Rigid motion-resolved prediction using deep learning for real-time parallel-transmission pulse design

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 $$ distributions following within-slice motion, which can then be used for tailored pTx pulse redesign.

Using simulated data, conditional generative adversarial networks were trained to predict $$ 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 ground-truth (simulated, following motion) B₁ maps. Tailored pTx pulses were designed using B₁ maps at the original position (simulated, no motion) and evaluated using simulated B₁ 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.

Predicted $$ 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.

We propose a framework for predicting $$ 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.