Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution.

Journal: Journal of applied clinical medical physics
Published Date: Jun 1, 2023

Abstract

PURPOSE: Deep learning-based virtual patient-specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning-based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed.

Authors

  • Ryota Tozuka
    Department of Radiology, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, 409-3898, Japan.
  • Noriyuki Kadoya
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan. kadoya.n@rad.med.tohoku.ac.jp.
  • Seiji Tomori
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Yuto Kimura
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura, Japan.
  • Tomohiro Kajikawa
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Yuto Sugai
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Yushan Xiao
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
  • Keiichi Jingu
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.

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