Feasibility of two-dimensional dose distribution deconvolution using convolution neural networks.

Journal: Medical physics
Published Date: Nov 6, 2019

Abstract

PURPOSE: The purpose of this study was to investigate the feasibility of two-dimensional (2D) dose distribution deconvolution using convolutional neural networks (CNNs) instead of an analytical approach for an in-house scintillation detector that has a detector-interface artifact in the penumbra region.

Authors

  • Wonjoong Cheon
    Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea.
  • Sung Jin Kim
    Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Kyuseok Kim
    College of Medicine, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea. Electronic address: dreinstein70@gmail.com.
  • Moonhee Lee
    Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea.
  • Jinhyeop Lee
    Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Korea.
  • Kwanghyun Jo
    Department of Radiation Oncology, Samsung Medical Center, Seoul, 06351, Korea.
  • Sungkoo Cho
    Department of Radiation Oncology, Samsung Medical Center, Seoul, 06351, Korea.
  • Hyosung Cho
    Department of Radiation Convergence Engineering, Yonsei University, Wonju, 26493, Korea.
  • Youngyih Han
    Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul 06351, Republic of Korea. Electronic address: youngyih@skku.edu.

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