Maximum-likelihood expectation-maximization (ML-EM) method and multiplicative algebraic reconstruction technique (MART), which are well-known iterative image reconstruction algorithms, produce relatively highquality performance but each of which has an advantage and disadvantage. In this paper, in order to compensate for both disadvantages, we present a novel iterative algorithm constructed by a nonautonomous iterative system derived from the minimization of an α-skew Kullback–Leibler divergence, which is considered as a combined objective function for ML-EM and MART. We confirmed effectiveness of the proposed hybrid method through numerical experiments.
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