Automatic chest computed tomography image noise quantification using deep learning.

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: Dec 1, 2023

Abstract

PURPOSE: This study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions.

Authors

  • Juuso H J Ketola
    Radiology, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Finland.
  • Satu I Inkinen
    HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland. Electronic address: satu.inkinen@hus.fi.
  • Teemu Mäkelä
    HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00029 HUS, Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland.
  • Touko Kaasalainen
    HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
  • Juha I Peltonen
    HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00029 HUS, Helsinki, Finland. Electronic address: juha.peltonen@hus.fi.
  • Marko Kangasniemi
    HUS Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340 (Haartmaninkatu 4), FI-00290, Helsinki, Finland.
  • Kirsi Volmonen
    Radiology, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Finland.
  • Mika Kortesniemi
    HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. Electronic address: mika.kortesniemi@hus.fi.

Keywords

External Resources

Popular Topics
Recent Journals