Rigid motion-resolved B(1)(+) prediction using deep learning for real-time parallel-transmission pulse design

PURPOSE: Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B(1)(+) distributions following within-slice motion, which can then be used for tailored pTx pulse redesign. METHODS: Using simulated data, conditional generative adversarial networks were trained to predict B(1)(+) distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with groundtruth (simulated, following motion) B(1)(+) maps. Tailored pTx pulses were designed using B(1)(+) maps at the original position (simulated, no motion) and evaluated using simulated B(1)(+) maps at displaced position (ground-truth maps) to quantify motion-related excitation error. A second pulse was designed using predicted maps (also evaluated on ground-truth maps) to investigate improvement offered by the proposed method. RESULTS: Predicted B(1)(+) maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps. CONCLUSION: We propose a framework for predicting B(1)(+) maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion–related error in pTx.