  3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but
low-resolution image acquisition and highly detailed but slow image
acquisition. Fast imaging is required for targets that move to avoid motion
artefacts. This is in particular difficult for fetal MRI. Spatially independent
upsampling techniques, which are the state-of-the-art to address this problem,
are error prone and disregard contextual information. In this paper we propose
a context-sensitive upsampling method based on a residual convolutional neural
network model that learns organ specific appearance and adopts semantically to
input data allowing for the generation of high resolution images with sharp
edges and fine scale detail. By making contextual decisions about appearance
and shape, present in different parts of an image, we gain a maximum of
structural detail at a similar contrast as provided by high-resolution data. We
experiment on $145$ fetal scans and show that our approach yields an increased
PSNR of $1.25$ $dB$ when applied to under-sampled fetal data \emph{cf.}
baseline upsampling. Furthermore, our method yields an increased PSNR of $1.73$
$dB$ when utilizing under-sampled fetal data to perform brain volume
reconstruction on motion corrupted captured data.
