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Rigid motion‐resolved B 1 + prediction using deep learning for real‐time parallel‐transmission pulse design
Author(s) -
Plumley Alix,
Watkins Luke,
Treder Matthias,
Liebig Patrick,
Murphy Kevin,
Kopanoglu Emre
Publication year - 2022
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.29132
Subject(s) - motion (physics) , pulse (music) , transmission (telecommunications) , computer science , artificial intelligence , nuclear magnetic resonance , physics , telecommunications , detector
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 ground‐truth (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.