We developed a new approach to quantitative coronary angiography (QCA), whi
ch overcomes several limitations of available programs, such as dependence
on operator input; limited tracking ability; fixed correction of the point
spread function (PSF);, and different calibration on empty vs. contrast-fil
led catheters. The program (Intelligent Images QCA, version 1.4) provides a
bsolute reproducibility by deterministic, operator-independent identificati
on of the skeleton and the edges of the coronary tree. The algorithm works
as follows: application of a matched filter to emphasize selectively the co
ronary arteries; adaptive threshold binarization; binary thinning and skele
tonization; perpendicular resampling with sub-pixel interpolation; derivati
ve filtering; minimal cost edge detection; and automatic identification and
quantification of the stenosis, Operator's interaction is restricted to de
finition of a region of interest; editing of either skeleton or edges is no
t allowed. PSF correction is fine-tuned to the actual frequency response of
the imaging chain by calibration on a contrast-filled conical lucite phant
om, Catheter calibration is carried out by a second derivative-based edge d
etection much less sensitive to the presence of contrast. In vitro phantom
analysis (0.5 to 5.0 mm) showed accuracy of 0.028-0.031 mm and precision of
0.054-0.062 mm on nonmagnified images from the angio TV chain and the cine
projector, respectively. In vivo evaluation on a series of consecutive dia
gnostic angiograms yielded correct contour detection of 70/73 stenoses (96%
); interobserver intraframe MLD variability 0.00 mm; correct tracking of ca
theter edges 100%; interobserver variation coefficient of catheter calibrat
ion 3.3%; and mean difference of calibration factor on contrast-filled vs.
empty catheters 2.7%, This new approach significantly improves reproducibil
ity with respect to conventional QCA, maintaining high accuracy, precision,
and applicability, Cathet. Cardiovasc. Intervent. 48:435-445, 1999, (C) 19
99 Wiley-Liss, Inc.