Accuracy of deep learning-based attenuation correction in Tc-GSA SPECT/CT hepatic imaging.

Journal: Radiography (London, England : 1995)
PMID: 39549604

Abstract

INTRODUCTION: Attenuation correction (AC) is necessary for accurate assessment of radioactive distribution in single photon emission computed tomography (SPECT). The method of computed tomography-based AC (CTAC) is widely used because of its accuracy. However, patients are exposed to radiation during CT examination. The purpose of this study was to generate pseudo CT images for AC from non-AC SPECT images using deep learning and evaluate the effect of deep learning-based AC in Tc-labeled galactosyl human serum albumin SPECT/CT imaging.

Authors

  • M Miyai
    Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama-Shi, Okayama 700-8558, Japan; Department of Radiology, Kawasaki Medical School General Medical Center, 2-6-1 Nakasange, Kita-ku, Okayama-shi, Okayama 700-8505, Japan. Electronic address: miya0210@hp.kawasaki-m.ac.jp.
  • R Fukui
    Department of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama-Shi, Okayama 700-8558, Japan.
  • M Nakashima
    Division of Radiological Technology, Okayama University Hospital, 2-5-1 Shikata-cho, Kita-ku, Okayama-Shi, Okayama 700-8558, Japan.
  • D Hasegawa
    Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama-Shi, Okayama 700-8558, Japan; Department of Radiological Technology, Faculty of Health Science, Kobe Tokiwa University, 2-6-2 Otani-cho, Nagata-ku, Kobe, Hyogo 653-0838, Japan.
  • S Goto
    Department of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama-Shi, Okayama 700-8558, Japan.

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