Attenuation correction using deep learning for brain perfusion SPECT images.

Journal: Annals of nuclear medicine
Published Date: May 1, 2021

Abstract

OBJECTIVE: Non-uniform attenuation correction using computed tomography (CT) improves the image quality and quantification of single-photon emission computed tomography (SPECT). However, it is not widely used because it requires a SPECT/CT scanner. This study constructs a convolutional neural network (CNN) to generate attenuation-corrected SPECT images directly from non-attenuation-corrected SPECT images.

Authors

  • Kenta Sakaguchi
    Radiology Center, Kindai University Hospital, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan. sakaguchi_kenta@med.kindai.ac.jp.
  • Hayato Kaida
    Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osaka, Japan.
  • Shuhei Yoshida
    Radiology Center, Kindai University Hospital, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan.
  • Kazunari Ishii
    Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osaka, Japan.

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