Motivation: Online multi-channel B1+ mapping is essential in ultra-high field MRI for individual optimization of parallel transmit pulses. Goal (s): To rapidly and accurately estimate multi-channel B1+ maps at 7T. Approach: A deep learning reconstruction was implemented for accelerating the Sandwich sequence. The approach was validated by comparing the estimated 3D B1+ maps of five subjects with the fully sampled acquisitions and conventional compressed sensing reconstructions. Results: The proposed deep learning method generates accurate 3D multi-channel B1+ maps in under 10 seconds, with an RMSE of 3. 6\, ±\, 1. 4° for a target flip angle of 90°. Impact: This study demonstrates a deep learning-based method for rapid B1+ mapping in ultra-high field MRI, significantly reducing acquisition time to under 10 seconds while maintaining accuracy. The approach enhances the efficiency of parallel transmission, facilitating clinical applications at 7T.
Montemayor et al. (Tue,) studied this question.
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