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Multi-modality medical images can provide relevant or complementaryinformation for a target (organ, tumor or tissue). Registering multi-modalityimages to a common space can fuse these comprehensive information, and bringconvenience for clinical application. Recently, neural networks have beenwidely investigated to boost registration methods. However, it is stillchallenging to develop a multi-modality registration network due to the lack ofrobust criteria for network training. In this work, we propose a multi-modalityregistration network (MMRegNet), which can perform registration betweenmulti-modality images. Meanwhile, we present spatially encoded gradientinformation to train MMRegNet in an unsupervised manner. The proposed networkwas evaluated on MM-WHS 2017. Results show that MMRegNet can achieve promisingperformance for left ventricle cardiac registration tasks. Meanwhile, todemonstrate the versatility of MMRegNet, we further evaluate the method with aliver dataset from CHAOS 2019. Source code will be releasedpublicly\footnote{https://github.com/NanYoMy/mmregnet} once the manuscript isaccepted.

Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information