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Purpose: Rapid 2D RF pulse design with subject specific $B_1^+$ inhomogeneityand $B_0$ off-resonance compensation at 7 T predicted from convolutional neuralnetworks is presented.  Methods: The convolution neural network was trained on half a millionsingle-channel transmit, 2D RF pulses optimized with an optimal control methodusing artificial 2D targets, $B_1^+$ and $B_0$ maps. Predicted pulses weretested in a phantom and in vivo at 7 T with measured $B_1^+$ and $B_0$ mapsfrom a high-resolution GRE sequence.  Results: Pulse prediction by the trained convolutional neural network wasdone on the fly during the MR session in approximately 9 ms for multiple handdrawn 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 invivo experiments. The reconstructed image data agrees well with the simulationsusing the acquired $B_1^+$ and $B_0$ maps and the 2D RF pulse predicted by theconvolutional neural networks is as good as the conventional RF pulse obtainedby optimal control.  Conclusion: The proposed convolutional neural network based 2D RF pulsedesign method predicts 2D RF pulses with an excellent excitation pattern andcompensated $B_1^+$ and $B_0$ variations at 7 T. The rapid 2D RF pulseprediction (9 ms) enables subject-specific high-quality 2D RF pulses withoutthe need to run lengthy optimizations.

DeepControl: 2D RF pulses facilitating $B_1^+$ inhomogeneity and $B_0$ off-resonance compensation in vivo at 7T