DeepControl: 2D RF pulses facilitating B_1^+ inhomogeneity and B_0 off-resonance compensation in vivo at 7T
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Purpose: Rapid 2D RF pulse design with subject specific B_1^+ inhomogeneity and B_0 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, 2D RF pulses optimized with an optimal control method using artificial 2D targets, B_1^+ and B_0 maps. Predicted pulses were tested in a phantom and in vivo at 7 T with measured B_1^+ and B_0 maps from a high-resolution GRE 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 ROIs and the measured B_1^+ and B_0 maps. Compensation of B_1^+ inhomogeneity and B_0 off-resonances has been confirmed in the phantom and in vivo experiments. The reconstructed image data agrees well with the simulations using the acquired B_1^+ and B_0 maps and the 2D RF 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 2D RF pulse design method predicts 2D RF pulses with an excellent excitation pattern and compensated B_1^+ and B_0 variations at 7 T. The rapid 2D RF pulse prediction (9 ms) enables subject-specific high-quality 2D RF pulses without the need to run lengthy optimizations.
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