  Multimodal image registration (MIR) is a fundamental procedure in many
image-guided therapies. Recently, unsupervised learning-based methods have
demonstrated promising performance over accuracy and efficiency in deformable
image registration. However, the estimated deformation fields of the existing
methods fully rely on the to-be-registered image pair. It is difficult for the
networks to be aware of the mismatched boundaries, resulting in unsatisfactory
organ boundary alignment. In this paper, we propose a novel multimodal
registration framework, which leverages the deformation fields estimated from
both: (i) the original to-be-registered image pair, (ii) their corresponding
gradient intensity maps, and adaptively fuses them with the proposed gated
fusion module. With the help of auxiliary gradient-space guidance, the network
can concentrate more on the spatial relationship of the organ boundary.
Experimental results on two clinically acquired CT-MRI datasets demonstrate the
effectiveness of our proposed approach.
