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.


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    Titel :

    Local statistical regularization method for solving image reconstruction problems in emission Tomography with Poisson data


    Beteiligte:
    Denisova, N. (Autor:in) / Kertész, H. (Autor:in) / Beyer, T. (Autor:in) / Fomin, Vasily (Herausgeber:in) / Shiplyuk, Alexander (Herausgeber:in)

    Kongress:

    INTERNATIONAL CONFERENCE ON THE METHODS OF AEROPHYSICAL RESEARCH (ICMAR 2020) ; 2020 ; Novosibirsk, Russia


    Erschienen in:

    Erscheinungsdatum :

    24.05.2021


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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