Automated 3D Analysis of Cortical Geometry in MR Images

表征人脑的结构和其功能之间的关系是最重要的之一 当今神经科学的重要目标。医学成像已经被用来获得重要的新见解 这种关系通过使用解剖学和生理学的成像方法来实现。尽管意义重大 近年来取得的进展，目前的方法学仍然受限于缺乏详细的自动化方法 大脑皮层的分割、几何分析和标记。建议的主要目标 研究的目的是开发全自动的方法来寻找大脑皮层并在数学上表示 体积磁共振(MR)图像并自动识别和标记主要脑沟和脑回 采用详细的皮质几何学统计分析方法对皮质进行分析。具体地说，我们建议1)发展 并验证从体积磁共振图像中找到大脑皮层并对其进行数学表示的方法； 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 并验证脑沟和GRAI的自动标记；以及4)进行皮质变异性和体积的研究 正常衰老的变化。所有方法都将使用计算机幻影和 手动活体真实模型。我们开发的自动表示和标记大脑皮质的方法 大量的受试者也应该在1)发展对正常和 病变皮质几何学，2)用于可变形图谱配准的自动地标生成，3)统计 结构/功能关系的相关性研究，以及4)细胞形态变化的分析 个体发育、系统发育、衰老和疾病。

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.