Automated 3D Analysis of Cortical Geometry in MR Images

描述(由申请人提供)：表征人脑结构和其功能之间的关系是当今神经科学最重要的目标之一。医学成像已经被用来通过使用解剖学和生理学成像方法来获得对这种关系的重要新见解。尽管最近取得了重大进展，但目前的方法学仍然受到大脑皮层详细分割、几何分析和标记的自动化方法的限制。拟议研究的主要目标是开发全自动方法，在体积磁共振(MR)图像中找到大脑皮层并在数学上表示，并使用皮质几何图形的详细统计分析自动识别和标记大脑皮层上的主要脑沟和脑回。具体地说，我们建议1)开发和验证从体积MR图像中寻找和数学表示大脑皮层的方法；2)开发和验证计算皮质形状和体积的区域测量的方法；3)开发和验证脑沟和GRYI的自动标记；以及4)研究正常衰老中的皮质变异性和体积变化。所有方法都将使用计算机幻影和人工活体真相模型进行广泛验证。我们开发的在大量受试者中自动表示和标记皮质的方法也应该在以下方面有用：1)描述正常和疾病的皮质几何结构；2)自动生成可变形图谱注册的地标；3)结构/功能关系的统计相关性研究；以及4)个体发生、系统发生、衰老和疾病中的形态变化分析。

DESCRIPTION (provided by applicant): Characterizing the relationship between the structure of the human brain and its function is one of the most important goals in neuroscience today. Medical imaging has been used to gain significant new insights into this relationship through the use of both anatomical and physiological imaging methods. Despite significant recent advances, the current methodology is still limited by the lack of automatic methods for the detailed segmentation, geometric analysis, and labeling of the cerebral cortex. The major goals of the proposed research are to develop fully automated methods to find and mathematically represent the cerebral cortex in volumetric magnetic resonance (MR) images and to automatically identify and label the major sulci and gyri on the cortex using a detailed statistical analysis of cortical geometry. Specifically, we propose to 1) develop and validate methods to find and mathematically represent the cerebral cortex from volumetric MR images; 2) develop and validate methods to calculate regional measures of cortical shape and volume; 3) develop and validate automatic labelling of sulci and gryi; and 4) conduct studies of cortical variability and volume changes in normal aging. All methods will be extensively validated using both computer phantoms and manual in vivo truth models. The methods we develop to automatically represent and label the cortex in large numbers of subjects should also be useful in 1) the development of a description of normal versus diseased cortical geometry, 2) automatic landmark generation for deformable atlas registration, 3) statistical correlation studies of structure/function relationships, and 4) the analysis of morphological changes in ontogenesis, phylogenesis, aging, and disease.