High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning. (21st April 2023)

• Record Type: Journal Article
• Authors: Huang, Haitao; Yang, Qinqin; Wang, Jiechao; Zhang, Pujie; Cai, Shuhui; Cai, Congbo
• Is Part Of: Physics in medicine & biology. Volume 68:Number 8(2023)
• Publication Date: 2023-04-21
• Subjects: Bloch simulation -- magnetic resonance imaging -- deep learning -- dynamic convolution -- synthetic data generation; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/acc4a6
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: 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 T 2 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.