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The loss function of an unsupervised multimodal image registration frameworkhas two terms, i.e., a metric for similarity measure and regularization. In thedeep learning era, researchers proposed many approaches to automatically learnthe similarity metric, which has been shown effective in improving registrationperformance. However, for the regularization term, most existing multimodalregistration approaches still use a hand-crafted formula to impose artificialproperties on the estimated deformation field. In this work, we propose aunimodal cyclic regularization training pipeline, which learns task-specificprior knowledge from simpler unimodal registration, to constrain thedeformation field of multimodal registration. In the experiment of abdominalCT-MR registration, the proposed method yields better results over conventionalregularization methods, especially for severely deformed local regions.

Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks