Improving Low-contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study.

Journal: Academic radiology
Published Date: Jan 7, 2020

Abstract

RATIONALE AND OBJECTIVES: To evaluate the performance of iterative reconstruction (IR) and filtered back projection (FBP) images in terms of low-contrast detectability at different radiation doses, IR levels, and slice thickness using the mathematical model observer with a focus on low-contrast detectability.

Authors

  • Yoshinori Funama
    Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto 860-8556, Japan (Y.F.).
  • Hisashi Takahashi
    Hitachi Ltd. Healthcare Business Unit, Kashiwa, Chiba, Japan.
  • Taiga Goto
    Hitachi Ltd. Healthcare Business Unit, Kashiwa, Chiba, Japan.
  • Yuko Aoki
    Hitachi Ltd. Healthcare Business Unit, Kashiwa, Chiba, Japan.
  • Ryo Yoshida
    The Graduate University for Advanced Studies (SOKENDAI), Tachikawa, Japan. yoshidar@ism.ac.jp.
  • Yukio Kumagai
    Hitachi Ltd. Healthcare Business Unit, Kashiwa, Chiba, Japan.
  • Kazuo Awai
    Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan.

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