基于深度递归级联卷积神经网络的并行磁共振成像方法

快速磁共振成像是磁共振研究领域重要的课题之一.随着大数据和深度学习的兴起，神经网络成为快速磁共振技术的重要方法.然而网络性能表现和网络参数量之间较难取得平衡，且对于多通道数据重建的并行成像问题，相关研究较少.本文构建了一种深度递归级联卷积神经网络结构，用于处理并行成像问题.这种网络结构在减少网络参数量的同时，能够尽可能地提高网络的表达能力，提高网络重建的精确度.实验结果表明，相较于传统并行成像方法，通过训练好的神经网络对欠采样磁共振数据进行重建，可以得到更准确的重建结果，且重建时间大大缩短.

A Deep Recursive Cascaded Convolutional Network for Parallel MRI

Fast magnetic resonance imaging (MRI) has been attracting more and more research interests in recent years. With the emergence of big data and development of advanced deep learning algorithms, neural network has become a common and effective tool for image reconstruction in fast MRI. One main challenge to the deep learning-based methods for fast MRI reconstruction is the trade-off between the network performance and the network capacity. Few previous studies have used the deep learning-based methods in parallel imaging. In this work, a deep recursive cascaded convolutional network (DRCCN) architecture was designed for parallel MRI, with reduced number of network parameters while maintaining a satisfactory performance. The experimental results demonstrated that, compared to the classical methods, image reconstruction with the well-trained DRCCN networks were more accurate and less time consuming.