Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.

Journal: NeuroImage
Published Date: Sep 1, 2023

Abstract

PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time.

Authors

  • Jonghyun Bae
    Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York, USA.
  • Chenyang Li
  • Arjun Masurkar
    Center for Cognitive Neurology, Department of Neurology, New York University School of Medicine; Department of Neuroscience & Physiology, New York University School of Medicine; Neuroscience Institute, New York University School of Medicine. Electronic address: Arjun.Masurkar@nyulangone.org.
  • Yulin Ge
    Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, USA.
  • Sungheon Gene Kim
    Department of Radiology, Weill Cornell Medical College, New York, NY. Electronic address: sgk4001@med.cornell.edu.

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