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PURPOSE: Parallel transmission (PTx) requires knowledge of the B1+ produced by each element. However, B1+ mapping can be challenging when transmit fields exhibit large dynamic range. This study presents a method to produce high quality relative B1+ maps when this is the case. THEORY AND METHODS: The proposed technique involves the acquisition of spoiled gradient echo (SPGR) images at multiple radiofrequency drive levels for each transmitter. The images are combined using knowledge of the SPGR signal equation using maximum likelihood estimation, yielding an image for each channel whose signal is proportional to the B1+ field strength. Relative B1+ maps are then obtained by taking image ratios. The method was tested using numerical simulations, phantom imaging, and through in vivo experiments. RESULTS: The numerical simulations demonstrated that the proposed method can reconstruct relative transmit sensitivities over a wide range of B1+ amplitudes and at several SNR levels. The method was validated at 3 Tesla (T) by comparing it with an alternative B1+ mapping method, and demonstrated in vivo at 7T. CONCLUSION: Relative B1+ mapping in the presence of large dynamic range has been demonstrated through numerical simulations, phantom imaging at 3T and experimentally at 7T. The method will enable PTx to be applied in challenging imaging scenarios at ultrahigh field. Magn Reson Med 76:490-499, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Original publication




Journal article


Magn Reson Med

Publication Date





490 - 499


B1 mapping, parallel transmission, ultrahigh field MRI, Algorithms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Liver, Magnetic Resonance Imaging, Reproducibility of Results, Sensitivity and Specificity