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）：基于张量的形态计量学（TBM）是越来越流行的大脑MRI和DTI数据分析的方法。分析的主要步骤包括非线性注册，以使每个单独的扫描与公共空间保持一致，以及随后的统计分析以确定组之间的形态计量学差异或纤维结构之间的差异。在这里，我们提出了一种改善TBM的非线性注册和统计分析的方法。 TBM的传统非线性注册是在T1加权MR图像上进行的，要么单独使用2D皮层，要么在整个3D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，这两个选项都为整个大脑提供了令人满意的解决方案，因为2D皮质TBM忽略了其余的大脑，而3D体积TBM难以匹配皮质，并且可能与白质中的神经元纤维结构匹配。在这里，我们描述了3D体积TBM的一种新的统计非线性注册算法，该算法将皮质匹配的优势与整个大脑体积上的3D统计流体注册的优势结合在一起。此外，我们的目标是通过在扩散张量成像数据中得出的成本函数中的扩散量之间的距离来准确匹配基础纤维结构。 此外，我们建议通过以多元方式使用变形场的雅各布式中可用的所有信息来提高TBM中统计分析中的检测能力，并通过设置推理，以便可以通过体积变化和变形方向来解释它。 公共卫生相关性：我们以两种方式改进了基于张量的形态计量学，首先是将皮质，结构的MR和DTI信息用于组合的皮质和统计流体注册算法，其次，其次是使用多变量统计方法来分析完整的Jacobian Matrix和PollumeTric和Directional和方向信息。