Deep learning-based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study.

Journal: European radiology
Published Date: Apr 22, 2021

Abstract

OBJECTIVES: (1) To compare low-contrast detectability of a deep learning-based denoising algorithm (DLA) with ADMIRE and FBP, and (2) to compare image quality parameters of DLA with those of reconstruction methods from two different CT vendors (ADMIRE, IMR, and FBP).

Authors

  • Youngjune Kim
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam.
  • Dong Yul Oh
    Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea.
  • Won Chang
  • Eunhee Kang
  • Jong Chul Ye
  • Kyeorye Lee
    Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.
  • Hae Young Kim
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Young Hoon Kim
    Department of Surgery, College of Medicine, Ulsan University, Asan Medical Center, Seoul, Korea.
  • Ji Hoon Park
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Yoon Jin Lee
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Kyoung Ho Lee
    Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

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