NON-LINEAR 3D RECONSTRUCTION FOR COMPRESSIVE X-RAY TOMOSYNTHESIS
Qile Zhao, Xu Ma, Angela Cuadros, Gonzalo R. Arce, Rui Chen
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Compressive X-ray tomosynthesis is an emerging technology that can be used for medical diagnosis, safety inspection and industrial non-destructive testing. Traditional compressive X-ray tomosynthesis uses sequential illumination to interrogate the object of interest, which generates non-overlapping projection measurements. However, when multiple X-ray sources emit simultaneously, the projections corresponding to different sources overlap creating multiplexed measurements. Such measurement strategy leads to a non-linear reconstruction problem. The reconstruction of the three-dimensional (3D) object from multiplexed projections is an important problem. This paper proposes a non-linear 3D reconstruction approach for compressive X-ray tomosynthesis, where a set of coding masks are used to generate structured illumination and reduce radiation dose. The effectiveness of the method is verified by simulation experiments. It is shown that the proposed approach can reconstruct high-quality images with just a few snapshots.