Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy.

Journal: International journal of radiation oncology, biology, physics
Published Date:

Abstract

PURPOSE: We propose a tumor tracking framework for 2D cine magnetic resonance imaging (MRI) based on a pair of deep learning (DL) models relying on patient-specific (PS) training.

Authors

  • Elia Lombardo
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Laura Velezmoro
    Department of Radiation Oncology, LMU University Hospital, LMU Munich.
  • Sebastian N Marschner
    Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
  • Moritz Rabe
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, Germany.
  • Claudia Tejero
    Department of Radiation Oncology, LMU University Hospital, LMU Munich.
  • Christianna I Papadopoulou
    Department of Radiation Oncology, LMU University Hospital, LMU Munich.
  • Zhuojie Sui
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching, Germany.
  • Michael Reiner
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich 81377, 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.
  • Christopher Kurz
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Marco Riboldi
    Department of Medical Physics, Ludwig-Maximilians-Universität München, Germany.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.