Hybrid algorithm of maximum-likelihood expectation-maximization and multiplicative algebraic reconstruction technique for iterative tomographic image reconstruction

Maximum-likelihood expectation-maximization (ML-EM) method and multiplicative algebraic reconstruction technique (MART), which are well-known iterative image reconstruction algorithms, produce relatively high quality 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 a-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.