Quantitative and Qualitative Evaluation of Convolutional Neural Networks with a Deeper U-Net for Sparse-View Computed Tomography Reconstruction.

Journal: Academic radiology
Published Date: Jul 4, 2019

Abstract

RATIONALE AND OBJECTIVES: To evaluate the utility of a convolutional neural network (CNN) with an increased number of contracting and expanding paths of U-net for sparse-view CT reconstruction.

Authors

  • Hirotsugu Nakai
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate, School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan. Electronic address: nakai.hirotsugu.33x@kyoto-u.jp.
  • Mizuho Nishio
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Rikiya Yamashita
    Artera, Inc., Los Altos, CA.
  • Ayako Ono
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate, School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
  • Kyoko Kameyama Nakao
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate, School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
  • Koji Fujimoto
    Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Kaori Togashi
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.

Keywords

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