  Magnetic resonance imaging has been widely applied in clinical diagnosis,
however, is limited by its long data acquisition time. Although imaging can be
accelerated by sparse sampling and parallel imaging, achieving promising
reconstruction images with a fast reconstruction speed remains a challenge.
Recently, deep learning approaches have attracted a lot of attention for its
encouraging reconstruction results but without a proper interpretability. In
this letter, to enable high-quality image reconstruction for the parallel
magnetic resonance imaging, we design the network structure from the
perspective of sparse iterative reconstruction and enhance it with the residual
structure. The experimental results of a public knee dataset show that compared
with the optimization-based method and the latest deep learning parallel
imaging methods, the proposed network has less error in reconstruction and is
more stable under different acceleration factors.
