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Improvement of image quality at CT and MRI using deep learning

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Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as “noise and artifact reduction”, “super resolution” and “image acquisition and reconstruction”. For each category, we present and outline the features of some studies.

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Funding

Kazuo Awai received following research grant: Research Grant, Canon Medical systems, paid to the institution. Research Grant, Hitachi, paid to the institution.

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Authors and Affiliations

  1. Department of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan

    Toru Higaki, Yuko Nakamura, Fuminari Tatsugami & Kazuo Awai

  2. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, 860-8556, Japan

    Takeshi Nakaura

Authors
  1. Toru Higaki
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  2. Yuko Nakamura
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  3. Fuminari Tatsugami
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  4. Takeshi Nakaura
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  5. Kazuo Awai
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Corresponding author

Correspondence to Toru Higaki.

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The other authors declare that they have no conflict of interest.

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Higaki, T., Nakamura, Y., Tatsugami, F. et al. Improvement of image quality at CT and MRI using deep learning. Jpn J Radiol 37, 73–80 (2019). https://doi.org/10.1007/s11604-018-0796-2

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  • DOI: https://doi.org/10.1007/s11604-018-0796-2

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