Multigenerational analysis and visualization of large 3D vascular images

Methods exist for extracting vascular tree structures from very large 3D medical images, such as those arising from micro-CT scanners. Techniques have not been well addressed, however, for characterizing the detailed statistical structure of the tree or for interacting with such data. In this paper, we present our ongoing efforts on the detailed generational analysis of large 3D vascular trees. Our previously proposed system discussed the initial image analysis and tree representation of 3D vascular images. Our current work improves the performance of the image analysis process and gives new means for evaluating the quantitative information and geometrical characteristics of the vasculature. Furthermore, we have made it more feasible to perform multigenerational analysis and topology manipulation interactively by incorporating visualization tools. Our current implementation of the image processing and analysis methods generates varied details of the branching geometry, at generation, inter-branch, and intra-branch levels. Variations of vessel surfaces, blood volumes, cross-sectional areas, and branch lengths in a whole tree are studied. The visualization tools provide functionality of displaying slices, projections of the 3D images, and surface rendering of the segmented trees. Also, tree-editing capability permits a user to interactively manipulate the vascular topology, such as modification of extraneous, generally peripheral artifactual, branches and generations, and update the statistical details of the tree in real time. We present results for 3D micro-CT rat heart images.