Deep learning-based relative B1+-mapping in the human body at 7T
Felix Krüger1, Christoph Stefan Aigner1, Sebastian Dietrich1, Kerstin Hammernik2,3, and Sebastian Schmitter1,4,5
1Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany, 2Technical University of Munich, Munich, Germany, 3Imperial College London, London, United Kingdom, 4Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 5Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
In this work, we estimate relative 2D B1+-maps from initial localizer scans using deep learning at 7T. We investigate 7 UNets and MultiResUNets architectures to estimate complex, channel-wise, relative 2D B1+-maps of 8 transmit channels from a single gradient echo localizer obtained with 32 receive channels. The networks are evaluated in 5 unseen volunteers not included in the training library by comparing the prediction with the acquired relative B1+-maps using different evaluation metrics for homogeneous B1+ phase shimming. Our approach saves additional B1+-mapping scans, and, hence, overcomes long calibration times in the human body at 7T.
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