Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer.

Journal: International journal of computer assisted radiology and surgery
PMID: 38219257

Abstract

PURPOSE: While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images.

Authors

  • Yuhei Koike
    Department of Radiation Oncology, Osaka University Graduate School of Medicine, Suita, 565-0871, Japan.
  • Shingo Ohira
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan; Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Suita, Japan.
  • Yuki Yamamoto
    Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • Masayoshi Miyazaki
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan.
  • Koji Konishi
    Department of Radiation Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 537-8567, Japan.
  • Satoaki Nakamura
    Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajiicho Kawaramachi Hirokoji, Kamigyo-ku, Kyoto 602-8566, Japan.
  • Noboru Tanigawa
    Department of Radiology, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan.

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