The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review.

Journal: Radiography (London, England : 1995)
Published Date: Jul 27, 2021

Abstract

INTRODUCTION: Low-dose computed tomography tends to produce lower image quality than normal dose computed tomography (CT) although it can help to reduce radiation hazards of CT scanning. Research has shown that Artificial Intelligence (AI) technologies, especially deep learning can help enhance the image quality of low-dose CT by denoising images. This scoping review aims to create an overview on how AI technologies, especially deep learning, can be used in dose optimisation for low-dose CT.

Authors

  • E Immonen
    Metropolia University of Applied Sciences, Finland. Electronic address: elisa.immonen@metropolia.fi.
  • J Wong
    Singapore Institute of Technology (SIT), Singapore. Electronic address: 1801515@sit.singaporetech.edu.sg.
  • M Nieminen
    Metropolia University of Applied Sciences, Finland. Electronic address: mika.nieminen@metropolia.fi.
  • L Kekkonen
    Metropolia University of Applied Sciences, Finland. Electronic address: leena.kekkonen@metropolia.fi.
  • S Roine
    Metropolia University of Applied Sciences, Finland. Electronic address: sara.roine@metropolia.fi.
  • S Törnroos
    Metropolia University of Applied Sciences, Finland. Electronic address: sanna.tornroos@metropolia.fi.
  • L Lanca
    Singapore Institute of Technology (SIT), Singapore. Electronic address: luis.lanca@singaporetech.edu.sg.
  • F Guan
    Singapore Institute of Technology (SIT), Singapore. Electronic address: frank.guan@singaporetech.edu.sg.
  • E Metsälä
    Metropolia University of Applied Sciences, Finland. Electronic address: eija.metsala@metropolia.fi.

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