  Objective: Bloch simulation constitutes an essential part of magnetic
resonance imaging (MRI) development. However, even with the graphics processing
unit (GPU) acceleration, the heavy computational load remains a major
challenge, especially in large-scale, high-accuracy simulation scenarios. This
work aims to develop a deep learning-based simulator to accelerate Bloch
simulation. Approach: The simulator model, called Simu-Net, is based on an
end-to-end convolutional neural network and is trained with synthetic data
generated by traditional Bloch simulation. It uses dynamic convolution to fuse
spatial and physical information with different dimensions and introduces
position encoding templates to achieve position-specific labeling and overcome
the receptive field limitation of the convolutional network. Main Results:
Compared with mainstream GPU-based MRI simulation software, Simu-Net
successfully accelerates simulations by hundreds of times in both traditional
and advanced MRI pulse sequences. The accuracy and robustness of the proposed
framework were verified qualitatively and quantitatively. Besides, the trained
Simu-Net was applied to generate sufficient customized training samples for
deep learning-based T2 mapping and comparable results to conventional methods
were obtained in the human brain. Significance: As a proof-of-concept work,
Simu-Net shows the potential to apply deep learning for rapidly approximating
the forward physical process of MRI and may increase the efficiency of Bloch
simulation for optimization of MRI pulse sequences and deep learning-based
methods.
