Digital subtraction peripheral angiography using image stacking: initial clinical results.
Using clinically acquired x-ray angiography image sequences, we compared three algorithms for creating a single diagnostic quality image that combined input images containing flowing contrast agent. These image-stacking algorithms were: maximum opacity with the minimum gray-scale value across time recorded at each spatial location, (REC) recursive temporal filtering followed by a maximum opacity operation, and (AMF) an approximate matched filter consisting of a convolution with a kernel approximating the matched filter followed by a maximum opacity operation. Eighteen clinical exams of the peripheral arteries of the legs were evaluated. AMF gave 2.7 times greater contrast to noise ratio than the single best subtraction image and 1.3 times improvement over REC, the second best stacking algorithm. This is consistent with previous simulations showing that AMF performs nearly equal to the optimal result from matched filtering without the well-known limitations. For example, unlike matched filtering, AMF filter coefficients were obtained automatically using an image-processing algorithm. AMF effectively brought out small collateral arteries, otherwise difficult to see, without degrading artery sharpness or stenosis grading. Comparing results using reduced and full contrast agent volumes demonstrated that contrast agent load could be reduced to one-third of the conventional amount with AMF processing. By simulating reduced x-ray exposures on clinical exams, we determined that x-ray exposure could be reduced by 80% with AMF processing. We conclude that AMF is a promising, potential technique for reducing contrast agent load and for improving vessel visibility, both very important characteristics for vascular imaging.
Kump
K S
KS
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106, USA.
Sachs
P B
PB
Wilson
D L
DL
D000465:Algorithms; D015901:Angiography, Digital Subtraction / Q000295:instrumentation* / Q000379:methods*; D001158:Arteries / Q000473:pathology; D003287:Contrast Media / Q000494:pharmacology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D013997:Time Factors; D014965:X-Rays
D003287:Contrast Media