Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration. In this work, we therefore develop multiGradICON as a first step towards universal *multimodal* medical image registration. Specifically, we show that 1) we can train a DL registration model that is suitable for monomodal *and* multimodal registration; 2) loss function randomization can increase multimodal registration accuracy; and 3) training a model with multimodal data helps multimodal generalization. Our code and the multiGradICON model are available atthis https URL.
View on arXiv@article{demir2025_2408.00221,
title={ multiGradICON: A Foundation Model for Multimodal Medical Image Registration },
author={ Basar Demir and Lin Tian and Thomas Hastings Greer and Roland Kwitt and Francois-Xavier Vialard and Raul San Jose Estepar and Sylvain Bouix and Richard Jarrett Rushmore and Ebrahim Ebrahim and Marc Niethammer },
journal={arXiv preprint arXiv:2408.00221},
year={ 2025 }
}