Hyperbolic harmonic brain surface registration with curvature-based landmark matching.

Rui Shi, Wei Zeng, Zhengyu Su, Yalin Wang, Hanna Damasio, Zhonglin Lu, Shing Tung Yau, Xianfeng Gu

Research output: Contribution to journalArticle

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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.

Original languageEnglish (US)
Pages (from-to)159-170
Number of pages12
JournalInformation processing in medical imaging : proceedings of the ... conference
Volume23
Publication statusPublished - 2013

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ASJC Scopus subject areas

  • Medicine(all)

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