Spatial graphs for intra-cranial vascular network characterization, generation, and discrimination.
Graph methods that summarize vasculature by its branching topology are not sufficient for the statistical characterization of a population of intra-cranial vascular networks. Intra-cranial vascular networks are typified by topological variations and long, wandering paths between branch points. We present a graph-based representation, called spatial graphs, that captures both the branching patterns and the spatial locations of vascular networks. Furthermore, we present companion methods that allow spatial graphs to (1) statistically characterize populations of vascular networks, (2) generate the central vascular network of a population of vascular networks, and (3) distinguish between populations of vascular networks. We evaluate spatial graphs by using them to distinguish the gender and handedness of individuals based on their intra-cranial vascular networks.
Aylward
Stephen R
SR
CADDLab, Department of Radiology, USA. aylward@unc.edu
Jomier
Julien
J
Vivert
Christelle
C
LeDigarcher
Vincent
V
Bullitt
Elizabeth
E