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@ARTICLE{Krueger:294338,
      author       = {F. Krueger and C. S. Aigner and M. Lutz and L. T. Riemann
                      and K. Degenhardt and K. Hadjikiriakos and F. F. Zimmermann
                      and K. Hammernik and J. Schulz-Menger and T. Schaeffter and
                      S. Schmitter$^*$},
      title        = {{D}eep learning-based whole-brain {B}1 +-mapping at 7{T}.},
      journal      = {Magnetic resonance in medicine},
      volume       = {93},
      number       = {4},
      issn         = {1522-2594},
      address      = {New York, NY [u.a.]},
      publisher    = {Wiley-Liss},
      reportid     = {DKFZ-2024-02171},
      pages        = {1700-1711},
      year         = {2025},
      note         = {#LA:E020# / 2025 Apr;93(4):1700-1711},
      abstract     = {This study investigates the feasibility of using
                      complex-valued neural networks (NNs) to estimate
                      quantitative transmit magnetic RF field (B1 +) maps from
                      multi-slice localizer scans with different slice
                      orientations in the human head at 7T, aiming to accelerate
                      subject-specific B1 +-calibration using parallel
                      transmission (pTx).Datasets containing channel-wise B1
                      +-maps and corresponding multi-slice localizers were
                      acquired in axial, sagittal, and coronal orientation in 15
                      healthy subjects utilizing an eight-channel pTx transceiver
                      head coil. Training included five-fold cross-validation for
                      four network configurations: NN cx tra $$
                      {\mathrm{NN}}_{\mathrm{cx}}^{\mathrm{tra}} $$ used
                      transversal, NN cx sag $$
                      {\mathrm{NN}}_{\mathrm{cx}}^{\mathrm{sag}} $$ sagittal, NN
                      cx cor $$ {\mathrm{NN}}_{\mathrm{cx}}^{\mathrm{cor}} $$
                      coronal data, and NN cx all $$
                      {\mathrm{NN}}_{\mathrm{cx}}^{\mathrm{all}} $$ was trained on
                      all slice orientations. The resulting maps were compared to
                      B1 +-reference scans using different quality metrics. The
                      proposed network was applied in-vivo at 7T in two unseen
                      test subjects using dynamic kt-point pulses.Predicted B1
                      +-maps demonstrated a high similarity with measured B1
                      +-maps across multiple orientations. The estimation matched
                      the reference with a mean relative error in the magnitude of
                      (2.70 ± 2.86)\% and mean absolute phase difference of (6.70
                      ± 1.99)° for transversal, (1.82 ± 0.69)\% and (4.25 ±
                      1.62)° for sagittal ( NN cx sag $$
                      {\mathrm{NN}}_{\mathrm{cx}}^{\mathrm{sag}} $$ ), as well as
                      (1.33 ± 0.27)\% and (2.66 ± 0.60)° for coronal slices (
                      NN cx cor $$ {\mathrm{NN}}_{\mathrm{cx}}^{\mathrm{cor}} $$ )
                      considering brain tissue. NN cx all $$
                      {\mathrm{NN}}_{\mathrm{cx}}^{\mathrm{all}} $$ trained on all
                      orientations enables a robust prediction of B1 +-maps across
                      different orientations. Achieving a homogenous excitation
                      over the whole brain for an in-vivo application displayed
                      the approach's feasibility.This study demonstrates the
                      feasibility of utilizing complex-valued NNs to estimate
                      multi-slice B1 +-maps in different slice orientations from
                      localizer scans in the human brain at 7T.},
      keywords     = {7 tesla (Other) / B1+‐mapping (Other) / brain (Other) /
                      deep learning (Other) / parallel transmission (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:39462473},
      doi          = {10.1002/mrm.30359},
      url          = {https://inrepo02.dkfz.de/record/294338},
}