Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jul 1, 2022

Abstract

PURPOSE: Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges estimated from measured images were deviated because of limited spatial resolutions of the developed x-ray camera as well as statistical noise in the images. To solve these problems, we proposed a method for predicting high-resolution dose images from SEB images with various count level using a deep learning (DL) approach for range and width verification.

Authors

  • Takuya Yabe
    Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
  • Mitsutaka Yamaguchi
    Takasaki Advanced Radiation Research Institute, Quantum Beam Science Research Directorate, National Institutes for Quantum and Radiological Science (QST), Takasaki, Japan.
  • Chih-Chieh Liu
    Department of Biomedical Engineering, University of California, Davis, CA, United States of America.
  • Toshiyuki Toshito
    Department of Proton Therapy Physics, Nagoya Proton Therapy Center, Nagoya City University West Medical Center, Aichi, Japan.
  • Naoki Kawachi
    Takasaki Advanced Radiation Research Institute, Quantum Beam Science Research Directorate, National Institutes for Quantum and Radiological Science (QST), Takasaki, Japan.
  • Seiichi Yamamoto
    Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.

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