Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison.

Journal: European radiology
Published Date: Jun 29, 2020

Abstract

OBJECTIVE: This study determined the effect of dose reduction and kernel selection on quantifying emphysema using low-dose computed tomography (LDCT) and evaluated the efficiency of a deep learning-based kernel conversion technique in normalizing kernels for emphysema quantification.

Authors

  • So Hyeon Bak
    Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Republic of Korea.
  • Jong Hyo Kim
    Interdisciplinary Program of Radiation Applied Life Science, Seoul National University College of Medicine.
  • Hyeongmin Jin
    Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea. Department of Radiation Oncology, Seoul National University Hospital, Seoul 03080, Republic of Korea.
  • Sung Ok Kwon
    Biomedical Research Institute, Kangwon National University Hospital, Chuncheon, Republic of Korea.
  • Bom Kim
    Environmental Health Center, Kangwon National University Hospital, Chuncheon, Republic of Korea.
  • Yoon Ki Cha
    Department of Radiology, Dongguk University Ilsan Hospital, Goyang, Republic of Korea.
  • Woo Jin Kim
    The Heart Center of Chonnam National University Hospital, 42 Jaebongro, Dong-gu, Gwangju 501-757, South Korea.

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