Diffeomorphic Cortical Alignment via Direct Warping of Streamline Endpoints

• URL: http://arxiv.org/abs/2605.16742v1
• Date: Sat, 16 May 2026 01:46:32 GMT
• Title: Diffeomorphic Cortical Alignment via Direct Warping of Streamline Endpoints
• Abstract summary: Cortical surface registration is often driven by local geometric descriptors.<n>We introduce a novel connectivity-based surface registration method that aligns surfaces by operating directly on white-matter fiber-tract endpoints.<n> Experiments on Human Connectome Project data demonstrate improved tract-level correspondence.
• Score: 9.506507471747549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $Ω\times Ω$, where $Ω$ represents the spherical domain of the inflated cortical hemispheres. Our alignment method iteratively (i) computes a small diffeomorphic warp for $Ω$ 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 $Ω$ compared to state-of-the-art methods such as ENCORE and MSMAll.

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