Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study.

Journal: Physical and engineering sciences in medicine
Published Date: Sep 19, 2023

Abstract

PURPOSE: This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR).

Authors

  • Seiya Sato
    Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
  • Atsushi Urikura
    Division of Diagnostic Radiology, Shizuoka Cancer Center 1007 Shimonagakubo, Nagaizumi, Sunto, Shizuoka 411-8777, Japan. Electronic address: at.urikura@scchr.jp.
  • Makoto Mimatsu
    Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
  • Yuta Miyamae
    Department of Radiological Technology, Radiological Diagnosis, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan.
  • Yuji Jibiki
    Clinical Product Specialist Marketing Group, FUJIFILM Corporation, 7-3, Akasaka 9-Chome Minato-Ku, Tokyo, Japan.
  • Mami Yamashita
    Clinical Product Specialist Marketing Group, FUJIFILM Corporation, 7-3, Akasaka 9-Chome Minato-Ku, Tokyo, Japan.
  • Toshihiro Ishihara
    Department of Radiological Technology, National Cancer Center Japan, Tokyo, Japan.

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