Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems

Joseph, Shiju; Rousseau, Clément; Subhash, Hrebesh M.; Leahy, Martin J.; Adlam, David

doi:10.1117/12.2042053

Cited by 3 publications

(12 citation statements)

References 11 publications

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“…Intensity based correlation approaches have been reported for transverse flow measurements [17,18]. Recently vascular imaging using the correlation approach has also been reported [19][20][21][22].…”

Section: Analysis Techniquementioning

confidence: 99%

“…The higher degree of CC variation, in non-flow regions, can therefore be utilized. As proposed in section-2.1, the extent of CC variation can be better quantified either by taking both mean and SD or just the SD [22] of the 30 CC maps.…”

Section: Cross Correlation Variation Comparison Between Standard and mentioning

confidence: 99%

“…Since, flow regions have a propensity to change in intensity, so they will have low coefficient values. Correlation based flow detection techniques take advantage of the intensity variation in flow regions, which decorrelate across A-lines [17,18] or image frames [19,20,22]; while non-flow regions have high correlation. In flow mapping methods [19,20], correlation coefficients in the range ( −0.6 & 0.6) were considered low correlation values and so treated as flow regions; while non-flow regions had high correlation (< −0.6 or > 0.6).…”

Section: Analysis Techniquementioning

confidence: 99%

“…Pearson Correlation Coefficient (PCC), is a widely used flow detection method [17][18][19][20]22] where, 'm' and 'n' are dimensions of a rectangular window applied to two consecutive frames (2) ; I (1) mn and I (2) mn respectively are the mean of the intensity values within the sub-region. As the (m × n) window is shifted across the entire image, a 2D correlation map -X is generated.…”

Section: Analysis Techniquementioning

confidence: 99%

“…Whereas, the variation for a flow region will be muted. The differences in the degree of CC variation for flow and non-flow regions were quantified either by means of standard deviation (SD) using equation 2 [22], or using both mean (or average) and SD of the fluctuation.…”

Section: Analysis Techniquementioning

confidence: 99%

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Developing cross-correlation as a method for microvessel imaging using clinical intravascular optical coherence tomography systems

Joseph

Adnan

Subhash

et al. 2015

Biomed. Opt. Express

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Abstract:Current clinical intravascular optical coherence tomography (IV-OCT) imaging systems have limited in-vivo flow imaging capability because of non-uniform catheter rotation and inadequate A-line scan density. Thus any flow-localisation method that seeks to identify sites of variation within the OCT image data-sets, whether that is in amplitude or phase, produces non-representative correlation (or variance) maps. In this study, both mean and the variation within a set of cross-correlation maps, for static OCT imaging was used to differentiate flow from nonflow regions. Variation was quantified by use of standard deviation. The advantage of this approach is its ability to image flow, even in the presence of motion artifacts. The ability of this technique to suppress noise and capture flow maps was demonstrated by imaging microflow in an ex-vivo porcine coronary artery model, by nailfold capillary imaging and in-vivo microvessel imaging from within the human coronary sinus.

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Section: Analysis Techniquementioning

confidence: 99%

Section: Cross Correlation Variation Comparison Between Standard and mentioning

confidence: 99%

Section: Analysis Techniquementioning

confidence: 99%

Section: Analysis Techniquementioning

confidence: 99%

Section: Analysis Techniquementioning

confidence: 99%

See 3 more Smart Citations

Developing cross-correlation as a method for microvessel imaging using clinical intravascular optical coherence tomography systems

Joseph

Adnan

Subhash

et al. 2015

Biomed. Opt. Express

Self Cite

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show abstract

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

2016

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, "Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method," J. Abstract. Lumen segmentation from clinical intravascular optical coherence tomography (IV-OCT) images has clinical relevance as it provides a full three-dimensional perspective of diseased coronary artery sections. Inaccurate segmentation may occur when there are artifacts in the image, resulting from issues such as inadequate blood clearance. This study proposes a transmittance-based lumen intensity enhancement method that ensures only lumen regions are highlighted. A level-set-based active contour method that utilizes the local speckle distribution properties of the image is then employed to drive an image-specific active contour toward the true lumen boundaries. By utilizing local speckle properties, the intensity variation issues within the image are resolved. This combined approach has been successfully applied to challenging clinical IV-OCT datasets that contains multiple lumens, residual blood flow, and its shadowing artifact. A method to identify the guide-wire and interpolate the lost lumen segments has been implemented. This approach is fast and can be performed even when guide-wire boundaries are not easily identified. Lumen enhancement also makes it easy to identify vessel side branches. This automated approach is not only able to extract the arterial lumen, but also the smaller microvascular lumens that are associated with the vasa vasorum and with atherosclerotic plaque.

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Endoscopic OCT Angiography Using Clinical Proximal-End Scanning Catheters

et al. 2022

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Endoscopic optical coherence tomography angiography (OCTA) is a promising modality to inspect the microvasculature of inner organs in the early-stage tumor diagnosis. However, an endoscopic clinical proximal-end scanning catheter has limited flow imaging capability due to the nonuniform rotational distortion (NURD) and physiological motion. In this study, a combined local and global (CLG) optical flow algorithm was used to estimate the motion vectors caused by NURD and physiological motion. The motion vectors were used to bicubic-interpolation-resample the OCT structure to ensure that the circumferential pixels were equally spaced in the space domain. Then, angiograms were computed based on the statistical relation between inverse SNR (iSNR) and amplitude decorrelation (IDa), termed as IDa-OCTA. Finally, the ability of this technique for endoscopic OCTA imaging was demonstrated by flow phantom experiments and human nailfold capillary imaging.

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Variation in cross-correlation as a discriminator for microvessel imaging using clinical intravascular optical coherence tomography systems

Cited by 3 publications

References 11 publications

Developing cross-correlation as a method for microvessel imaging using clinical intravascular optical coherence tomography systems

Developing cross-correlation as a method for microvessel imaging using clinical intravascular optical coherence tomography systems

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

Endoscopic OCT Angiography Using Clinical Proximal-End Scanning Catheters

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