EAGER: Geometric Mapping and Diffusion for 3D Imaging Informatics

建立的软件方法在映射和匹配的一个大集合的多模态跨学科数据正在成为一个瓶颈的整体工作流程，并阻碍了理解和利用大规模数据的科学发现的进展。 在许多情况下，嵌入在真实世界对象的3D成像中的固有几何结构在映射单个对象以解释它们的相似性和差异方面非常有效。但是，最近的计算技术仍然集中在提取和测量的几何和物理属性在一个单一的数据集。几何数据的全局建模以及大型单个数据集内和跨数据集的相关信息的模式和关系的评估尚未得到充分探索。一个严格的计算框架，紧密耦合的几何映射和匹配是非常重要的，以完成综合分析的各种潜在的关系，包含在大规模的图像数据集的特征，并大大推进三维成像信息学。 这个EAGER计划的重点是黎曼几何映射和几何扩散中潜在的变革性研究思想和方法，这些思想和方法紧密耦合在一起，以建立大量学科的精确映射和匹配

Established software approaches in mapping and matching of a large collection of multimodality cross-subject data is becoming a bottleneck for the overall work stream and hindering progress in understanding and utilization of large-scale data for scientiÞc discovery. In many scenarios, intrinsic geometric structures embedded in 3D imaging of real-world objects are very effective in mapping individual objects for interpretation of their similarity and disparity. But recent computational techniques are still focused on the extraction and measurement of geometric and physical properties in a single dataset. Global modeling of geometric data and assessment of patterns and relationships of related information within and across large individual datasets are under-explored. A rigorous computational framework that tightly couples geometric mapping and matching is of great importance to accomplish integrative analysis of a variety of underlying relationships in features of contained in large-scale image datasets and advance 3D imaging informatics substantially. This EAGER proposal focuses on potentially transformative research ideas and approaches in Riemannian geometry mapping and geometric diffusion, which are tightly coupled together to establish the accurate mapping and matching across a large number of subjects