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Abstract #0230

Design of slice-selective RF pulses using deep learning

Felix Kr├╝ger1, Max Lutz1, Christoph Stefan Aigner1, and Sebastian Schmitter1
1Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany

We utilize a residual neural network for the design of slice-selective RF and gradient trajectories. The network was trained with 300k SLR RF pulses. The network predicts the RF pulse and the gradient for a desired magnetization profile. The aim is to evaluate the feasibility and dependence on different parameter variations of this new approach. This method is validated comparing the prediction of the neural network with Bloch simulations and with phantom experiments at 3T. These insights serve as a basis for more general and complex pulses for future neural network design.

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