  Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to
characterise the microstructure of the nervous tissue, e.g. to delineate brain
white matter connections in a non-invasive manner via fibre tracking. Magnetic
Resonance Imaging (MRI) in high spatial resolution would play an important role
in visualising such fibre tracts in a superior manner. However, obtaining an
image of such resolution comes at the expense of longer scan time. Longer scan
time can be associated with the increase of motion artefacts, due to the
patient's psychological and physical conditions. Single Image Super-Resolution
(SISR), a technique aimed to obtain high-resolution (HR) details from one
single low-resolution (LR) input image, achieved with Deep Learning, is the
focus of this study. Compared to interpolation techniques or sparse-coding
algorithms, deep learning extracts prior knowledge from big datasets and
produces superior MRI images from the low-resolution counterparts. In this
research, a deep learning based super-resolution technique is proposed and has
been applied for DW-MRI. Images from the IXI dataset have been used as the
ground-truth and were artificially downsampled to simulate the low-resolution
images. The proposed method has shown statistically significant improvement
over the baselines and achieved an SSIM of $0.913\pm0.045$.
