Blar i NTNU Open på forfatter "Gurney-Champion, Oliver J."

Viser treff 1-3 av 3

    • Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients 

      Kaandorp, Misha Pieter Thijs; Barbieri, Sebastiano; Klaassen, Remy; van Laarhoven, Hanneke W. M.; Crezee, Hans; While, Peter Thomas; Nederveen, Aart J.; Gurney-Champion, Oliver J. (Peer reviewed; Journal article, 2021)
      Purpose Earlier work showed that IVIM-NETorig, an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to ...

    • Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy 

      Gurney-Champion, Oliver J.; Landry, Guillaume; Redalen, Kathrine; Thorwarth, Daniela (Peer reviewed; Journal article, 2022)
      Quantitative magnetic resonance imaging (qMRI) has been shown to provide many potential advantages for personalized adaptive radiotherapy (RT). Deep learning models have proven to increase efficiency, robustness and speed ...

    • Quantitative imaging for radiotherapy purposes 

      Gurney-Champion, Oliver J.; Mahmood, Faisal; van Schie, Marcel; Julian, Robert; George, Ben; Philippens, Marielle E.P.; van der Heide, Uulke A.; Thorwarth, Daniela; Redalen, Kathrine (Peer reviewed; Journal article, 2020)
      Quantitative imaging biomarkers show great potential for use in radiotherapy. Quantitative images based on microscopic tissue properties and tissue function can be used to improve contouring of the radiotherapy targets. ...