Problem
Research questions and friction points this paper is trying to address.
brain surface shape
magnetic resonance images
surface metrics
Laplace-Beltrami operator
cortical regions
Tensor-based Brain Surface Modeling and Analysis
🤖 AI Summary
本文提出一种基于张量的计算方法，通过结合表面建模、数据平滑和统计分析来检测两组临床样本间脑表面形状差异。
📝 Abstract
We present a unified computational approach to tensor-based morphometry in detecting the brain surface shape differences between two clinical groups based on magnetic resonance images. Our approach is novel in a sense that we combined surface modeling, surface data smoothing and statistical analysis in a coherent unified mathematical framework. The cerebral cortex has the topology of a 2D highly convoluted sheet. Between two different clinical groups, the local surface area and curvature of the cortex may differ. It is highly likely that such surface shape differences are not uniform over the whole cortex. By computing how such surface metrics differ, the regions of the most rapid structural differences can be localized. To increase the signal to noise ratio, diffusion smoothing based on the explicit estimation of Laplace-Beltrami operator has been developed and applied to the surface metrics. As an illustration, we demonstrate how this new tensor-based surface morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children.
Innovation
Methods, ideas, or system contributions that make the work stand out.
tensor-based morphometry
unified mathematical framework
Laplace-Beltrami operator
surface metrics
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