High Performance Groupwise Cortical Surface Registration with Multimodal Surface Matching

Improving the accuracy of cortical surface registration while preserving individual characteristics is a difficult task, since cortical shape varies significantly across different individuals. In this paper we propose an extension of Multimodal Surface Matching (MSM) towards a hierarchical groupwise framework to address these challenges. Central to the success of this framework has been re-implementation of MSM to improve its efficiency. Experiments show that the proposed method supports unbiased and efficient alignment of large groups of up to 250 subjects, while significantly improving the alignment of cortical folds, relative to alignment to a single population average template. This implies significant advantages with regards to the search for subtle cortical signatures of cognition and disease. The code can be found at https://github.com/rbesenczi/newMSM .