Non-invasive regional cerebral blood flow quantification in the 123I-IMP autoradiography using artificial neural network.

Journal: PloS one
PMID: 36888603

Abstract

PURPOSE: Regional cerebral blood flow (rCBF) quantification using 123I-N-isopropyl-p-iodoamphetamine (123I-IMP) requires an invasive, one-time-only arterial blood sampling for measuring the 123I-IMP arterial blood radioactivity concentration (Ca10). The purpose of this study was to estimate Ca10 by machine learning (ML) using artificial neural network (ANN) regression analysis and consequently calculating rCBF and cerebral vascular reactivity (CVR) in the dual-table autoradiography (DTARG) method.

Authors

  • Tetsuro Kaga
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Hiroki Kato
    Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Osaka, Japan.
  • Toyohiro Imai
    Department of Radiology Services, Gifu University Hospital, Gifu, Japan.
  • Tomohiro Ando
    Department of Radiology, Gifu University, Gifu, Japan.
  • Yoshifumi Noda
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Takayuki Miura
    Department of Radiology Services, Gifu University Hospital, Gifu, Japan.
  • Yukiko Enomoto
    Department of Neurosurgery, Gifu University Graduate School of Medicine, Gifu, Japan.
  • Fuminori Hyodo
    Department of Radiology, Frontier Science for Imaging, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Toru Iwama
    Department of Neurosurgery, Gifu University Graduate School of Medicine, Gifu, Japan.
  • Masayuki Matsuo
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.

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