Aortic Aneurysms (AA) are the 13th leading cause of death in the US. In standard clinical
practice, intervention is initiated when the maximal diameter cross-sectional reaches
5.5cm. However, this is a 1D measure and it has been suggested in the literature that
higher order measurements (area, volume) might be more appropriate clinically.
Unfortunately, no commercially available tools exist for extracting a 3D model of the
epithelial layer (versus the lumen) of the vessel. Therefore, we present work towards
semi-automatically recovering the aorta from CT angiography volumes with the aim to
facilitate such studies.
We build our work upon a previous approach to this problem. Bodur et. al.,
presented a variant of the iso-perimetric algorithm to semi-automatically segment several
individual aortic cross-sections across longitudinal studies, quantifying any growth. As a
by-product of these sparse cross-sections, it is possible to form a series of rough 3D
models of the aorta.
In this work we focus on creating a more detailed 3D model at a single time point
by automatically recovering the aorta between the sparse user-initiated segmentations.
Briefly, we fit a tube model to the sparse segmentations to approximate the cross-sections
at intermediate regions, refine the approximations and apply the isoperimetric algorithm
to them. From these resulting dense cross-sections we reconstruct our model. We applied
our technique to 12 clinical datasets which included significant amounts of thrombus.
Comparisons of the automatically recovered cross-sections with cross-sections drawn by
an expert resulted in an average difference of .3cm for diameter and 2cm^2 for area.