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Abstract #4643

Rapid multi-dimensional RF pulse design with deep learning

Mads Sloth Vinding1, Birk Skyum2, Ryan Sangill1, and Torben Ellegaard Lund1

1Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark, 2Interdisciplinary Nanoscience Center, Aarhus University, Aarhus, Denmark

For multi-dimensional RF pulses, neural networks and deep learning may boost the clinical applicability by allowing very rapid pulse predictions, based on offline training and offline generated training libraries. This can potentially offer opportunities, for example, to revive slow, abandoned pulse design techniques, or to include many more constraints or complexities into the pulse designs that until now were infeasible to bring into a clinical setting, since the neural network will simply learn the features of the training library. We are demonstrating the principle with numerical simulations, and phantom and in vivo experiments.

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