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Biomedical subjects

Robert A Close

Publications and source records attributed to Robert A Close.

2 recordsLinked to original sources

Improved localization of coronary stents using layer decomposition.

Accurate placement and expansion of coronary stents is hindered by the fact that most stents are only slightly radiopaque and hence difficult to see in typical coronary X-ray images. We propose a new technique for improved image guidance of multiple coronary stent deployment using layer decomposition of coronary X-ray image sequences. We hypothesize that layer decomposition can improve the accuracy of localization of the end of a deployed stent. Layer decomposition is used to obtain good quality images of a stent in vitro. The resultant background-subtracted stent images are embedded into other cine X-ray image sequences to form a database of simulated image sequences. For each simulated sequence, the position of the stent edge is estimated from raw and layer-decomposed images using a small region of the original layer image as a template. Layer decomposition reduced median position errors in 33 of 47 image sequences (70%), including 16 of 18 sequences in which the position errors for raw and layer images differed by 5.0 pixels (0.5 mm) or more. Layer decomposition significantly reduces errors in determination of stent edge location in simulated cine X-ray image sequences.

Cineangiography↗

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.

Algorithms↗