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| Item Type: | Article |
|---|---|
| Title: | Deep learning-based whole-brain B(1)(+)-mapping at 7T |
| Creators: |
Krueger, F., Aigner, C.S.  ORCID: https://orcid.org/0000-0003-3618-9610, Lutz, M. ORCID: https://orcid.org/0009-0001-1956-3757, Riemann, L.T. ORCID: https://orcid.org/0000-0001-5411-7288, Degenhardt, K. ORCID: https://orcid.org/0000-0002-0482-9698, Hadjikiriakos, K. ORCID: https://orcid.org/0009-0002-8625-8522, Zimmermann, F.F. ORCID: https://orcid.org/0000-0002-0862-8973, Hammernik, K. ORCID: https://orcid.org/0000-0002-2734-1409, Schulz-Menger, J. ORCID: https://orcid.org/0000-0003-3100-1092, Schaeffter, T. ORCID: https://orcid.org/0000-0003-1310-2631 and Schmitter, S. ORCID: https://orcid.org/0000-0003-4410-6790
|
| Abstract: | PURPOSE: This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B(1)(+)) maps from multi-slice localizer scans with different slice orientations in the human head at 7T, aiming to accelerate subject-specific B(1)(+)-calibration using parallel transmission (pTx). METHODS: Datasets containing channel-wise B(1)(+)-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(tra)(cx) used transversal, NN(sag)(cx) sagittal, NN(cor)(cx) coronal data, and NN(all)(cx) was trained on all slice orientations. The resulting maps were compared to B(1)(+)-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. RESULTS: Predicted B(1)(+)-maps demonstrated a high similarity with measured B(1)(+)-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(sag)(cx)), as well as (1.33 ± 0.27)% and (2.66 ± 0.60)° for coronal slices (NN(cor)(cx)) considering brain tissue. NN(all)(cx) trained on all orientations enables a robust prediction of B(1)(+)-maps across different orientations. Achieving a homogenous excitation over the whole brain for an in-vivo application displayed the approach's feasibility. CONCLUSION: This study demonstrates the feasibility of utilizing complex-valued NNs to estimate multi-slice B(1)(+)-maps in different slice orientations from localizer scans in the human brain at 7T. |
| Keywords: | 7 Tesla, B(1)(+), Mapping, Brain, Deep Learning, Parallel Transmission |
| Source: | Magnetic Resonance in Medicine |
| ISSN: | 0740-3194 |
| Publisher: | Wiley |
| Volume: | 93 |
| Number: | 4 |
| Page Range: | 1700-1711 |
| Date: | April 2025 |
| Official Publication: | https://doi.org/10.1002/mrm.30359 |
| PubMed: | View item in PubMed |
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