Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 38403025

Abstract

BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available - fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm - Deep Match.

Authors

  • Di Xu
    School of Chemistry and Chemical Engineering, Chongqing University of Science & Technology, Chongqing, 401331, China. xdcq86@163.com.
  • Martina Descovich
    UCSF Department of Radiation Oncology, San Francisco, California 94115.
  • Hengjie Liu
    Radiation Oncology, University of California, Los Angeles, United States.
  • Yi Lao
    Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, United States of America.
  • Alexander R Gottschalk
    Department of Radiation Oncology, University of California at San Francisco, San Francisco, California.
  • Ke Sheng
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.

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