  Parallel imaging has been an essential technique to accelerate MR imaging.
Nevertheless, the acceleration rate is still limited due to the ill-condition
and challenges associated with the undersampled reconstruction. In this paper,
we propose a model-based convolutional de-aliasing network with adaptive
parameter learning to achieve accurate reconstruction from multi-coil
undersampled k-space data. Three main contributions have been made: a
de-aliasing reconstruction model was proposed to accelerate parallel MR imaging
with deep learning exploring both spatial redundancy and multi-coil
correlations; a split Bregman iteration algorithm was developed to solve the
model efficiently; and unlike most existing parallel imaging methods which rely
on the accuracy of the estimated multi-coil sensitivity, the proposed method
can perform parallel reconstruction from undersampled data without explicit
sensitivity calculation. Evaluations were conducted on \emph{in vivo} brain
dataset with a variety of undersampling patterns and different acceleration
factors. Our results demonstrated that this method could achieve superior
performance in both quantitative and qualitative analysis, compared to three
state-of-the-art methods.
