  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
at https://github.com/uncbiag/uniGradICON.
