Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions.

Journal: European radiology
Published Date: Nov 25, 2021

Abstract

OBJECTIVES: To compare the overall image quality and detectability of significant (malignant and pre-malignant) liver lesions of low-dose liver CT (LDCT, 33.3% dose) using deep learning denoising (DLD) to standard-dose CT (SDCT, 100% dose) using model-based iterative reconstruction (MBIR).

Authors

  • Sungeun Park
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Jeong Hee Yoon
  • Ijin Joo
    Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Mi Hye Yu
    Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea.
  • Jae Hyun Kim
    Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66047; Macromolecule and Vaccine Stabilization Center, University of Kansas, Lawrence, Kansas 66047.
  • Junghoan Park
    Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
  • Se Woo Kim
    Department of Radiology, Seoul National University Hospital, Seoul, Korea.
  • Seungchul Han
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Chulkyun Ahn
    Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
  • Jong Hyo Kim
    Interdisciplinary Program of Radiation Applied Life Science, Seoul National University College of Medicine.
  • Jeong Min Lee

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