We propose an unsupervised contrastive representation learning framework for deformable and diffeomorphic multi-modality MR image registration. The proposed deep network and data-driven objective function yield improved registration performance in terms of anatomical volume overlap over several previous hand-crafted objectives such as Mutual Information and others. For fair comparison, our experiments train all methods over the entire range of a key registration hyperparameter controlling deformation smoothness using conditional registration hypernetworks. T1w and T2w brain MRI registration improvements are presented across a large cohort of 1041 high-field 3T research-grade acquisitions while maintaining comparable deformation smoothness and invertibility characteristics to previous methods.
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