Emission computed tomography reconstruction requires compensation for photon attenuation. The usual way to do this is by a previous transmission scan to reconstruct the attenuation map. An important improvement could be achieved if it is possible to retrieve the attenuation map directly from the activity data. Several approaches have been suggested before to do this by using iterative methods for solving maximum likelihood (ML) problems (or penalized maximum likelihood, MAP) that take into account the Poisson nature of the noise. One of the main drawbacks has been that these methods tend to retrieve solutions that generate an undesired crosstalk between the attenuation and the activity maps. In this paper we present a new approach to the problem in positron emission tomography that uses a minorization function algorithm applied to the Poisson likelihood function; this algorithm, together with an appropriate decreasing multiplicative factor and iterative data refinement tend to eliminate the crosstalk between the images.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Simultaneous activity attenuation reconstruction in positron emission tomography via maximum likelihood and iterative methods


    Beteiligte:
    De Pierro, A.R. (Autor:in) / Crepaldi, F. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    257780 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Positron Emission Tomography: Physical Models and Reconstruction Issues

    Ollinger, J. / IEEE; Signal Processing Society | British Library Conference Proceedings | 1994


    A maximum likelihood framework for iterative eigendecomposition

    Robles-Kelly, A. / Hancock, E.R. | IEEE | 2001


    Emission Spectral Tomography Reconstruction Based on Maximum Entropy Interpolation

    Xiong, W. / Jianglin, Y. | British Library Online Contents | 2009