Motion-resolved parallel-transmit (pTx) B1-maps can be predicted using neural networks, facilitating online pulse re-design – a prospective solution to motion. However, networks require large training-datasets. Since different pTx coils inherently produce different B1-distributions, it is unclear whether coil-specific training-datasets are needed. Here, we train networks on simulated data from one coil-model and test on 6 differently-sized coil-models. While performance was optimal for the coil on which networks were trained, B1-prediction yielded lower error than that caused by motion in ≥91% of magnitude, and ≥55% of phase evaluations for 5 out of the 6 models, demonstrating some generalisability across coils.
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