AUTOMATED 3D ANALYSIS OF CORTICAL GEOMETRY IN MR IMAGES

描述（改编自申请人的摘要）： 人类大脑的结构和功能之间的关系是一个 当今神经科学最重要的目标。 医学影像学已经成为 过去，通过使用 解剖学和生理学的成像方法。 当前 然而，由于缺乏自动方法， 分割、几何分析和大脑皮层的标记。 的 拟议研究的目标是开发自动化的方法， 数学上表示大脑皮层在体积磁 磁共振（MR）图像，并识别和标记上的主要沟 皮质使用皮质几何形状的详细分析。 具体而言是 申请人建议：1）开发和验证方法， 在数学上表示中间层（近似层4）， 体积MR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脑沟的方法 大脑;以及4）进行区域皮质体积和几何形状的初步研究 正常老化的变化。 所有方法都将使用以下方法进行广泛验证： 计算机幻影和人工体内真实模型。 的方法 自动分析大量大脑皮层的几何形状， 主题也应该是有用的：1）发展的描述， 正常与病变皮质几何结构; 2）自动界标生成 用于可变形图谱配准; 3） 结构/功能关系; 4）形态学分析 个体发育、生殖发育、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.