  Inter-modality image registration is an critical preprocessing step for many
applications within the routine clinical pathway. This paper presents an
unsupervised deep inter-modality registration network that can learn the
optimal affine and non-rigid transformations simultaneously.
Inverse-consistency is an important property commonly ignored in recent deep
learning based inter-modality registration algorithms. We address this issue
through the proposed multi-task architecture and the new comprehensive
transformation network. Specifically, the proposed model learns a
modality-independent latent representation to perform cycle-consistent
cross-modality synthesis, and use an inverse-consistent loss to learn a pair of
transformations to align the synthesized image with the target. We name this
proposed framework as FIRE due to the shape of its structure. Our method shows
comparable and better performances with the popular baseline method in
experiments on multi-sequence brain MR data and intra-modality 4D cardiac
Cine-MR data.
