The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting.

Journal: Clinical radiology
Published Date: Nov 19, 2020

Abstract

AIM: To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).

Authors

  • A Hata
    Department of Future Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. Electronic address: a-hata@radiol.med.osaka-u.ac.jp.
  • M Yanagawa
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • Y Yoshida
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • T Miyata
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • N Kikuchi
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • O Honda
    Department of Radiology, Kansai Medical University, 2-5-1 Shin-machi, Hirakata, Osaka, 573-1010, Japan.
  • N Tomiyama
    Department of Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.

Keywords

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