Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Journal: Medical physics
PMID: 39625126

Abstract

BACKGROUND: Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction.

Authors

  • Adrian Thummerer
    Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Lukas Schmidt
    GATE & Department of Economics, Ecole Normale Supérieure de Lyon, Lyon, France.
  • Jan Hofmaier
    Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
  • Stefanie Corradini
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Claus Belka
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Christopher Kurz
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.

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