  Magnetic resonance imaging (MRI) is a widely used medical imaging modality.
However, due to the limitations in hardware, scan time, and throughput, it is
often clinically challenging to obtain high-quality MR images. In this article,
we propose a method of using artificial intelligence to expand the channel to
achieve the goal of generating the virtual coils. The main characteristic of
our work is utilizing dummy variable technology to expand/extrapolate the
receive coils in both image and k-space domains. The high-dimensional
information formed by channel expansion is used as the prior information to
improve the reconstruction effect of parallel imaging. Two main components are
incorporated into the network design, namely variable augmentation technology
and sum of squares (SOS) objective function. Variable augmentation provides the
network with more high-dimensional prior information, which is helpful for the
network to extract the deep feature information of the data. The SOS objective
function is employed to solve the deficiency of k-space data training while
speeding up convergence. Experimental results demonstrated its great potentials
in super-resolution of MR images and accelerated parallel imaging
reconstruction.
