  Deformable registration is one of the most challenging task in the field of
medical image analysis, especially for the alignment between different
sequences and modalities. In this paper, a non-rigid registration method is
proposed for 3D medical images leveraging unsupervised learning. To the best of
our knowledge, this is the first attempt to introduce gradient loss into
deep-learning-based registration. The proposed gradient loss is robust across
sequences and modals for large deformation. Besides, adversarial learning
approach is used to transfer multi-modal similarity to mono-modal similarity
and improve the precision. Neither ground-truth nor manual labeling is required
during training. We evaluated our network on a 3D brain registration task
comprehensively. The experiments demonstrate that the proposed method can cope
with the data which has non-functional intensity relations, noise and blur. Our
approach outperforms other methods especially in accuracy and speed.
