Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.

Journal: Annals of nuclear medicine
Published Date: Aug 1, 2022

Abstract

OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over an hour after injection to obtain sufficient image quality can be challenging for patients with suspected dementia and their caregivers. This study aimed to develop an image-based deep-learning technique to generate delayed uptake patterns of amyloid positron emission tomography (PET) images using only early-phase images obtained from 0-20 min after radiotracer injection.

Authors

  • Seisaku Komori
    Future Design Lab, New Concept Design, Global Strategic Challenge Center, Hamamatsu Photonics K.K. 5000, Hirakuchi, Hamakita-ku, Hamamatsu-City, 434-8601 Japan.
  • Donna J Cross
    Department of Radiology and Imaging Sciences, University of Utah, 30 North 1900 East #1A71, Salt Lake City, UT 84132-2140, USA. Electronic address: d.cross@utah.edu.
  • Megan Mills
    Department of Radiology and Imaging Sciences, University of Utah, 30 N. 1900 E. #1A71, Salt Lake City, UT, 84132-2140, USA.
  • Yasuomi Ouchi
    Department of Biofunctional Imaging, Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu 431-3192, Japan.
  • Sadahiko Nishizawa
    Hamamatsu Medical Imaging Center, Hamamatsu Medical Photonics Foundation, Hamamatsu, 434-8601, Japan.
  • Hiroyuki Okada
    Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
  • Takashi Norikane
    Department of Radiology, Faculty of Medicine, Kagawa Univerisity, Takamatsu, Japan.
  • Tanyaluck Thientunyakit
    Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Yoshimi Anzai
    Department of Radiology and Imaging Sciences, University of Utah, 30 N. 1900 E. #1A71, Salt Lake City, UT, 84132-2140, USA.
  • Satoshi Minoshima
    Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah.

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