This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities or prior information of predefined transforms, DeepcomplexMRI takes advantage of the availability of a large number of existing multi-channel groudtruth images and uses them as labeled data to train the deep residual convolutional neural network offline. In particular, a complex convolutional network is proposed to take into account the correlation between the real and imaginary parts of MR images. In addition, the k space data consistency is further enforced repeatedly in between layers of the network. The evaluations on in vivo datasets show that the proposed method has the capability to recover the desired multi-channel images. Its comparison with state-of-the-art method also demonstrates that the proposed method can reconstruct the desired MR images more accurately.

本研究提出了一种名为DeepcomplexMRI的多通道图像重建方法，使用残差复杂卷积神经网络加速并行MR图像采集，并在多层网络中加强了K空间数据一致性，评估结果表明，该方法能够更准确地重建所需的MR图像。

DeepcomplexMRI: 利用深度残差网络，通过复数卷积快速进行并行磁共振成像