  Magnetic resonance (MR) images exhibit various contrasts and appearances
based on factors such as different acquisition protocols, views, manufacturers,
scanning parameters, etc. This generally accessible appearance-related side
information affects deep learning-based undersampled magnetic resonance imaging
(MRI) reconstruction frameworks, but has been overlooked in the majority of
current works. In this paper, we investigate the use of such side information
as normalisation parameters in a convolutional neural network (CNN) to improve
undersampled MRI reconstruction. Specifically, a Side Information-Guided
Normalisation (SIGN) module, containing only few layers, is proposed to
efficiently encode the side information and output the normalisation
parameters. We examine the effectiveness of such a module on two popular
reconstruction architectures, D5C5 and OUCR. The experimental results on both
brain and knee images under various acceleration rates demonstrate that the
proposed method improves on its corresponding baseline architectures with a
significant margin.
