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    High accuracy multiple scatter modelling for 3D whole body PET.

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    Authors
    Markiewicz, P J
    Tamal, M
    Julyan, Peter J
    Hastings, David L
    Reader, Andrew J
    Affiliation
    School of Chemical Engineering and Analytical Science, The University of Manchester, Faraday Building, Sackville Street, Manchester M60 1QD, UK. p.markiewicz@postgrad.manchester.ac.uk
    Issue Date
    2007-02-07
    
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    Abstract
    A new technique for modelling multiple-order Compton scatter which uses the absolute probabilities relating the image space to the projection space in 3D whole body PET is presented. The details considered in this work give a valuable insight into the scatter problem, particularly for multiple scatter. Such modelling is advantageous for large attenuating media where scatter is a dominant component of the measured data, and where multiple scatter may dominate the total scatter depending on the energy threshold and object size. The model offers distinct features setting it apart from previous research: (1) specification of the scatter distribution for each voxel based on the transmission data, the physics of Compton scattering and the specification of a given PET system; (2) independence from the true activity distribution; (3) in principle no scaling or iterative process is required to find the distribution; (4) explicit multiple scatter modelling; (5) no scatter subtraction or addition to the forward model when included in the system matrix used with statistical image reconstruction methods; (6) adaptability to many different scatter compensation methods from simple and fast to more sophisticated and therefore slower methods; (7) accuracy equivalent to that of a Monte Carlo model. The scatter model has been validated using Monte Carlo simulation (SimSET).
    Citation
    High accuracy multiple scatter modelling for 3D whole body PET. 2007, 52 (3):829-47 Phys Med Biol
    Journal
    Physics in Medicine and Biology
    URI
    http://hdl.handle.net/10541/70048
    DOI
    10.1088/0031-9155/52/3/021
    PubMed ID
    17228124
    Type
    Article
    Language
    en
    ISSN
    0031-9155
    ae974a485f413a2113503eed53cd6c53
    10.1088/0031-9155/52/3/021
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