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@ARTICLE{Kilic:287250,
      author       = {T. Kilic and P. Liebig and O. B. Demirel and J. Herrler and
                      A. Nagel$^*$ and K. Ugurbil and M. Akçakaya},
      title        = {{U}nsupervised deep learning with convolutional neural
                      networks for static parallel transmit design: {A}
                      retrospective study.},
      journal      = {Magnetic resonance in medicine},
      volume       = {91},
      number       = {6},
      issn         = {1522-2594},
      address      = {New York, NY [u.a.]},
      publisher    = {Wiley-Liss},
      reportid     = {DKFZ-2024-00182},
      pages        = {2498-2507},
      year         = {2024},
      note         = {2024 Jun;91(6):2498-2507},
      abstract     = {To mitigate B 1 + $$ {B}_1^{+} $$ inhomogeneity at 7T for
                      multi-channel transmit arrays using unsupervised deep
                      learning with convolutional neural networks (CNNs).Deep
                      learning parallel transmit (pTx) pulse design has received
                      attention, but such methods have relied on supervised
                      training and did not use CNNs for multi-channel B 1 + $$
                      {B}_1^{+} $$ maps. In this work, we introduce an alternative
                      approach that facilitates the use of CNNs with multi-channel
                      B 1 + $$ {B}_1^{+} $$ maps while performing unsupervised
                      training. The multi-channel B 1 + $$ {B}_1^{+} $$ maps are
                      concatenated along the spatial dimension to enable
                      shift-equivariant processing amenable to CNNs. Training is
                      performed in an unsupervised manner using a physics-driven
                      loss function that minimizes the discrepancy of the Bloch
                      simulation with the target magnetization, which eliminates
                      the calculation of reference transmit RF weights. The
                      training database comprises 3824 2D sagittal, multi-channel
                      B 1 + $$ {B}_1^{+} $$ maps of the healthy human brain from
                      143 subjects. B 1 + $$ {B}_1^{+} $$ data were acquired at 7T
                      using an 8Tx/32Rx head coil. The proposed method is compared
                      to the unregularized magnitude least-squares (MLS) solution
                      for the target magnetization in static pTx design.The
                      proposed method outperformed the unregularized MLS solution
                      for RMS error and coefficient-of-variation and had
                      comparable energy consumption. Additionally, the proposed
                      method did not show local phase singularities leading to
                      distinct holes in the resulting magnetization unlike the
                      unregularized MLS solution.Proposed unsupervised deep
                      learning with CNNs performs better than unregularized MLS in
                      static pTx for speed and robustness.},
      keywords     = {7 T (Other) / RF inhomogeneity mitigation (Other) /
                      convolutional neural networks (Other) / deep learning
                      (Other) / parallel excitation (Other) / unsupervised
                      learning (Other)},
      cin          = {E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38247050},
      doi          = {10.1002/mrm.30014},
      url          = {https://inrepo02.dkfz.de/record/287250},
}