EAGER: Geometric Mapping and Diffusion for 3D Imaging Informatics

Wayne State University, Detroit MI

Investigators

Abstract

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