Statistical Methods for Brain Image Registration and Tensor-Based Morphometry

描述(申请人提供)：基于张量的形态测量(TBM)是一种日益流行的脑MRI和DTI数据分组分析方法。分析中的主要步骤包括将每个单独的扫描与公共空间对齐的非线性配准，以及随后的统计分析，以确定形态测量学差异或组之间纤维结构的差异。在这里，我们提出了一种改进的方法，用于改进TBM的非线性配准和统计分析。传统的TBM非线性配准是对T1加权的MR图像进行配准，或者只对分割的2D大脑皮层进行配准，或者对整个3D大脑图像进行配准，然后对这些域进行相应的统计分析。到目前为止，这两种方法都不能为整个大脑提供令人满意的解决方案，因为2D皮质TBM忽略了大脑的其余部分，而3D体积TBM难以与皮质匹配，并且可能与白质中的神经纤维结构不太匹配。在这里，我们描述了一种新的3D体积TBM的统计非线性配准算法，该算法结合了皮质匹配的优点和3D统计流体配准的优点。此外，我们的目标是通过在扩散张量成像数据得到的成本函数中添加扩散张量之间的距离来精确匹配潜在的纤维结构。此外，我们还建议以多变量的方式利用形变场雅可比中的所有信息，并通过建立推理以使其既可以用体积变化来解释，也可以用变形方向来解释，从而提高了在TBM统计分析中的检测能力。 公共卫生相关性：我们从两个方面改进了基于张量的形态测量法用于群体分析，第一是将皮质、结构MR和DTI信息结合到皮质和统计流体配准算法中，第二是使用多元统计方法分析整个雅可比矩阵以及其中的体积和方向信息。

DESCRIPTION (provided by applicant): Tensor-Based Morphometry (TBM) is an increasingly popular method for group analysis of brain MRI and DTI data. The main steps in the analysis consist of a nonlinear registration to align each individual scan to a common space, and a subsequent statistical analysis to determine morphometric differences, or difference in fiber structure between groups. Here, we propose a method to improve both the nonlinear registration and statistical analyses for TBM. The traditional nonlinear registration for TBM is performed on T1-weighted MR images, either on the seg- mented 2D cortices alone, or on the whole 3D brain images, followed by corresponding statistical analyses on those domains. To date, neither option provides a satisfactory solution for the entire brain, since 2D cortical TBM ignores the rest of the brain, while 3D volumetric TBM has difficulty matching the cortex and may not match well neuronal fiber structures in the white matter. Here we describe a new statistical nonlinear registration algorithm for 3D volumetric TBM that combines the advantages of cortical matching to those of a 3D statistical fluid registration on the whole brain volume. In addition, we aim to match the underlying fiber structure accurately by adding a distance between diffusion tensors in the cost function derived from diffusion tensor imaging data. Furthermore, we propose to improve the detection power in the statistical analysis in TBM by using all the information available in the Jacobian of the deformation field in a multivariate fashion, and by setting up the inference so that it can be interpreted in terms of both volumetric changes and directions of deformation. PUBLIC HEALTH RELEVANCE: We improve on Tensor-Based Morphometry for group analysis in two ways, first by using cortical, structural MR and DTI information into a combined cortical and statistical fluid registration algorithm, and secondly by using multivariate statistical methods to analyze the full Jacobian matrix and the volumetric and directional information in it.