Foundation-Guided Representation Alignment for Multimodal Medical Image Registration

Multimodal medical image registration is crucial for diagno-sis and treatment planning, yet remains challenging due to significantappearance differences between modalities such as MR and CT. Themodality gap requires that the encoder in learning-based registrationmethods has cross-modal capabilities. Meanwhile, recent vision founda-tion models (VFMs) have demonstrated strong capabilities in extractingrich cross-modal representations that help bridge such modality gaps.Inspired by this, we propose a foundation-guided multimodal represen-tation alignment framework, which transfers the rich cross-modal knowl-edge from a pretrained VFM to the registration encoder. We introduce anovel Data ReAssembly strategy to transform volumetric medical imagesinto VFM input while preserving spatial information. Additionally, weimplement a Hierarchical Representation Fusion module to dynamicallyintegrate multi-level features from the VFM, yielding semantically andstructurally rich representations. Extensive experiments on two publicMR–CT datasets for brain and abdominal imaging demonstrate the ef-fectiveness of the proposed method compared with existing approaches.The code is available at https://github.com/MeggieGuo/FGRA-Reg.