Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning.

Journal: Physics and imaging in radiation oncology
Published Date: May 28, 2025

Abstract

BACKGROUND AND PURPOSE: Deep learning-based tumor tracking is promising for real-time magnetic-resonance-imaging (MRI)-guided radiotherapy. We investigate the applicability of a tumor tracking model developed for 0.35 T MRI-linac sagittal cine-MRI for 1.5 T interleaved orthogonal cine-MRI and implement transfer learning to further improve its performance.

Authors

  • Yiling Wang
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany; Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China.
  • Elia Lombardo
    Department of Radiation Oncology, University Hospital, LMU Munich, Munich, 81377, Germany.
  • Jie Wang
  • Yu Fan
    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA. YFan1@mdanderson.org.
  • Yue Zhao
    The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.
  • 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.
  • Marco Riboldi
    Department of Medical Physics, Ludwig-Maximilians-Universität München, Germany.
  • Christopher Kurz
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.
  • Guillaume Landry
    Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching bei München, 85748, Germany.

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