Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.

Journal: Tomography (Ann Arbor, Mich.)
Published Date: Dec 18, 2024

Abstract

OBJECTIVES: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).

Authors

  • Yusuke Inoue
    Department of Diagnostic Radiology, Kitasato University School of Medicine, Sagamihara 252-0374, Japan.
  • Hiroyasu Itoh
    Department of Radiology, Kitasato University Hospital, Sagamihara 252-0375, Japan.
  • Hirofumi Hata
    Department of Radiology, Kitasato University Hospital, Sagamihara 252-0375, Japan.
  • Hiroki Miyatake
    Department of Radiology, Kitasato University Hospital, Sagamihara 252-0375, Japan.
  • Kohei Mitsui
    Department of Diagnostic Radiology, Kitasato University School of Medicine, Sagamihara 252-0374, Japan.
  • Shunichi Uehara
    Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
  • Chisaki Masuda
    Department of Diagnostic Radiology, Kitasato University School of Medicine, Sagamihara 252-0374, Japan.

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