Enhanced parameter estimation in multiparametric arterial spin labeling using artificial neural networks.

Journal: Magnetic resonance in medicine
PMID: 38852172

Abstract

PURPOSE: Multiparametric arterial spin labeling (MP-ASL) can quantify cerebral blood flow (CBF) and arterial cerebral blood volume (CBV). However, its accuracy is compromised owing to its intrinsically low SNR, necessitating complex and time-consuming parameter estimation. Deep neural networks (DNNs) offer a solution to these limitations. Therefore, we aimed to develop simulation-based DNNs for MP-ASL and compared the performance of a supervised DNN (DNN), physics-informed unsupervised DNN (DNN), and the conventional lookup table method (LUT) using simulation and in vivo data.

Authors

  • Shota Ishida
    Department of Radiological Technology, Faculty of Medical Sciences, Kyoto College of Medical Science, Kyoto, Japan.
  • Yasuhiro Fujiwara
    Division of Clinical Radiology, Tottori University Hospital.
  • Yuki Matta
    Radiological Center, University of Fukui Hospital, Eiheiji, Japan.
  • Naoyuki Takei
    GE Healthcare, Tokyo, Japan.
  • Masayuki Kanamoto
    Radiological Center, University of Fukui Hospital, Fukui, Japan.
  • Hirohiko Kimura
    Faculty of Medical Sciences, University of Fukui, Fukui, Japan.
  • Tetsuya Tsujikawa
    Department of Radiology, Faculty of Medical Sciences, University of Fukui, Fukui, Japan.

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