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Abstract #0392

Learning-based non-linear registration robust to MRI-sequence contrast

Malte Hoffmann1,2, Benjamin Billot3, Juan Eugenio Iglesias1,2,3,4, Bruce Fischl1,2,4, and Adrian V Dalca1,2,4
1Department of Radiology, Harvard Medical School, Boston, MA, United States, 2Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, 3Centre for Medical Image Computing, University College London, London, United Kingdom, 4Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States

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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