Keywords: High-Field MRI, RF Pulse Design & Fields, High-Field MRI, pTx, B1+ mapping, neural networks
Motivation: B1+ -maps needed for subject-specific pTx can be derived from localizers using a neural network (NN), omitting separate B1+-mapping. Ideally, a single, general NN applicable to all UHF sites is highly desired.
Goal(s): Investigate the robustness of a neural network predicted B1+-field maps. Utilizing receive profiles from an 8Tx/32Rx head coil as neural network input.
Approach: Comparing the performance on data from identical and different commercial head coils across multiple MRI sites.
Results: Achieving SSIMs as high as 96% and RMSEs as low as 2.7%, with error mapping predominantly localizing discrepancies at the cranial margins, suggesting that larger datasets could enhance Gaussian convergence.
Impact: The study suggests that a single NN trained by a large B1+ library for one type of pTx head coil may be disseminated to other UHF sites that use the same coil. This will enable a fast, streamlined pTx calibration.
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