Brain MRI Registration

Brain MRI registration aims to precisely align different brain MRI scans, enabling accurate comparison and analysis across individuals or modalities. Current research heavily emphasizes deep learning approaches, employing architectures like U-Nets, Transformers (e.g., TransMorph), and novel state-space models (e.g., MambaMorph) to achieve robust and efficient registration, often incorporating keypoint detection or contrastive learning strategies. These advancements improve accuracy, particularly for challenging scenarios like multi-modal registration and large initial misalignments, impacting various fields including clinical diagnosis, neurosurgical planning, and longitudinal brain studies. The development of foundational models and large-scale datasets further enhances the generalizability and performance of these methods.