DeepControl: 2DRF pulses facilitating inhomogeneity and B<sub>0</sub> off‐resonance compensation in vivo at 7 T
Mads Sloth Vinding, Christoph Stefan Aigner, Sebastian Schmitter, Torben E. Lund
Abstract
Purpose Rapid 2DRF pulse design with subject‐specific 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 2DRF pulses optimized with an optimal control method using artificial 2D targets, and B 0 maps. Predicted pulses were tested in a phantom and in vivo at 7 T with measured and B 0 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 and B 0 maps. Compensation of inhomogeneity and B 0 off‐resonances has been confirmed in the phantom and in vivo experiments. The reconstructed image data agree well with the simulations using the acquired and B 0 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 and B 0 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.