DeepControl: 2DRF pulses facilitating inhomogeneity and B 0 off‐resonance compensation in vivo at 7 T

PURPOSE Rapid 2DRF pulse design with subject-specific B1+ 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, B1+ and B0 maps. Predicted pulses were tested in a phantom and in vivo at 7 T with measured B1+ 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 B1+ and B0 maps. Compensation of B1+ 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 B1+ 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 B1+ 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.