Conversion of single-energy CT to parametric maps of dual-energy CT using convolutional neural network.

Journal: The British journal of radiology
Published Date: May 29, 2024

Abstract

OBJECTIVES: We propose a deep learning (DL) multitask learning framework using convolutional neural network for a direct conversion of single-energy CT (SECT) to 3 different parametric maps of dual-energy CT (DECT): virtual-monochromatic image (VMI), effective atomic number (EAN), and relative electron density (RED).

Authors

  • Sangwook Kim
    School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.
  • Jimin Lee
    Department of Nuclear Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
  • Jungye Kim
    Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
  • Bitbyeol Kim
    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.
  • Seongmoon Jung
    Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.

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