Optimization of the structured illumination series for compressive x-ray tomosynthesis

Abstract

Compressive x-ray tomosynthesis (CXT) uses a set of encoded projection measurements from different incident angles to reconstruct the object under inspection. We consider the variable motion of objects on a conveyor mechanism and establish an imaging model based on the sensing geometry of a dynamic CXT system. Then, a numerical algorithm is proposed to optimize the structured illumination series to improve reconstruction accuracy with reduced radiation dose. Compared with the state-of-the-art method, the proposed strategy increases the degrees of optimization freedom by jointly optimizing the coding mask patterns, locations of x-ray sources, and exposure moments in the CXT system, thus obtaining better reconstruction performance. A genetic algorithm is applied to achieve the optimization results. It shows that the proposed method outperforms the traditional CXT approach by further improving reconstruction performance under comparable radiation dose.