Abstract
Statistical iterative methods for image reconstruction like maximum likelihood expectation maximization are more vigorous and feasible than analytical inversion methods and allow for accurately modelling the counting statistics and the photon transport during acquisition. The maximum likelihood approach provides images with superior noise characteristics compared to conventional algorithms, but it has huge computation burden. The objection of this paper is to develop a new algorithm, which modifies the number of projections and the step size for each iteration in order to recover various frequency components. In the present method, the number of projections in a subset increases and the step size decreases when all the subsets have been processed. In addition, the pixel data are divided into subsets to accelerate image reconstruction. The experimental results demonstrate that this algorithm converge faster than any other reconstruction algorithms and can provide high quality of reconstructed images.
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