  Prior work on the Image Quality Transfer on Diffusion MRI (dMRI) has shown
significant improvement over traditional interpolation methods. However, the
difficulty in obtaining ultra-high resolution Diffusion MRI scans poses a
problem in training neural networks to obtain high-resolution dMRI scans. Here
we hypothesise that the inclusion of structural MRI images, which can be
acquired at much higher resolutions, can be used as a guide to obtaining a more
accurate high-resolution dMRI output. To test our hypothesis, we have
constructed a novel framework that incorporates structural MRI scans together
with dMRI to obtain high-resolution dMRI scans. We set up tests which evaluate
the validity of our claim through various configurations and compare the
performance of our approach against a unimodal approach. Our results show that
the inclusion of structural MRI scans do lead to an improvement in
high-resolution image prediction when T1w data is incorporated into the model
input.
