Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep-learning-based synthetic computed tomography generation technique.

Journal: Journal of applied clinical medical physics
Published Date: May 17, 2022

Abstract

PURPOSE: The objective of this study was to fabricate an anthropomorphic multimodality pelvic phantom to evaluate a deep-learning-based synthetic computed tomography (CT) algorithm for magnetic resonance (MR)-only radiotherapy.

Authors

  • Hyeongmin Jin
    Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea. Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea.
  • Sung Young Lee
    Division of Technology Business, National Institute for Nanomaterials Technology (NINT) Pohang University of Science and Technology (POSTECH) Pohang Republic of Korea.
  • Hyun Joon An
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Chang Heon Choi
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Eui Kyu Chie
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Hong-Gyun Wu
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea.
  • Jong Min Park
  • Sukwon Park
    Department of Radiation Oncology, Myongji Hospital, Goyang-si, Gyeonggi-do, Republic of Korea.
  • Jung-In Kim
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.

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