  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.
