  Magnetic resonance (MR) image acquisition is an inherently prolonged process,
whose acceleration has long been the subject of research. This is commonly
achieved by obtaining multiple undersampled images, simultaneously, through
parallel imaging. In this paper, we propose the Dual-Octave Network (DONet),
which is capable of learning multi-scale spatial-frequency features from both
the real and imaginary components of MR data, for fast parallel MR image
reconstruction. More specifically, our DONet consists of a series of
Dual-Octave convolutions (Dual-OctConv), which are connected in a dense manner
for better reuse of features. In each Dual-OctConv, the input feature maps and
convolutional kernels are first split into two components (ie, real and
imaginary), and then divided into four groups according to their spatial
frequencies. Then, our Dual-OctConv conducts intra-group information updating
and inter-group information exchange to aggregate the contextual information
across different groups. Our framework provides three appealing benefits: (i)
It encourages information interaction and fusion between the real and imaginary
components at various spatial frequencies to achieve richer representational
capacity. (ii) The dense connections between the real and imaginary groups in
each Dual-OctConv make the propagation of features more efficient by feature
reuse. (iii) DONet enlarges the receptive field by learning multiple
spatial-frequency features of both the real and imaginary components. Extensive
experiments on two popular datasets (ie, clinical knee and fastMRI), under
different undersampling patterns and acceleration factors, demonstrate the
superiority of our model in accelerated parallel MR image reconstruction.
