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Abstract #3719

Rapid multi-slice whole-brain B1+-mapping at 7T using deep learning

Felix Krueger1, Christoph Stefan Aigner1, Max Lutz1, Layla Tabea Riemann1, Katja Degenhardt1, Bernd Ittermann1, Tobias Schaeffter1,2, Kerstin Hammernik3,4, and Sebasian Schmitter1,5,6
1Physikalisch-Technische Bundesanstalt, Berlin and Braunschweig, Germany, 2Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom, 3Technical University of Munich, Munich, Germany, 4Imperial College London, London, United Kingdom, 5Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 6Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Synopsis

Keywords: RF Pulse Design & Fields, Parallel Transmit & MultibandIn this work we utilize deep learning to estimate multi-slice whole-brain B1+-maps in sub-seconds from initial localizer scans at 7T. The investigated neural networks use the receive profiles of the individual coil elements of an 8Tx/8Rx transceiver head coil as input information. The networks are trained on seven volunteers and tested in 2 unseen subjects for transversal/coronal/sagittal slices by comparing the prediction with the acquired B1+-maps. Subsequently, the feasibility of using the DL-based B1+-maps in a subject-specific calibration pipeline is demonstrated.

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