AUTOMATED 3D ANALYSIS OF CORTICAL GEOMETRY IN MR IMAGES

描述(改编自申请者摘要)：描述 人脑的结构和它的功能之间是一种关系 当今神经科学中最重要的目标之一。医学成像已经成为 用来通过使用获得对这种关系的重要新见解 解剖和生理成像方法的结合。海流 然而，方法论受到缺乏自动方法的限制 大脑皮层的分割、几何分析和标记。这个 拟议研究的目标是开发自动方法来查找和 用体积磁场数学表示大脑皮层 磁共振(MR)图像，并识别和标记上的主要脑沟 使用皮质详细分析了皮质几何结构。具体地说， 申请者建议：1)开发和验证查找和 在数学上表示中央层(大约第4层) 体积磁共振图像中的大脑皮层；2)开发和验证方法 从皮质表面提取几何量；3)开发和 验证自动识别和标记主沟的方法 大脑；以及4)进行区域皮质体积和几何形状的初步研究 正常衰老的变化。所有方法都将得到广泛的验证，使用 既有计算机幻影，也有人工活体真相模型。这些方法可用于 自动分析大量大脑皮层的几何形状 主题在以下方面也应该有用：1)制定描述 正常与病变的皮质几何图形；2)自动生成地标 用于可变形地图集配准；3)统计相关性研究 结构/功能关系；4)形态分析 个体发育、系统发育、衰老和疾病的变化。

DESCRIPTION (Adapted from Applicant's Abstract): Characterizing the relationship between the structure of the human brain and it 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. The current methodology, however, is limited by the lack of automatic methods for the segmentation, geometric analysis, and labeling of the cerebral cortex. The goals of the proposed research are to develop automated methods to find and mathematically represent the cerebral cortex in volumetric magnetic resonance (MR) images, and to identify and label the major sulci on the cortex using a detailed analysis of cortical geometry. Specifically, the applicants proposed to: 1) develop and validate methods to find and mathematically represent the central layer (approximately layer 4) of the cerebral cortex in volumetric MR images; 2) develop and validate methods to extract geometric quantities from the cortical surface; 3) develop and validate methods to automatically identify and label themajor sulci of the brain; and 4) conduct a pilot study of regional cortical volume and geometry changes in normal aging. All methods will be extensively validated using both computer phantoms and manual in vivo truth models. The methods to automatically analyze the geometry of 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.