  Diffeomorphic deformable multi-modal image registration is a challenging task
which aims to bring images acquired by different modalities to the same
coordinate space and at the same time to preserve the topology and the
invertibility of the transformation. Recent research has focused on leveraging
deep learning approaches for this task as these have been shown to achieve
competitive registration accuracy while being computationally more efficient
than traditional iterative registration methods. In this work, we propose a
simple yet effective unsupervised deep learning-based {\em multi-modal} image
registration approach that benefits from auxiliary information coming from the
gradient magnitude of the image, i.e. the image edges, during the training. The
intuition behind this is that image locations with a strong gradient are
assumed to denote a transition of tissues, which are locations of high
information value able to act as a geometry constraint. The task is similar to
using segmentation maps to drive the training, but the edge maps are easier and
faster to acquire and do not require annotations. We evaluate our approach in
the context of registering multi-modal (T1w to T2w) magnetic resonance (MR)
brain images of different subjects using three different loss functions that
are said to assist multi-modal registration, showing that in all cases the
auxiliary information leads to better results without compromising the runtime.
