Wang Yalin, Chan Tony F, Toga Arthur W, Thompson Paul M
Lab. of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095, USA.
Med Image Comput Comput Assist Interv. 2009;12(Pt 1):337-44. doi: 10.1007/978-3-642-04268-3_42.
Here we introduce multivariate tensor-based surface morphometry using holomorphic one-forms to study brain anatomy. We computed new statistics from the Riemannian metric tensors that retain the full information in the deformation tensor fields. We introduce two different holomorphic one-forms that induce different surface conformal parameterizations. We applied this framework to 3D MRI data to analyze hippocampal surface morphometry in Alzheimer's Disease (AD; 26 subjects), lateral ventricular surface morphometry in HIV/AIDS (19 subjects) and cortical surface morphometry in Williams Syndrome (WS; 80 subjects). Experimental results demonstrated that our method powerfully detected brain surface abnormalities. Multivariate statistics on the local tensors outperformed other TBM methods including analysis of the Jacobian determinant, the largest eigenvalue, or the pair of eigenvalues, of the surface Jacobian matrix.
在此，我们介绍使用全纯一元形式的基于多变量张量的表面形态计量学来研究脑解剖结构。我们从黎曼度量张量计算出新的统计量，这些统计量保留了变形张量场中的全部信息。我们引入两种不同的全纯一元形式，它们会诱导出不同的表面共形参数化。我们将此框架应用于3D MRI数据，以分析阿尔茨海默病（AD；26名受试者）中的海马体表面形态计量学、艾滋病毒/艾滋病（19名受试者）中的侧脑室表面形态计量学以及威廉姆斯综合征（WS；80名受试者）中的皮质表面形态计量学。实验结果表明，我们的方法能够有效地检测出脑表面异常。局部张量上的多变量统计优于其他张量脑形态计量学方法，包括对表面雅可比矩阵的雅可比行列式、最大特征值或一对特征值的分析。
