Deep learning-based reconstruction in ultra-high-resolution computed tomography: Can image noise caused by high definition detector and the miniaturization of matrix element size be improved?

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jan 13, 2021

Abstract

PURPOSE: This study aimed to assess the noise characteristics of ultra-high-resolution computed tomography (UHRCT) with deep learning-based reconstruction (DLR).

Authors

  • Atsushi Urikura
    Division of Diagnostic Radiology, Shizuoka Cancer Center 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka 411-8777, Japan. Electronic address: at.urikura@scchr.jp.
  • Tsukasa Yoshida
    Division of Diagnostic Radiology, Shizuoka Cancer Center 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka 411-8777, Japan. Electronic address: ts.yoshida@scchr.jp.
  • Yoshihiro Nakaya
    Vocational School Toyo Public Health Academy, 6-21-7 Honmachi, Shibuya-ku, Tokyo 151-0071, Japan. Electronic address: bskfc464@ybb.ne.jp.
  • Eiji Nishimaru
    Department of Radiology, Hiroshima University Hospital, 1-2-3, Kasumi, Minami-ku, Hiroshima 734-8551, Japan. Electronic address: eiji2403@tk9.so-net.ne.jp.
  • Takanori Hara
    Department of Medical Technology, Nakatsugawa Municipal General Hospital, 1522-1 Komanba, Nakatsugawa, Gifu 508-0011, Japan. Electronic address: hara_tnk2@ybb.ne.jp.
  • Masahiro Endo
    Division of Diagnostic Radiology, Shizuoka Cancer Center 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka 411-8777, Japan; Division of Comprehensive Radiology Center, Chiba University Hospital 1-8-1 Inohana, Chuo-ku, Chiba-shi, Chiba 260-8677, Japan. Electronic address: m.endo@scchr.jp.

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