Radio frequency (RF) pulses play a key role in magnetic resonance imaging. However, current MRI systems mostly use pre-defined RF waveforms with minimal adaptions. In this work, we propose a novel hybrid framework to combine convolutional neural network (CNN) with signal modeling by feature extractions. The result demonstrates that the proposed method provides more robust pulse design estimation result than using a generalized CNN framework for any desired rectangle excitations.
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