Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.

Journal: Annals of nuclear medicine
Published Date: Mar 1, 2024

Abstract

OBJECTIVE: Deep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computed tomography (SPECT). In this study, we proposed a novel deep learning approach referring to positron emission tomography (PET). The aims of this study were to analyze the agreement of representative voxel values and perfusion scores of SPECT-to-PET translation model-generated SPECT (SPECT) against PET in 17 segments according to the American Heart Association (AHA).

Authors

  • Masateru Kawakubo
    Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka-shi, Fukuoka, 812-8582, Japan. kawakubo.masateru.968@m.kyushu-u.ac.jp.
  • Michinobu Nagao
    Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, Tokyo, Japan.
  • Yoko Kaimoto
    Department of Radiology, Tokyo Women's Medical University, Tokyo, Japan.
  • Risako Nakao
    Department of Cardiology, Tokyo Women's Medical University, Tokyo, Japan.
  • Atsushi Yamamoto
    Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan.
  • Hiroshi Kawasaki
    Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
  • Takafumi Iwaguchi
    Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
  • Yuka Matsuo
    Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan.
  • Koichiro Kaneko
    Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan.
  • Akiko Sakai
    Department of Cardiology, Tokyo Women's Medical University, Tokyo, Japan.
  • Shuji Sakai
    Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, 8-1 Kawada-cho, Shinjuku-Ku, Tokyo, 162-8666, Japan.

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