High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing units (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. Here we present a framework based on convolutional neural networks to build a high-efficient 2D Bloch simulator, termed Simu-Net. Compared to the mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by over hundreds of times in three MRI pulse sequences. The accuracy and robustness of the proposed framework were also verified qualitatively and quantitatively. 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. As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the Bloch equation as a forward physical process.