A generic statistical approach for emission computed tomography reconstruction

Nowadays nuclear imaging is increasingly used for non-invasive diagnosis. The image modalities in nuclear imaging suffer of worse statistics, in comparison with computed tomography, since they are based on emission transition tomography. Thus, precise reconstruction methods that can deal with incomplete or missing measurements are needed in order to improve the quality of nuclear images. In this paper we present a generalization of the state of the art EMML and ISRA algorithms for emission computed tomography reconstruction. The proposed method was tested and validated in comparison with the mentioned state of the art methods on a set of synthetic data. Better results (in terms of speed of convergence) were obtained for certain parameter settings.