Calculating the target exposure index using a deep convolutional neural network and a rule base.

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: Feb 27, 2020

Abstract

PURPOSE: The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnosable exposure index (EI) and the target exposure index (EIt).

Authors

  • Takeshi Takaki
    Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan; Department of Radiology, Hospital of University of Occupational and Environmental Health, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Fukuoka 807-8555, Japan. Electronic address: takaki-t@clnc.uoeh-u.ac.jp.
  • Seiichi Murakami
    Department of Radiological Science, Faculty of Health Sciences, Junshin Gakuen University, 1-1-1 Chikushigaoka, Minami-ku, Fukuoka 815-8510, Japan.
  • Ryo Watanabe
    Department of Radiology, Hospital of University of Occupational and Environmental Health, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Fukuoka 807-8555, Japan.
  • Takatoshi Aoki
    Department of Radiology, University of Occupational and Environmental Health School of Medicine, Iseigaoka 1-1, Yahatanishi-ku, Kitakyushu-shi, Fukuoka 807-8555, Japan.
  • Toshioh Fujibuchi
    Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan.

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