Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

Journal: Medical physics
Published Date: Dec 1, 2019

Abstract

PURPOSE/OBJECTIVE(S): Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters in addition to the presence of noise, machine learning approaches were proposed to establish their relationship.

Authors

  • Zhongxing Li
  • Yiang Wang
    Department of Medical Physics, Wuhan University, Wuhan, 430072, China.
  • Yajun Yu
    Department of Medical Physics, Wuhan University, Wuhan, 430072, China.
  • Kuanjun Fan
    School of Electrical Engineering, Huazhong University of Science & Technology, Wuhan, 430074, China.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Hao Peng
    Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong, P. R. China.

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