multiGradICON: A Foundation Model for Multimodal Medical Image Registration
This addresses the need for universal registration tools in medical imaging, though it is incremental as it builds on prior work like uniGradICON.
The paper tackles the problem of multimodal medical image registration by developing multiGradICON, a deep learning model that works for both monomodal and multimodal tasks, achieving improved accuracy through loss function randomization and multimodal training.
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.