Quantitative coronary angiography with deformable spline models.

Although current edge-following schemes can be very efficient in determining coronary boundaries, they may fail when the feature to be followed is disconnected (and the scheme is unable to bridge the discontinuity) or branch points exist where the best path to follow is indeterminate. In this paper, we present new deformable spline algorithms for determining vessel boundaries, and enhancing their centerline features. A bank of even and odd S-Gabor filter pairs of different orientations are convolved with vascular images in order to create an external snake energy field. Each filter pair will give maximum response to the segment of vessel having the same orientation as the filters. The resulting responses across filters of different orientations are combined to create an external energy field for snake optimization. Vessels are represented by B-Spline snakes, and are optimized on filter outputs with dynamic programming. The points of minimal constriction and the percent-diameter stenosis are determined from a computed vessel centerline. The system has been statistically validated using fixed stenosis and flexible-tube phantoms. It has also been validated on 20 coronary lesions with two independent operators, and has been tested for interoperator and intraoperator variability and reproducibility. The system has been found to be specially robust in complex images involving vessel branchings and incomplete contrast filling.