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005     20250203105546.0
024 7 _ |a 10.1002/mrm.30359
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024 7 _ |a 0740-3194
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037 _ _ |a DKFZ-2024-02171
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Krueger, Felix
|b 0
245 _ _ |a Deep learning-based whole-brain B1 +-mapping at 7T.
260 _ _ |a New York, NY [u.a.]
|c 2025
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500 _ _ |a #LA:E020# / 2025 Apr;93(4):1700-1711
520 _ _ |a 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.
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650 _ 7 |a 7 tesla
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650 _ 7 |a B1+‐mapping
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650 _ 7 |a brain
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650 _ 7 |a deep learning
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650 _ 7 |a parallel transmission
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700 1 _ |a Aigner, Christoph Stefan
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700 1 _ |a Lutz, Max
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700 1 _ |a Riemann, Layla Tabea
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700 1 _ |a Degenhardt, Katja
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700 1 _ |a Hadjikiriakos, Kimon
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700 1 _ |a Zimmermann, Felix Frederik
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700 1 _ |a Hammernik, Kerstin
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700 1 _ |a Schulz-Menger, Jeanette
|0 0000-0003-3100-1092
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700 1 _ |a Schaeffter, Tobias
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700 1 _ |a Schmitter, Sebastian
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