Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging.

Journal: AJNR. American journal of neuroradiology
Published Date: Jan 27, 2022

Abstract

BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

Authors

  • K T Chen
    From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California chenkt@ntu.edu.tw.
  • O Adeyeri
    Department of Computer Science (O.A.), Salem State University, Salem, Massachusetts.
  • T N Toueg
    Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, Stanford, California.
  • M Zeineh
    From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California.
  • E Mormino
    Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, Stanford, California.
  • M Khalighi
    From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California.
  • G Zaharchuk
    From the Departments of Radiology (G.Z., M.W., D.R., C.P.L.) gregz@stanford.edu.

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