&lt;title&gt;Improved image guidance of coronary stent deployment&lt;/title&gt;

Close, Robert A.; Abbey, Craig K.; Whiting, James S.

doi:10.1117/12.383052

Cited by 5 publications

(8 citation statements)

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“…We propose to use this method of layer decomposition to improve visual assessment of stent positioning and deployment. We have previously reported that layer decomposition can improve the visibility of a deployed stent 7. In this article, we show that layer decomposition can reduce errors in the determination of the stent edge position in simulated cine X‐ray sequences.…”

Section: Introductionmentioning

confidence: 70%

“…In particular, we utilize the fact that, in a time‐sequence of images, the moving stent appears over different backgrounds and with independent noise samples. We decompose the cine X‐ray sequence into a sum of moving layers whose optical density profiles are computed by motion‐compensated temporal averaging 6–8. This procedure removes background structures and reduces random noise.…”

Section: Introductionmentioning

confidence: 99%

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Improved localization of coronary stents using layer decomposition

Abbey

Whiting

2002

Comput. Aided Surg.

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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.

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Section: Introductionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Improved localization of coronary stents using layer decomposition

Abbey

Whiting

2002

Comput. Aided Surg.

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“…There exists in the literature two teams that described similar techniques we can compare to. The first one, Close et al (2000Close et al ( , 2002; Close et al (2003), deals with transparent layer decomposition. These pioneer articles do not aim at presenting an automatic DSE algorithm.…”

Section: Comparison With Related Workmentioning

confidence: 99%

“…Since 2000, an image processing technique called digital stent enhancement (DSE) has emerged to produce an enhanced image of a stent from an X-ray image sequence (Close et al, 2000;Florent et al, 2008;Bismuth and Vaillant, 2008). It has gained interest from clinicians over the past years, leading imaging system manufacturers to collaborate with them to study this topic (Koolen, 2005;Ross et al, 2005;Conway et al, 2005;Mishell et al, 2007;Ohanessian et al, 2008;Bismuth and Vaillant, 2008;Córdova et al, 2009;Funck et al, 2009).…”

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confidence: 99%

“…DSE techniques have emerged independently from two different viewpoints. Close et al (2000) were interested in the representation of an X-ray image as a superimposition of transparent layers and proposed a general framework to estimate the layers present in a sequence of images. They considered that if the motion of a given layer was known, its intensities could be estimated by averaging the value of each pixel along its trajectory in the image sequence.…”

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A comprehensive study of stent visualization enhancement in X-ray images by image processing means

Bismuth

Vaillant

Funck

et al. 2011

Medical Image Analysis

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In this work we propose a comprehensive study of Digital Stent Enhancement (DSE), from the analysis of the requirements to the validation of the proposed solution. First, we derive the stent visualization requirements in the context of the clinical application and workflow. Then, we propose a DSE algorithm combining automatic detection, tracking, registration and contrast enhancement. The most original parts of our solution: landmark segmentation and non-linear image registration are detailed. Finally, we validate the algorithm on a large number of synthetic and clinical cases. Performance is characterized in terms of automation, image quality and execution time. This work is, to the best of our knowledge, the first comprehensive article on DSE, covering problem statement, proposed solution, and validation strategies.

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