Purpose: Some advanced RF pulses, like multidimensional RF pulses, are often long and require substantial computation time because of a number of constraints and requirements, sometimes hampering clinical use. However, the pulses offer opportunities of reduced-FOV imaging, regional flip-angle homogenization, and localized spectroscopy, e.g., of hyperpolarized metabolites. Proposed herein is a novel deep learning approach to ultrafast design of multidimensional RF pulses with intention of real-time pulse updates.
Methods: The proposed neural network considers input maps of the desired excitation region of interest and outputs a single-channel, multidimensional RF pulse. The training library is, e.g., retrieved from a large image database, and the target RF pulses trained upon are calculated with a method of choice.
Results: A relatively simple neural network is enough to produce reliable 2D spatial-selective RF pulses of comparable performance to the teaching method. For binary regions of interest, the training library does not need to be vast; hence, reestablishment of the training library is not necessarily cumbersome. The predicted pulses were tested numerically and experimentally at 3 T.
Conclusion: Relatively effortless training of multidimensional RF pulses, based on non-MRI-related inputs, but working in an MRI setting still, has been demonstrated. The prediction time of a few milliseconds renders real-time updates of advanced RF pulses possible.
Keywords: deep learning; multidimensional RF pulses; neural networks; optimal control theory.
© 2019 International Society for Magnetic Resonance in Medicine.