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Diffeomorphic Sulcal Shape Analysis for Cortical Surface Registration.

Shantanu H Joshi¹, Ryan P Cabeen, Anand A Joshi

¹Laboratory of Neuro Imaging, University of California Los Angeles Los Angeles, California, 90095, USA.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition

|November 16, 2010

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This study introduces a novel framework for creating human cortical sulcal shape atlases using continuous curves and Riemannian geometry. This method significantly improves the accuracy of surface atlases for brain anatomy analysis.

Area of Science:

Neuroimaging
Computational Anatomy
Differential Geometry

Background:

Accurate representation of human cortical anatomy is crucial for understanding brain function and disease.
Existing methods for sulcal pattern analysis often lack robustness and intrinsic shape properties.
Developing intrinsic frameworks for shape analysis is essential for comparative neuroanatomy.

Purpose of the Study:

To present an intrinsic framework for constructing sulcal shape atlases of the human cortex.
To analyze sulcal and gyral patterns using continuous open curves in 3D space.
To improve surface atlas construction through advanced shape analysis techniques.

Main Methods:

Representing sulcal and gyral patterns as continuous open curves in ℝ(3).

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Utilizing a Riemannian L(2) metric on the shape manifold for curve matching.

Computing geodesics analytically in the shape space and via optimization in quotient spaces (modulo rigid transformations and reparameterizations).

Integrating an elastic shape model into a surface registration framework.

Main Results:

The proposed Riemannian L(2) metric demonstrates desirable properties for matching sulcal shapes.
Analytical and optimized geodesic computations provide robust shape comparisons.
Integration into a surface registration framework for 176 subjects yielded considerable improvements in atlas quality.

Conclusions:

The intrinsic framework offers a powerful and geometrically sound approach for sulcal shape analysis.
This method enhances the construction of accurate and detailed human cortical surface atlases.
The findings contribute to advancements in computational neuroanatomy and brain mapping.