Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.

Journal: Japanese journal of radiology
Published Date: Sep 19, 2018

Abstract

PURPOSE: The confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences.

Authors

  • Tomoyuki Noguchi
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan. tnogucci@radiol.med.kyushu-u.ac.jp.
  • Daichi Higa
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
  • Takashi Asada
    Memory Clinics Ochanomizu, 4th floor, Ochanomizu Igaku Kaikan, 1-5-34, Yushima, Bunkyo-ku, Tokyo, 113-0034, Japan.
  • Yusuke Kawata
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
  • Akihiro Machitori
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
  • Yoshitaka Shida
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
  • Takashi Okafuji
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
  • Kota Yokoyama
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
  • Fumiya Uchiyama
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.
  • Tsuyoshi Tajima
    Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan.

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