Optimization of the split-spectrum amplitude-decorrelation angiography algorithm on a spectral optical coherence tomography system

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104

Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area. ©2015 Optical Society of America

Section: Patient Selection and Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography

Liu¹,

Gao²,

Bailey³

et al. 2015

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104

“…An iterative routine maximizes the cross-correlation of the OCT signal of the two B-scans acquired at each position by rigid displacements of the second Bscan in two directions. After recognizing the optimal shift, it was applied to the second OCT image of each the 11 pairs generated by spectrum splitting [40]. Finally, the OCT images were down-sampled and the amplitude decorrelation signal was computed.…”

Section: Comparison To Inter-b-scan Registration Algorithmmentioning

confidence: 99%

Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography

Camino¹,

Jia²,

Liu³

et al. 2017

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Abstract:We developed an algorithm to remove decorrelation noise due to bulk motion in optical coherence tomography angiography (OCTA) of the posterior eye. In this algorithm, OCTA B-frames were divided into segments within which the bulk motion velocity could be assumed to be constant. This velocity was recovered using linear regression of decorrelation versus the logarithm of reflectance in axial lines (A-lines) identified as bulk tissue by percentile analysis. The fitting parameters were used to calculate a reflectance-adjusted upper bound threshold for bulk motion decorrelation. Below this threshold, voxels are identified as non-flow tissue, their flow values are set to zeros. Above this threshold, the voxels are identified as flow voxels and bulk motion velocity is subtracted from each using a nonlinear decorrelation-velocity relationship previously established in laboratory flow phantoms. Compared to the simpler median-subtraction method, the regression-based bulk motion subtraction improved angiogram signal-to-noise ratio, contrast, vessel density repeatability, and bulk motion noise cleanup in the foveal avascular zone, while preserving the connectivity of the vascular networks in the angiogram.

“…Then the flow signal is represented by the averaged decorrelation value of all the spectral bands. This method has been shown to greatly improve the signal-to-noise ratio of OCTA [15] and been employed to identify vascular and blood flow changes in different aspects of ophthalmology [1-4, 16, 17].…”

Section: Introductionmentioning

confidence: 99%

Hematocrit dependence of flow signal in optical coherence tomography angiography

Yang¹,

Su²,

Wang³

et al. 2017

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Abstract:The hematocrit dependence of flow signal (split-spectrum amplitude decorrelation angiography-SSADA decorrelation value) was investigated in this paper. Based on the normalized field temporal correlation function and concentration dependent particle scattering properties, the relationship between hematocrit and flow signal was analytically derived. Experimental verification of the relationship was performed with custom-designed microfluidic chips and human blood with 45%, 40% and 32% hematocrit. It was found that, in large flow channels and blood vessels, the normal hematocrit is near the decorrelation saturation point and therefore a change in hematocrit has little effect on the SSADA decorrelation value (flow signal). However, in narrow channels in the capillary size range, the effective hematocrit (adjusted for the overlap between OCT beam and channel) is in the range of 6.7-9.5% and therefore variation in hematocrit does significantly affect the flow signal.