NORMAL MR NEUROMORPHOMETRY BY GLOBAL PATTERN MATCHING

正常大脑中由于生长和 发育、衰老、性二态性、偏侧性和利手性是 可通过MRI观察到，但需要精确和准确的方法， 侦测 我们建议自动搜索神经形态学 通过一种新的数学鲁棒性方法研究大脑亚结构的差异 对小的局部和区域形状具有前所未有的敏感性 差异 现代MR脑成像方法提供了详细的体内 关于个体大脑解剖结构的信息。 然而，这些解剖数据的解释受到了阻碍， 无法快速量化不同品种之间的形态差异 个体 困难在于两个方面。 首先，图像之间 不同的个体必须在一个共同的参照系中，但他们不是 以这种方式收集。 第二，即使注册，正常变化 这使得比较变得困难，如果不是不可能的话。 我们建议进一步 开发用于融合解剖数据的算法工具，如通过 全脑MRI，因此可以描述正常对照人群， 比较了 这项建议的主要重点是发展数学 大脑神经解剖变异的表现和特定的 特别是在海马和颞叶。 这涉及 单个形态测量图谱到多个个体目标MR的映射 图像卷。 地图集是一个多值空间数组，其中包含 信号值（对应于CT、MR和彩色冷冻切片图像）， 它们的符号标签（组织类型、解剖命名） 对调查有医学和生物学意义的子卷。 子体积包括特定的脑沟和各种颞叶 （海马、海马旁区和颞回）。 这些 表示提供了一个独特的工具，算法生成的 将地图集（模板）及其子体积平滑映射到 目标解剖结构。 这样，从组中选择的形态测量特征 将正常人进行比较。 通过评估这些方法在生物样品表征中的性能， 正常人群，我们将能够明确测试假设 关于异常人群的大脑结构变化， 用现有的方法解决。 这项工作将提供一个直接 用于测量疾病过程的微小影响的形态测量工具。 的 可变形脑图谱将反映潜在的稳定性或 预定义的解剖标记区域，以及它们相对于 一个单一的生物学意义上的坐标系的法线集。 我们 将测试该方法的能力，以确定假设的组间 大脑结构的大小和形状的差异。 这个新的地图 工具将显示定位量化个人和人口的大脑 由于正常变异、性别、用手习惯和 偏侧性

Small neuroanatomical difference in the normal brain due to growth and development, aging, sexual dimorphism, laterality, and handedness are observable by MRI but require precise and accurate methods for their detection. We propose to automate the search for neuromorphological differences in brain substructures by a new mathematically robust method with unprecedented sensitivity to small local and regional shape differences. Modern MR brain imaging methods provide detailed in vivo information regarding the anatomical structure of individual brains. However, interpretation of these anatomic data has been hindered by the inability to expeditiously quantify morphological differences across individuals. The difficulty lies in two areas. First, images between different individuals must be in a common reference frame, but they are not collected in this fashion. Second, even when registered, normal variation makes comparisons difficult if not impossible. We propose to further develop algorithmic tools for fusion of anatomical data, as measured via whole brain MRI, so normal control populations can be described and compared. The major focus of this proposal is the development of mathematical representations of neuroanatomical variation of the brain and specific subregions, especially in the hippocampus and temporal lobe. This involves mapping of a single morphometric atlas to multiple individual target MR image volumes. An atlas is a multivalued spatial array that contains signal values (corresponding to CT, MR and color cryosection images) with their symbolic labels (tissue type, anatomic nomenclature) for all subvolumes of medical and biological significance to the investigation. The subvolumes include specific sulci and various of the temporal lobe (hippocampus, parahippocampal region and temporal gyri). These representations provide a unique tool for the algorithmic generation of smooth maps from an atlas (template) and its subvolumes onto families of target anatomies. In this way, selected morphometric features from groups of normal individuals will be compared. By assessing the performance of these methods in athe characterization of normal populations, we will be able to definitively test hypotheses regarding brain substructure changes in abnormal populations that cannot be resolved with present methods. This work will provide a direct morphometric tool for measuring small effects of disease processes. The deformable brain atlas will reflect both the underlying stability or predefined anatomically labeled regions, and t heir covariation relative to a single biologically meaningful coordinate system in sets of normals. We will test the method's ability to identify hypothesized intergroup differences in the size and shape of brain structures. This new mapping tool will be shown to locate the quantify individual and population brain structural differences due to normal variation, gender, handedness, and laterality.