Cramér-Rao bound-informed training of neural networks for quantitative MRI.

Journal: Magnetic resonance in medicine
Published Date: Mar 28, 2022

Abstract

PURPOSE: To improve the performance of neural networks for parameter estimation in quantitative MRI, in particular when the noise propagation varies throughout the space of biophysical parameters.

Authors

  • Xiaoxia Zhang
    Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
  • Quentin Duchemin
    LAMA, Univ Gustave Eiffel, Univ Paris Est Creteil, Marne-la-Vallée, France.
  • Kangning Liu
    School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
  • Cem Gultekin
    Courant Institute of Mathematical Sciences, New York University, New York City, New York, USA.
  • Sebastian Flassbeck
    Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York City, New York, USA.
  • Carlos Fernandez-Granda
    Center for Data Science, Courant Institute of Mathematical Sciences, New York University.
  • Jakob Assländer
    Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York City, New York, USA.

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