Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy.

Journal: Medical physics
PMID: 38507259

Abstract

BACKGROUND: In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is essential.

Authors

  • Yabo Fu
    Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA.
  • Pengpeng Zhang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA. Electronic address: zhangp@mskcc.org.
  • Qiyong Fan
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • Weixing Cai
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • Hai Pham
    School of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
  • Andreas Rimner
    Memorial Sloan Kettering Cancer Center, New York, New York.
  • John Cuaron
    Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
  • Laura Cervino
    Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037.
  • Jean M Moran
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Tianfang Li
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.

Keywords

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