Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Journal: Medical physics
Published Date: Jun 6, 2024

Abstract

BACKGROUND: Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful. However, the introduction of non-linear reconstruction methods, such as iterative and deep learning-based reconstruction, makes noise insertion in the image domain intractable, as it is not possible to determine the noise textures analytically.

Authors

  • Sjoerd A M Tunissen
    Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.
  • Nikita Moriakov
    Department of Medical Imaging, Radboud University Medical Center, the Netherlands; Department of Radiation Oncology, Netherlands Cancer Institute, the Netherlands.
  • Mikhail Mikerov
    Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Ewoud J Smit
    Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101 (route 766), 6500 HB, Nijmegen, The Netherlands.
  • Ioannis Sechopoulos
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Jonas Teuwen
    Department of Radiology and Nuclear Medicine, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands.

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