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@ARTICLE{Eberhardt:906178,
      author       = {Eberhardt, Boris and Poser, Benedikt A. and Shah, N. Jon
                      and Felder, Jörg},
      title        = {{B}1 field map synthesis with generative deep learning used
                      in the design of parallel-transmit {RF} pulses for
                      ultra-high field {MRI}},
      journal      = {Zeitschrift für medizinische Physik},
      volume       = {32},
      number       = {3},
      issn         = {0939-3889},
      address      = {Amsterdam},
      publisher    = {Elsevier, Urban $\&$ Fischer},
      reportid     = {FZJ-2022-01278},
      pages        = {334-345},
      year         = {2022},
      abstract     = {Spoke trajectory parallel transmit (pTX) excitation in
                      ultra-high field MRI enablesinhomogeneities arising from the
                      shortened RF wavelength in biological tissue to be
                      mitigated. To this end, current RF excitation pulse design
                      algorithms either employ the acquisition of field maps with
                      subsequent non-linear optimization or a universal approach
                      applying robust pre-computed pulses. We suggest and evaluate
                      an intermediate method that uses a subset of acquired field
                      maps combined with generative machine learning models to
                      reduce the pulse calibration time while offering more
                      tailored excitation than robust pulses (RP).The possibility
                      of employing image-to-image translation and semantic image
                      synthesis machine learning models based on generative
                      adversarial networks (GANs) to deduce the missing field maps
                      is examined. Additionally, an RF pulse design that employs a
                      predictive machine learning model to find solutions for the
                      non-linear (two-spokes) pulse design problem is
                      investigated.As a proof of concept, we present simulation
                      results obtained with the suggested machine learning
                      approaches that were trained on a limited data-set, acquired
                      in vivo. The achieved excitation homogeneity based on a
                      subset of half of themaps acquired in the calibration scans
                      and half of the maps synthesized with GANs is comparable
                      with state of the art pulse design methods when using the
                      full set of calibration data while halving the total
                      calibration time. By employing RP dictionaries or
                      machine-learning RF pulse predictions, the total calibration
                      time can be reduced significantly as these methods take only
                      seconds or milliseconds per slice, respectively.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000878590000008},
      doi          = {10.1016/j.zemedi.2021.12.003},
      url          = {https://juser.fz-juelich.de/record/906178},
}