Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study.

Journal: European radiology
Published Date: Aug 14, 2021

Abstract

OBJECTIVES: To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning-based image reconstruction algorithm (DLR, TrueFidelity™).

Authors

  • Hyunsu Choi
    Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro-173-beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
  • Won Chang
  • Jong Hyo Kim
    Interdisciplinary Program of Radiation Applied Life Science, Seoul National University College of Medicine.
  • Chulkyun Ahn
    Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
  • Heejin Lee
    Department of Applied bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
  • Hae Young Kim
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Jungheum Cho
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam.
  • Yoon Jin Lee
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Young Hoon Kim
    Department of Surgery, College of Medicine, Ulsan University, Asan Medical Center, Seoul, Korea.

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