Evaluation of layer decomposition for multiframe quantitative coronary angiography

Multiframe quantitative coronary angiography is typically performed by averaging measurements of artery diameter over multiple frames. This approach reduces errors attributable to random noise but may not reduce systematic errors caused by background structures, nonlinear system response, and motion blur. We attempt to reduce these sources of error by decomposing the image sequence into moving layers, one of which includes the artery. We embed simulated arteries into clinical angiographic sequences so that the true vessel dimensions are known accurately. The measurement tasks are minimum diameter, geometric percent stenosis, and densitometric percent stenosis. We compare measurements for single and multiple raw images, single images with fixed mask subtraction, single and multiple images with layered background subtraction, and time-averaged layer images. We find that both multiframe averaging and layer decomposition significantly improve geometric and densitometric accuracy compared with single-frame measurements. The best results were obtained by averaging measurements from multiple frames of layered background-subtracted images.