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Abstract #0785

DeepControl: AI-powered slice flip-angle homogenization by 2DRF pulses

Mads Sloth Vinding1, Christoph Stefan Aigner2, Jason Stockmann3,4, Bastien Guérin3,4, Sebastian Schmitter2,5, and Torben Ellegaard Lund1
1Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark, 2Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany, 3Harvard Medical School, Boston, MA, United States, 4Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 5Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

We test the DeepControl convolutional neural network for brain slice 2DRF 30o excitations (single-channel) at 7 T with a uniform FA profile. While the DeepControl framework was originally designed for localized 2D spatial-selective excitations, we demonstrate robustness towards FA homogenization at 7 T. Our numerical study, a comparison to gradient-ascent-pulse-engineering optimal control pulses, shows good pulse performance and (near-)conformity to the optimal control training library, including the RF pulse constraints. The DeepControl pulse prediction time takes only ~9 ms, which is more than 1000 times faster than the optimal control.

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