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）是一种越来越流行的脑MRI和DTI数据组分析方法。分析的主要步骤包括非线性配准，使每个单独的扫描与公共空间对齐，以及随后的统计分析，以确定形态测量差异，或组间纤维结构的差异。本文提出了一种改进TBM非线性配准和统计分析的方法。传统的TBM非线性配准方法是在t1加权MR图像上进行配准，要么只对分割的二维皮质进行配准，要么对整个三维脑图像进行配准，然后对这些区域进行相应的统计分析。迄今为止，这两种方法都不能对整个大脑提供满意的解决方案，因为二维皮质TBM忽略了大脑的其余部分，而三维体积TBM难以匹配皮层，可能无法很好地匹配白质中的神经元纤维结构。在这里，我们描述了一种新的三维体积TBM统计非线性配准算法，该算法结合了皮质匹配和全脑体积三维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出的代价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，我们提出利用变形场的雅可比矩阵以多元方式提供的所有信息，并通过建立推理，使其可以根据体积变化和变形方向进行解释，从而提高TBM统计分析中的检测能力。