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    Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model.

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    Authors
    Walker, M D
    Asselin, M-C
    Julyan, Peter J
    Feldmann, M
    Talbot, P S
    Jones, T
    Matthews, J C
    Affiliation
    School of Cancer and Enabling Sciences, Wolfson Molecular Imaging Centre, MAHSC, The University of Manchester, Manchester, UK.
    Issue Date
    2011-02-21
    
    Metadata
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    Abstract
    Iterative image reconstruction methods such as ordered-subset expectation maximization (OSEM) are widely used in PET. Reconstructions via OSEM are however reported to be biased for low-count data. We investigated this and considered the impact for dynamic PET. Patient listmode data were acquired in [(11)C]DASB and [(15)O]H(2)O scans on the HRRT brain PET scanner. These data were subsampled to create many independent, low-count replicates. The data were reconstructed and the images from low-count data were compared to the high-count originals (from the same reconstruction method). This comparison enabled low-statistics bias to be calculated for the given reconstruction, as a function of the noise-equivalent counts (NEC). Two iterative reconstruction methods were tested, one with and one without an image-based resolution model (RM). Significant bias was observed when reconstructing data of low statistical quality, for both subsampled human and simulated data. For human data, this bias was substantially reduced by including a RM. For [(11)C]DASB the low-statistics bias in the caudate head at 1.7 M NEC (approx. 30 s) was -5.5% and -13% with and without RM, respectively. We predicted biases in the binding potential of -4% and -10%. For quantification of cerebral blood flow for the whole-brain grey- or white-matter, using [(15)O]H(2)O and the PET autoradiographic method, a low-statistics bias of <2.5% and <4% was predicted for reconstruction with and without the RM. The use of a resolution model reduces low-statistics bias and can hence be beneficial for quantitative dynamic PET.
    Citation
    Bias in iterative reconstruction of low-statistics PET data: benefits of a resolution model. 2011, 56 (4):931-49 Phys Med Biol
    Journal
    Physics in Medicine and Biology
    URI
    http://hdl.handle.net/10541/201092
    http://hdl.handle.net/10541/201087
    DOI
    10.1088/0031-9155/56/4/004
    PubMed ID
    21248391
    Type
    Article
    Language
    en
    ISSN
    1361-6560
    ae974a485f413a2113503eed53cd6c53
    10.1088/0031-9155/56/4/004
    Scopus Count
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    All Christie Publications
    All Christie Publications
    Christie Medical Physics and Engineering Research

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