Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2

Mikael Agn*, Claus Svarer, Vibe G. Frokjaer, Douglas N. Greve, Gitte M. Knudsen, Koen Van Leemput

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    This paper presents a mathematical model that regularizes dynamic PET data by using a Bayesian framework. We base the model on the well known two-parameter multilinear reference tissue method MRTM2 and regularize on the assumption that spatially close regions have similar parameters. The developed model is compared to the conventional approach of improving the low signal-to-noise ratio of PET data, i.e., spatial filtering of each time frame independently by a Gaussian kernel. We show that the model handles high levels of noise better than the conventional approach, while at the same time retaining a higher resolution. In addition, it results in a higher reliability between scans on individual subject data, measured by intraclass correlation for absolute agreement.

    Original languageEnglish
    Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
    PublisherIEEE
    Pages955-958
    Number of pages4
    ISBN (Electronic)9781467319591
    DOIs
    Publication statusPublished - 29 Jul 2014
    MoE publication typeA4 Article in a conference publication
    EventIEEE International Symposium on Biomedical Imaging - Beijing, China
    Duration: 29 Apr 20142 May 2014
    Conference number: 11

    Conference

    ConferenceIEEE International Symposium on Biomedical Imaging
    Abbreviated titleISBI
    CountryChina
    CityBeijing
    Period29/04/201402/05/2014

    Keywords

    • Bayesian modeling
    • Multilinear reference tissue method
    • Parametric imaging
    • PET
    • Regularization

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