Efficient and accurate commissioning and quality assurance of radiosurgery beam via prior-embedded implicit neural representation learning.

Journal: Medical physics
PMID: 39812551

Abstract

BACKGROUND: Dosimetric commissioning and quality assurance (QA) for linear accelerators (LINACs) present a significant challenge for clinical physicists due to the high measurement workload and stringent precision standards. This challenge is exacerbated for radiosurgery LINACs because of increased measurement uncertainty and more demanding setup accuracy for small-field beams. Optimizing physicists' effort during beam measurements while ensuring the quality of the measured data is crucial for clinical efficiency and patient safety.

Authors

  • Lianli Liu
    Department of Radiation Oncology, Stanford University, Palo Alto, California, USA.
  • Cynthia Chang
    Department of Radiation Oncology, Stanford University, Palo Alto, California, USA.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Xuejun Gu
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
  • Gregory Szalkowski
    Department of Radiation Oncology, Stanford University, Palo Alto, California, USA.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.

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