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 target 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 methods also demonstrates that the proposed method can reconstruct the desired MR images more accurately.

中文翻译：

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DeepcomplexMRI：利用深残差网络进行复杂卷积的快速并行MR成像。

本文提出了一种多通道图像重建方法，称为DeepcomplexMRI，以利用残差复杂卷积神经网络加速并行MR成像。与大多数现有的依靠线圈灵敏度或预先定义的变换的先验信息的工作不同，DeepcomplexMRI利用大量现有的多通道地物图像的可用性并将其用作目标数据来训练深度残积卷积神经网络离线。特别地，提出了复杂的卷积网络以考虑MR图像的实部和虚部之间的相关性。另外，在网络的各层之间进一步重复地加强了k空间数据的一致性。对体内数据集的评估表明，所提出的方法具有恢复所需多通道图像的能力。它与最新技术方法的比较还表明，该方法可以更准确地重建所需的MR图像。