In the present paper, a method of local statistical regularization is studied for solving image reconstruction problems in emission tomography with Poisson data. A priori model based on the properties of the object under study is developed. A new reconstruction algorithm MAP-KL based on the Bayesian Maximum a Posteriori (MAP) approach with the a priori probability density defined by the Kullback-Leibler (KL) divergence is proposed. To study the developed local regularization method and to compare the MAP-KL algorithm with the standard maximum likelihood approach, computer simulation of SPECT liver imaging was performed.
Local statistical regularization method for solving image reconstruction problems in emission Tomography with Poisson data
INTERNATIONAL CONFERENCE ON THE METHODS OF AEROPHYSICAL RESEARCH (ICMAR 2020) ; 2020 ; Novosibirsk, Russia
AIP Conference Proceedings ; 2351 , 1
2021-05-24
6 pages
Conference paper
Electronic Resource
English
Statistical approach to inverse problems in emission tomography with Poisson data
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