  Tracking microsctructural changes in the developing brain relies on accurate
inter-subject image registration. However, most methods rely on either
structural or diffusion data to learn the spatial correspondences between two
or more images, without taking into account the complementary information
provided by using both. Here we propose a deep learning registration framework
which combines the structural information provided by T2-weighted (T2w) images
with the rich microstructural information offered by diffusion tensor imaging
(DTI) scans. We perform a leave-one-out cross-validation study where we compare
the performance of our multi-modality registration model with a baseline model
trained on structural data only, in terms of Dice scores and differences in
fractional anisotropy (FA) maps. Our results show that in terms of average Dice
scores our model performs better in subcortical regions when compared to using
structural data only. Moreover, average sum-of-squared differences between
warped and fixed FA maps show that our proposed model performs better at
aligning the diffusion data.
