发表机构
University of Wisconsin-Madison; Montreal Neurological Institute, McGill University（威斯康星大学麦迪逊分校; 麦吉尔大学蒙特利尔神经学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出统一计算框架，结合表面建模、平滑与统计分析，通过张量形态测量学定位儿童脑皮层灰质变化区域，实现脑表面形状差异检测。
AI 中文摘要
我们提出了一种基于张量的形态测量学的统一计算方法，用于从磁共振图像中检测两个临床组之间的脑表面形状差异。该方法的新颖之处在于，将表面建模、表面数据平滑和统计分析整合到一个连贯统一的数学框架中。大脑皮层具有高度褶皱的二维片状拓扑结构，不同临床组间的皮层局部表面积和曲率可能存在差异，且此类表面形状差异极可能在整个皮层上分布不均。通过计算这些表面指标的差异，可定位出结构差异最显著的区域。为提高信噪比，我们开发了基于拉普拉斯-贝尔特拉米算子显式估计的扩散平滑方法，并将其应用于表面指标。作为示例，我们展示了这种新的基于张量的表面形态测量学如何应用于纵向收集的儿童组脑图像，以定位灰质组织增生与丢失的皮层区域。
英文摘要
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.
Journal refThe proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2003 Vol I 467-473
