EXTENDED THIN-PLATE SPLINES FOR BRAIN VARIATION IN 3D

用于 3D 大脑变化的扩展薄板样条

• 批准号: 2121933
• 负责人: FRED LEON BOOKSTEIN
• 金额: $ 21.87万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-30 至 1998-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2121933
• 关键词: brain imaging /visualization /scanning; brain mapping; computer program /software; computer simulation; human data; image enhancement; mathematical model; method development; model design /development; morphology; statistics /biometry

This is a proposal for extensions of an extant statistical technology so that it becomes directly applicable to Phase I of the Human Brian Project. Integration and analysis of neuroscientific information require a facility for the biometric standardization of gross brain shape. This standardization is required for achieving such aims as averaging brain images over homogeneous patient or normal samples, referring histological or ultrastructural measurements to a macroscopic coordinate system, and evaluating the correlation of structure with function in health or in disease. The present proposal will construct a comprehensive algebraic- geometric framework for that standardization of brain shape. The proposed work builds on modern techniques of morphometrics, the statistics of biological shape and shape change. These techniques all presume the existence of landmarks, points, such as "Anterior Commissure," with biological identities as well as geometric locations. Configurations of landmarks both serve as multivariate data themselves, as for averaging of shape or its correlation with causes or effects, and drive a canonical interpolation map, the thin-plate spline, which serves to standardize the geometry of any subsequent image processing, averaging, and analysis. In the past year techniques have been introduced that extend these strengths of the landmark-driven spline to incorporate information from edge-directions and other important aspects of the local structure of these images. To date this work has emphasized two-dimensional images. We propose to extend the new differential techniques to handle the three-dimensional structures, such as tracts, lobes, and gyri, that are crucial for the representation of neuroanatomical variability at the gross level. We propose to supply the Human Brain Project with an analytically uniform set of algorithms for standardization of their clinical images across sites and projects. We will enumerate the types of descriptors required, supply algorithmic parameterizations as necessary, and generate a consistent set of three-dimensional warping facilities for the uniform representation of these aspects of geometry across all the laboratories of the Project, whatever the precision or scale of the relevant measurements. We will further supply a mathematical basis for the intercomparison of the many earlier versions of these computations, both superposition techniques and "deformable template" analyses, that have been previously developed by many other neuroscientists. Finally, we will serve as a test site for the new approaches to more sophisticated neuroscientific visualizations, such as structure-function correlations, that the standardizations make possible. The new methods should optimize the geometric efficiency and statistical power with which the Human Brain Project will eventually exploit the massive archives of data it intends to assemble for diverse scientific and clinical purposes.

这是扩展现存统计技术的建议 它直接适用于人类Brian的第一阶段 项目。 神经科学信息的整合和分析需要 大脑总形状的生物识别标准化的设施。 这 实现平均大脑等目标需要标准化 均质患者或正常样本的图像，参考组织学 或宏观坐标系的超微结构测量，以及 评估结构与健康功能的相关性 疾病。 本提案将构建一个全面的代数 - 大脑形状标准化的几何框架。 这 拟议的工作以形态计量学的现代技术为基础 生物形状和形状变化的统计数据。 这些技术全部 假定存在地标的存在，例如“前部” 合并，“具有生物身份和几何位置。 地标的配置都用作多元数据本身， 至于平均形状或与原因或效果的相关性，以及 驱动一个适用于薄板样条的规范插值图 为了标准化任何后续图像处理的几何形状， 平均和分析。 在过去的一年中 引入了将具有里程碑标记驱动的样条的这些优势扩展到 合并边缘方向和其他重要方面的信息 这些图像的局部结构。 迄今为止，这项工作强调了二维图像。 我们建议 扩展新的差分技术来处理三维 结构，例如区域，叶和回旋，对 神经解剖学变异性在总水平上的表示。 我们 建议向人脑项目提供分析统一的项目 一组用于标准化其临床图像的算法 站点和项目。 我们将列举所需的描述符类型， 根据需要提供算法参数化，并生成一个 制服的一致一组三维翘曲设施 代表所有实验室几何方面的这些方面 项目的相关精度或规模如何 测量。 我们将进一步提供数学基础 这些计算的许多较早版本的比较，都 叠加技术和“可变形模板”分析，具有 以前是由许多其他神经科学家开发的。 最后，我们 将作为新方法的测试网站，以实现更复杂的方法 神经科学的可视化，例如结构功能相关性， 标准化是可能的。 新方法应优化 人脑的几何效率和统计能力 项目最终将利用它打算的大量数据档案 为了多种科学和临床目的组装。