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.


    Access

    Access via TIB


    Export, share and cite



    Title :

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


    Contributors:

    Conference:

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


    Published in:

    Publication date :

    2021-05-24


    Size :

    6 pages





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Statistical approach to inverse problems in emission tomography with Poisson data

    Denisova, N. / Ruzankin, P. / Lim, Y. | American Institute of Physics | 2021

    Free access

    Image Reconstruction of Positron Emission Tomography Based on Wavelet-Packets Transform

    Zhaoxia, W. / Jingzhi, C. | British Library Online Contents | 2001