• DocumentCode
    3684404
  • Title

    Non-invasive quantification of brain [18F]-FDG uptake by combining medical health records and dynamic PET imaging data

  • Author

    Elisa Roccia;Arthur Mikhno;Francesca Zanderigo;Elsa D. Angelini;R. Todd Ogden;J. John Mann;Andrew F. Laine

  • Author_Institution
    Department of Information Engineering, University of Padova, ITALY
  • fYear
    2015
  • Firstpage
    2243
  • Lastpage
    2246
  • Abstract
    Quantification of regional cerebral metabolic rate of glucose (rCMRglu) via positron emission tomography (PET) imaging requires measuring the arterial input function (AIF) via invasive arterial blood sampling. In this study we describe a non-invasive approach, the non-invasive simultaneous estimation (nSIME), for the estimation of rCMRglu that considers a pharmacokinetic input function model and constraints derived from machine learning applied to a fusion of individual medical health records and dynamic [18F]-FDG-PET brain images data. The results obtained with our data indicate potential for future clinical application of nSIME, with correlation measures of 0.87 for rCMRglu compared to quantification with full arterial blood sampling.
  • Keywords
    "Blood","Positron emission tomography","Predictive models","Sugar","Estimation","Kinetic theory","Biomedical imaging"
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
  • ISSN
    1094-687X
  • Electronic_ISBN
    1558-4615
  • Type

    conf

  • DOI
    10.1109/EMBC.2015.7318838
  • Filename
    7318838
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3684404