Rigid motion-resolved prediction using deep learning for real-time parallel-transmission pulse design.

Journal: Magnetic resonance in medicine
Published Date: Dec 27, 2021

Abstract

PURPOSE: Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx distributions following within-slice motion, which can then be used for tailored pTx pulse redesign.

Authors

  • Alix Plumley
    Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
  • Luke Watkins
    Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
  • Matthias Treder
    School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom.
  • Patrick Liebig
    Siemens Healthcare, GmbH, Erlangen, Germany.
  • Kevin Murphy
    School of Physics & Astronomy, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, United Kingdom.
  • Emre Kopanoglu
    Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.

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