the new ωBP-algorithm. The comparison of the results to the
non-weighted case demonstrates the potential of the proposed weighted backprojection to reduce the blurring
artifacts in musculoskeletal DT. The proposed weighting scheme is not limited to the tomosynthesis limitedangle
geometry. It can also be adapted for Computed Tomography (CT) and included in iterative reconstruction
algorithms (e.g. SART).
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