Blar i forfatter "Axelsson, Jan"

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    • Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function 

      Kuttner, Samuel; Wickstrøm, Kristoffer Knutsen; Lubberink, Mark; Tolf, Andreas; Burman, Joachim; Sundset, Rune; Jenssen, Robert; Appel, Lieuwe; Axelsson, Jan (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-08)
      <p>Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of <sup>15</sup>O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning ...

    • 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 ...