Diffeomorphic Cortical Alignment via Direct Warping of Streamline Endpoints

TL;DR

This paper introduces a novel cortical surface registration method that aligns surfaces by directly warping white-matter fiber-tract endpoints, improving tract-level correspondence and robustness over existing methods.

Contribution

It presents a connectivity-based registration approach operating directly on fiber-tract endpoints, ensuring diffeomorphic warps and optimizing fiber bundle matching.

Findings

Achieves higher connectivity overlap coefficients on major fiber bundles.

Demonstrates stronger robustness across grid resolutions.

Outperforms state-of-the-art methods like ENCORE and MSMAll.

Abstract

Cortical surface registration is often driven by local geometric descriptors (e.g., sulcal depth and curvature). While this approach achieves geometric correspondence, it neglects the long-range wiring constraints imposed by white-matter anatomy. Diffusion MRI tractography offers these crucial constraints; however, prior connectivity-informed pipelines typically align precomputed connectivity matrices, making the optimization highly sensitive to connectivity estimation and its resolution. In this paper, we introduce a novel connectivity-based surface registration method that aligns cortical surfaces by operating directly on white-matter fiber-tract endpoints. We model tract endpoints as a point cloud on the product manifold $\Omega \times \Omega$, where $\Omega$ represents the spherical domain of the inflated cortical hemispheres. Our alignment method iteratively (i) computes a small diffeomorphic warp for $\Omega$ by minimizing connectivity mismatch, and (ii) updates the endpoints based on this warp. The method relies on a geometric framework that ensures output warps are diffeomorphisms and has a final goal that optimizes the matching of well-known fiber bundles. Experiments on Human Connectome Project (HCP) data demonstrate improved tract-level correspondence, achieving higher connectivity-level overlap coefficients on major fiber bundles and stronger robustness across grid resolutions for $\Omega$ compared to state-of-the-art methods such as ENCORE and MSMAll.