Ultrafast (milliseconds), multidimensional RF pulse design with deep learning

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.