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| Home > Publications database > Deep learning-based whole-brain B1 +-mapping at 7T. |
| Journal Article | DKFZ-2024-02171 |
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2025
Wiley-Liss
New York, NY [u.a.]
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Please use a persistent id in citations: doi:10.1002/mrm.30359
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.
Keyword(s): 7 tesla ; B1+‐mapping ; brain ; deep learning ; parallel transmission
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