We introduce a novel strategy for learning deformable registration without acquired imaging data, producing networks robust to MRI contrast. While classical methods repeat an optimization for every new image pair, learning-based methods require retraining for accurate registration of unseen image types. To address these inefficiencies, we leverage a generative strategy for diverse synthetic label maps and images that enable training powerful networks that generalize to a broad spectrum of MRI contrasts. We demonstrate robust and accurate registration of arbitrary unseen MRI contrasts with a single network, thereby eliminating the need for retraining models.
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