Multi Modal Registration

Multi-modal registration aims to align images from different sources (e.g., MRI, ultrasound, microscopy) to integrate complementary information, crucial for tasks like image-guided surgery and disease diagnosis. Current research emphasizes developing robust and efficient methods, often employing deep learning architectures such as variational autoencoders, and focusing on techniques like keypoint matching, information-theoretic metrics (e.g., mutual information), and discriminator-free image translation to handle modality discrepancies. These advancements improve accuracy, speed, and interpretability, impacting various fields by enabling more precise analyses and improved clinical workflows.