Statistical Methods for Brain Image Registration and Tensor-Based Morphometry

脑图像配准和基于张量的形态测量的统计方法

• 批准号: 8115254
• 负责人: Natasha Lepore
• 金额: $ 27.45万
• 依托单位: CHILDREN'S HOSPITAL OF LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-15 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8115254
• 关键词: Accounting; Algorithms; Anatomy; Brain; Brain imaging; Code; Computer Analysis; Data; Detection; Diffusion; Diffusion Magnetic Resonance Imaging; Fiber; Goals; Growth; Image; Individual; Joints; Liquid substance; Magnetic Resonance Imaging; Maps; Methods; Morphology; Multivariate Analysis; Neurologic; Neurons; Pharmaceutical Preparations; Research; Rest; Scanning; Solutions; Statistical Methods; Structure; Surface; Time; Toxic effect; Variant; Weight; base; brain volume; comparison group; cost; design; disease diagnosis; image registration; imaging modality; improved; insight; interest; morphometry; tool; white matter

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.

描述（由申请人提供）：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。分析的主要步骤包括非线性配准，将每个单独的扫描对齐到公共空间，以及随后的统计分析，以确定形态差异或组间纤维结构的差异。在这里，我们提出了一种改进 TBM 非线性配准和统计分析的方法。 TBM 的传统非线性配准是在 T1 加权 MR 图像上进行的，可以单独在分段的 2D 皮质上进行，也可以在整个 3D 大脑图像上进行，然后对这些域进行相应的统计分析。迄今为止，这两种选择都无法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 难以匹配皮质，并且可能无法很好地匹配白质中的神经元纤维结构。在这里，我们描述了一种用于 3D 体积 TBM 的新统计非线性配准算法，该算法结合了皮质匹配与全脑体积上 3D 统计流体配准的优点。此外，我们的目标是通过在从扩散张量成像数据导出的成本函数中添加扩散张量之间的距离来准确匹配底层纤维结构。 此外，我们建议通过以多元方式使用变形场雅可比行列式中的所有可用信息，并建立推理，以便可以用体积和体积来解释它，从而提高 TBM 统计分析中的检测能力。变形的变化和方向。 公共健康相关性：我们通过两种方式改进基于张量的形态测量法进行组分析，首先将皮质、结构 MR 和 DTI 信息用于组合的皮质和统计流体配准算法，其次使用多元统计方法分析完整的雅可比行列式矩阵以及其中的体积和方向信息。