A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with Kr-gas SPECT ventilation imaging.

Journal: Medical physics
PMID:

Abstract

PURPOSE: This study aimed to evaluate the accuracy of deep learning (DL)-based computed tomography (CT) ventilation imaging (CTVI).

Authors

  • Tomohiro Kajikawa
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Noriyuki Kadoya
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan. kadoya.n@rad.med.tohoku.ac.jp.
  • Yosuke Maehara
    Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
  • Hiroshi Miura
    Department of Radiology, Japanese Red Cross Kyoto Daini Hospital, Kyoto, Japan.
  • Yoshiyuki Katsuta
    Department of Radiology, Takeda General Hospital, Aizuwakamatsu, Japan.
  • Shinsuke Nagasawa
    Dept. of Surgery, Yokohama City University Hospital.
  • Gen Suzuki
    Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho Kawaramachi Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan.
  • Hideya Yamazaki
    Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho Kawaramachi Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan CyberKnife Center, Soseikai General Hospital, 126 Kami-Misu, Shimotoba Fushimi-ku, Kyoto, Japan hideya10@hotmail.com.
  • Nagara Tamaki
    Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
  • Kei Yamada
    Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kajii-cho, Kawaramachi Hirokoji Agaru, Kamigyo-ku, Kyoto, Kyoto, 602-8566, Japan.