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

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution