Deep learning proton beam range estimation model for quality assurance based on two-dimensional scintillated light distributions in simulations.

Journal: Medical physics
Published Date: Nov 1, 2023

Abstract

BACKGROUND: Many studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects.

Authors

  • Eunho Lee
    Department of Brain and Cognitive Engineering, Korea University, Republic of Korea.
  • Byungchul Cho
    Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
  • Jungwon Kwak
    Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
  • Chiyoung Jeong
    Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
  • Min-Jae Park
    Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.
  • Sung-Woo Kim
    Physical Activity and Performance Institute, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Korea.
  • Si Yeol Song
    Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.
  • Youngmoon Goh
    Department of Radiation Oncology, Asan Medical Center, Seoul, South Korea.

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