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In Magnetic resonance in medicine ; h5-index 66.0

PURPOSE : Rapid 2DRF pulse design with subject-specific B 1 + inhomogeneity and B0 off-resonance compensation at 7 T predicted from convolutional neural networks is presented.

METHODS : The convolution neural network was trained on half a million single-channel transmit 2DRF pulses optimized with an optimal control method using artificial 2D targets, B 1 + and B0 maps. Predicted pulses were tested in a phantom and in vivo at 7 T with measured B 1 + and B0 maps from a high-resolution gradient echo sequence.

RESULTS : Pulse prediction by the trained convolutional neural network was done on the fly during the MR session in approximately 9 ms for multiple hand-drawn regions of interest and the measured B 1 + and B0 maps. Compensation of B 1 + inhomogeneity and B0 off-resonances has been confirmed in the phantom and in vivo experiments. The reconstructed image data agree well with the simulations using the acquired B 1 + and B0 maps, and the 2DRF pulse predicted by the convolutional neural networks is as good as the conventional RF pulse obtained by optimal control.

CONCLUSION : The proposed convolutional neural network-based 2DRF pulse design method predicts 2DRF pulses with an excellent excitation pattern and compensated B 1 + and B0 variations at 7 T. The rapid 2DRF pulse prediction (9 ms) enables subject-specific high-quality 2DRF pulses without the need to run lengthy optimizations.

Vinding Mads Sloth, Aigner Christoph Stefan, Schmitter Sebastian, Lund Torben Ellegaard


2DRF pulses, 7 T, artificial intelligence, deep learning, optimal control