DeepControl: 2DRF pulses facilitating B1+ inhomogeneity and B0 off-resonance compensation in vivo at 7 T

Abstract

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.

Keywords: 2DRF pulses; 7 T; artificial intelligence; deep learning; optimal control.