Geometric Modeling for Spatial Analysis of Bio-Medical Data

Washington University, Saint Louis MO

Investigators

Abstract

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.