To extend the VMambaMorph framework for multi-modal image registration beyond MR-CT cases, several adaptations can be considered. Firstly, incorporating additional modalities such as PET, MRI, or ultrasound would require adapting the network architecture to handle the diverse characteristics of these modalities. This could involve modifying the feature extraction module to extract relevant features from different modalities effectively. Additionally, incorporating fusion strategies to combine information from multiple modalities could enhance the registration performance. Utilizing attention mechanisms or cross-modal learning techniques can help the network learn to align features across different modalities. Moreover, data augmentation techniques specific to each modality can be employed to enhance the model's ability to generalize across diverse imaging data.