Geometric Modeling for Spatial Analysis of Bio-Medical Data

生物医学数据空间分析的几何建模

• 批准号: 0702662
• 负责人: Tao Ju
• 金额: $ 30万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0702662&HistoricalAwards=false
• 关键词: Geometric; Modeling; Spatial; Analysis; Bio

Advances in bio-medical imaging, such as microscopy, MRI, CT, and PET scanning, are producing a growing body of available 3D data. While computer modeling to date has largely focused on viewing or recovering the 3D shapes of anatomical structures from this data, more and more application areas involve further analysis of the data itself. One such area is the comparative analysis of 3D data collected from different individuals or from the same subject at different times. These comparisons are often the basis for making clinical decisions as well as scientific hypothesis. Supporting multi-subject analysis on this type of data requires establishing a mapping from one individual's volume to another's. This is a challenging problem not only because the shape of anatomical structures may vary greatly from one individual to another, but also because of noise and resolution issues inherent in the 3D capture process.The goal of this research is to develop a new modeling paradigm suitable for comparative analysis of 3D data of different subjects. This paradigm consists of constructing a topologically correct surface from segmented data of individual subjects, establishing a consistent mapping among all individuals on their boundary surfaces, and a simple, direct extension from surface mapping (2D) to smooth mappings between enclosed volumes (3D). The methods can be applied to both manifold and non-manifold surface representations, and the resulting volume mapping supports organization of a large repository of 3D images for efficient volume-based queries. As the boarder impact, the research will directly benefits medical researchers by providing them with the necessary computational tools to perform multi-subject, 2D and 3D data comparisons.

生物医学成像（例如显微镜、MRI、CT 和 PET 扫描）的进步正在产生越来越多的可用 3D 数据。虽然迄今为止的计算机建模主要集中于从这些数据中查看或恢复解剖结构的 3D 形状，但越来越多的应用领域涉及对数据本身的进一步分析。其中一个领域是对从不同个体或同一对象在不同时间收集的 3D 数据进行比较分析。这些比较通常是做出临床决策和科学假设的基础。支持对此类数据的多主题分析需要建立从一个人的卷到另一个人的卷的映射。这是一个具有挑战性的问题，不仅因为解剖结构的形状可能因人而异，而且还因为 3D 捕获过程中固有的噪声和分辨率问题。这项研究的目标是开发一种适合的新建模范例用于对不同受试者的 3D 数据进行比较分析。该范例包括从个体受试者的分段数据构建拓扑正确的表面，在其边界表面上的所有个体之间建立一致的映射，以及从表面映射（2D）到封闭体积（3D）之间的平滑映射的简单、直接的扩展。这些方法可应用于流形和非流形表面表示，并且生成的体积映射支持组织大型 3D 图像存储库，以实现基于体积的高效查询。作为边界影响，该研究将直接使医学研究人员受益，为他们提供必要的计算工具来进行多主题、2D 和 3D 数据比较。