用张量方法分析脑表面形态差异，定位儿童脑灰质增减区域。
Tensor-based Brain Surface Modeling and Analysis
- 基于张量的统一框架建模脑表面，融合形变、平滑与统计分析。
- 在儿童纵向数据中定位灰质增长/流失的快速变化区域。
- 适合神经影像研究者，尤其关注发育或退行性脑病研究。
我们提出一种统一的计算方法，通过磁共振成像检测两组临床人群间脑表面形态差异。大脑皮层具有二维高度折叠的拓扑结构，不同群体间局部表面积和曲率可能存在差异，且这些差异并非均匀分布。通过计算表面度量的变化，可精确定位结构变化最显著的区域。为提高信噪比，我们基于拉普拉斯-贝尔特拉米算子显式估计，发展并应用了扩散平滑方法于表面度量。以儿童纵向采集的数据为例，演示了该张量化表面形态计量方法在定位灰质组织生长与损失区域中的应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。