Deep learning-based Intraoperative MRI reconstruction.

Journal: European radiology experimental
PMID:

Abstract

BACKGROUND: We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery.

Authors

  • Jon André Ottesen
    Computational Radiology & Artificial Intelligence (CRAI) Unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway. jon.a.ottesen@gmail.com.
  • Tryggve Storas
    Division of Radiology and Nuclear Medicine, Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway.
  • Svein Are Sirirud Vatnehol
    The Intervention Centre, Oslo University Hospital, Oslo, Norway.
  • Grethe Løvland
    The Intervention Centre, Oslo University Hospital, Oslo, Norway.
  • Einar Osland Vik-Mo
    Vilhelm Magnus Laboratory, Department of Neurosurgery, Oslo University Hospital, Oslo, Norway.
  • Till Schellhorn
    Computational Radiology & Artificial Intelligence unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, 0372, Oslo, Norway.
  • Karoline Skogen
    Computational Radiology & Artificial Intelligence (CRAI) Research Group, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
  • Christopher Larsson
    Computational Radiology & Artificial Intelligence (CRAI) Research Group, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
  • Atle Bjornerud
  • Inge Rasmus Groote-Eindbaas
    Computational Radiology & Artificial Intelligence (CRAI) Research Group, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
  • Matthan W A Caan
    Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands.