Blar i forfatter "Martin-Armas, Montserrat"

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    • Glioblastoma PET/MRI: kinetic investigation of [<sup>18</sup>F]rhPSMA-7.3, [<sup>18</sup>F]FET and [<sup>18</sup>F]fluciclovine in an orthotopic mouse model of cancer 

      Lindemann, Marcel; Oteiza, Ana; Martin-Armas, Montserrat; Guttormsen, Yngve; Moldes-Anaya, Angel; Berzaghi, Rodrigo; Bogsrud, Trond; Bach-Gansmo, Tore; Sundset, Rune; Kranz, Mathias (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-22)
      <b><p>Purpose</b> Glioblastoma multiforme (GBM) is the most common glioma and standard therapies can only slightly prolong the survival. Neo-vascularization is a potential target to image tumor microenvironment, as it defines its brain invasion. We investigate [<sup>18</sup>F]rhPSMA-7.3 with PET/MRI for quantitative imaging of neo-vascularization in GBM bearing mice and human tumor tissue and compare ...

    • Machine learning derived input-function in a dynamic 18F-FDG PET study of mice 

      Kuttner, Samuel; Wickstrøm, Kristoffer Knutsen; Kalda, Gustav; Dorraji, Seyed Esmaeil; Martin-Armas, Montserrat; Oteiza, Ana; Jenssen, Robert; Fenton, Kristin Andreassen; Sundset, Rune; Axelsson, Jan (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-01-13)
      Tracer kinetic modelling, based on dynamic <sup>18</sup>F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. Our aim was to explore two non-invasive machine learning-based models, for AIF prediction in a small-animal dynamic FDG PET study. 7 tissue ...