  Spatial resolution is a critical imaging parameter in magnetic resonance
imaging (MRI). Acquiring high resolution MRI data usually takes long scanning
time and would subject to motion artifacts due to hardware, physical, and
physiological limitations. Single image super-resolution (SISR), especially
that based on deep learning techniques, is an effective and promising
alternative technique to improve the current spatial resolution of magnetic
resonance (MR) images. However, the deeper network is more difficult to be
effectively trained because the information is gradually weakened as the
network deepens. This problem becomes more serious for medical images due to
the degradation of training examples. In this paper, we present a novel channel
splitting and serial fusion network (CSSFN) for single MR image
super-resolution. Specifically, the proposed CSSFN network splits the
hierarchical features into a series of subfeatures, which are then integrated
together in a serial manner. Thus, the network becomes deeper and can deal with
the subfeatures on different channels discriminatively. Besides, a dense global
feature fusion (DGFF) is adopted to integrate the intermediate features, which
further promotes the information flow in the network. Extensive experiments on
several typical MR images show the superiority of our CSSFN model over other
advanced SISR methods.
