Computational neuroanatomy of aging using shape analysis

描述(由申请人提供)： 该项目的目标是继续并显著扩展我们在脑磁共振图像分析方法方面的工作，重点是可变形配准及其在衰老纵向研究中的脑图像形态计量分析和空间归一化中的应用，并使用这些方法开发基于图像的轻度认知障碍和阿尔茨海默病早期诊断工具。个体形态特征的量化是通过形状变换实现的，即使解剖学模板适应被研究个体的形态的空间变换。形状变换是解剖学的一种非常详细的数学表示，用于结构和功能图像的个体间比较和空间归一化。该项目的总体目标是解决当前技术的三个限制，这三个限制在各自的具体目标中进行了处理。具体地说，我们建议1)开发和验证用于从MR图像中获得丰富的图像表示的方法，这将允许不同的大脑区域具有不同的形态特征，从而促进用于确定解剖上准确的形状变换的自动化算法，2)开发和验证用于从纵向图像数据中发现4维形状变换的方法，其中第四维表示时间；通过将时间平滑约束合并到不同时间点的形状转换的估计中，该方法将显著减少测量误差；3)开发和验证基于Aim 2的形状转换的形态表示，该形态表示将根据解剖模板的形状转换来表示个体的解剖；以及4)将这些方法应用于巴尔的摩老龄化纵向研究，以验证我们的假设，即由于形态测量的改进的准确性，使用MR图像的早期检测的敏感性和特异性将显著提高，开发一种基于高维图像的模式分类方法，用于阿尔茨海默病的早期诊断。

DESCRIPTION (provided by applicant): The goal of this project is to continue and significantly expand our work on image analysis methods for brain magnetic resonance images, with emphasis on deformable registration and its application to morphometric analysis and spatial normalization of brain images in a longitudinal study of aging, and the use of these methods to develop an image-based early diagnostic tool for mild cognitive impairment and Alzheimer's Disease. Quantification of individual morphometric characteristics is achieved via a shape transformation, i.e. a spatial transformation that adapts a template of anatomy to the morphology of the individual under study. The shape transformation is a very detailed mathematical representation of anatomy, and is used for inter-individual comparisons and spatial normalization of structural and functional images. The overall goal of this project is to address three limitations of current technology, which are treated in the respective specific aims. Specifically we propose to 1) develop and validate a methodology for obtaining a rich image representation from MR images, which will allow for different brain regions to have distinctive morphological signatures, thereby facilitating automated algorithms for determining anatomically accurate shape transformations, 2) develop and validate a methodology for finding 4-dimensional shape transformations from longitudinal image data, with the fourth dimension representing time; this methodology will significantly reduce measurement error by incorporating temporal smoothness constrains into the estimation of the shape transformation at different time-points, 3) develop and validate a morphological representation based on the shape transformation of Aim 2, which will represent an individual's anatomy in terms of a shape transformation of an anatomical template, and a residual image that captures information that is not captured by the shape transformation, and 4) to apply these methods to the Baltimore Longitudinal Study of Aging, in order to test our hypothesis that sensitivity and specificity of early detection of cognitive decline using MR images will be significantly improved by the new technology, because of improved accuracy in morphologic measurements, and to develop a high-dimensionality image-based pattern classification method for early diagnosis of Alzheimer's Disease.