Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study.

Journal: Magnetic resonance in medicine
Published Date: Jan 21, 2024

Abstract

PURPOSE: To mitigate inhomogeneity at 7T for multi-channel transmit arrays using unsupervised deep learning with convolutional neural networks (CNNs).

Authors

  • Toygan Kilic
    Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
  • Patrick Liebig
    Siemens Healthcare, GmbH, Erlangen, Germany.
  • Omer Burak Demirel
  • Jürgen Herrler
    Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Schwabachanlage 6 (Kopfklinikum), 91054 Erlangen, Germany; Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
  • Armin M Nagel
    Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Kâmil Uğurbil
    Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota.
  • Mehmet Akçakaya
    Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota.

Keywords

External Resources

Popular Topics
Recent Journals