Motion-resolved B1+ prediction using deep learning for real-time pTx pulse-design.

Motion in parallel-transmit (pTx) causes flip-angle error due to dependence of channels' B1-sensitivities on head position. Real-time pTx pulse-design could mitigate motion-induced flip-angle error, but requires real-time, motion-resolved B1⁺ distributions (not measurable). A deep learning method is presented to estimate motion-resolved B1⁺ maps via a system of conditional generative adversarial networks. Using simulations, we demonstrate that estimated maps can be used to design tailored pTx pulses which yield similar flip-angle profiles to those without motion, reducing maximum observed flip-angle error from 79% to 25%. Importantly, networks can be run sequentially to accurately predict B1⁺ for arbitrary displacements incorporating multiple directions.