title: Hyperbolic harmonic brain surface registration with curvature-based landmark matching.
abstract: Brain Cortical surface registration is required for inter-subject studies of functional and anatomical data. Harmonic mapping has been applied for brain mapping, due to its existence, uniqueness, regularity and numerical stability. In order to improve the registration accuracy, sculcal landmarks are usually used as constraints for brain registration. Unfortunately, constrained harmonic mappings may not be diffeomorphic and produces invalid registration. This work conquer this problem by changing the Riemannian metric on the target cortical surface o a hyperbolic metric, so that the harmonic mapping is guaranteed to be a diffeomorphism while the landmark constraints are enforced as boundary matching condition. The computational algorithms are based on the Ricci flow method and yperbolic heat diffusion. Experimental results demonstrate that, by changing the Riemannian metric, the registrations are always diffeomorphic, with higher qualities in terms of landmark alignment, curvature matching, area distortion and overlapping of region of interests.
authors.forename: Rui
authors.initials: R
authors.lastname: Shi
authors.forename: Wei
authors.initials: W
authors.lastname: Zeng
authors.forename: Zhengyu
authors.initials: Z
authors.lastname: Su
authors.forename: Yalin
authors.initials: Y
authors.lastname: Wang
authors.forename: Hanna
authors.initials: H
authors.lastname: Damasio
authors.forename: Zhonglin
authors.initials: Z
authors.lastname: Lu
authors.forename: Shing-Tung
authors.initials: ST
authors.lastname: Yau
authors.forename: Xianfeng
authors.initials: X
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000033:anatomy & histology*; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique