DeepCEST: 9.4 T Chemical exchange saturation transfer MRI contrast predicted from 3 T data - a proof of concept study.

Journal: Magnetic resonance in medicine
Published Date: Feb 25, 2019

Abstract

PURPOSE: To determine the feasibility of employing the prior knowledge of well-separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z-spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach trained with 3 T and 9.4 T CEST spectral data from brains of the same subjects.

Authors

  • Moritz Zaiss
    High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
  • Anagha Deshmane
    High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
  • Mark Schuppert
    High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
  • Kai Herz
    High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
  • Felix Glang
    High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
  • Philipp Ehses
    German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
  • Tobias Lindig
    High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
  • Benjamin Bender
    Department of Diagnostic and Interventional Neuroradiology, Eberhard-Karls University Tübingen, Tübingen, Germany.
  • Ulrike Ernemann
    Department of Diagnostic and Interventional Neuroradiology, Eberhard-Karls University Tübingen, Tübingen, Germany.
  • Klaus Scheffler
    High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

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