Deep learning-based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis.

Journal: European radiology
Published Date: Apr 5, 2022

Abstract

OBJECTIVES: To evaluate the diagnostic value of deep learning model (DLM) reconstructed dual-energy CT (DECT) low-keV virtual monoenergetic imaging (VMI) for assessing hypoenhancing hepatic metastases.

Authors

  • Taehee Lee
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
  • Jeong Min Lee
  • Jeong Hee Yoon
  • Ijin Joo
    Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Jae Seok Bae
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
  • Jeongin Yoo
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, 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.
  • 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.

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