Zhongdi Chu

Quantifying Microvascular Density and Morphology in Diabetic Retinopathy Using Spectral-Domain Optical Coherence Tomography Angiography.

Purpose To quantify changes in retinal microvasculature in diabetic retinopathy (DR) by using spectral-domain optical coherence tomography angiography (SD-OCTA). Methods Retrospective, cross-sectional, observational study of healthy and diabetic adult subjects with and without DR. Retinal microvascular changes were assessed by using SD-OCTA images and an intensity-based optical microangiography algorithm. A semiautomated program was used to calculate indices of microvascular density and morphology in nonsegmented and segmented SD-OCTA images. Microvascular density was quantified by using skeleton density (SD) and vessel density (VD), while vessel morphology was quantified as fractal dimension (FD) and vessel diameter index (VDI). Statistical analyses were performed by using the Students t-test or analysis of variance with post hoc Tukey honest significant difference tests for multiple comparisons. Results Eighty-four eyes with DR and 14 healthy eyes were studied. Spearmans rank test demonstrated a negative correlation between DR severity and SD, VD, and FD, and a positive correlation with VDI (ρ = −0.767, −0.7166, −0.768, and +0.5051, respectively; P < 0.0001). All parameters showed high reproducibility between graders (ICC = 0.971, 0.962, 0.937, and 0.994 for SD, VD, FD, and VDI, respectively). Repeatability (κ) was greater than 0.99 for SD, VD, FD, and VDI. Conclusions Vascular changes in DR can be objectively and reliably characterized with SD, VD, FD, and VDI. In general, decreasing capillary density (SD and VD), branching complexity (FD), and increasing average vascular caliber (VDI) were associated with worsening DR. Changes in capillary density and morphology were significantly correlated with diabetic macular edema.

Comparison Between Spectral-Domain and Swept-Source Optical Coherence Tomography Angiographic Imaging of Choroidal Neovascularization

Purpose The purpose of this study was to compare imaging of choroidal neovascularization (CNV) using swept-source (SS) and spectral-domain (SD) optical coherence tomography angiography (OCTA). Methods Optical coherence tomography angiography was performed using a 100-kHz SS-OCT instrument and a 68-kHz SD-OCTA instrument (Carl Zeiss Meditec, Inc.). Both 3 × 3- and 6 × 6-mm2 scans were obtained on both instruments. The 3 × 3-mm2 SS-OCTA scans consisted of 300 A-scans per B-scan at 300 B-scan positions, and the SD-OCTA scans consisted of 245 A-scans at 245 B-scan positions. The 6 × 6-mm2 SS-OCTA scans consisted of 420 A-scans per B-scan at 420 B-scan positions, and the SD-OCTA scans consisted of 350 A-scans and 350 B-scan positions. B-scans were repeated four times at each position in the 3 × 3-mm2 scans and twice in the 6 × 6-mm2 scans. Choroidal neovascularization was excluded if not fully contained within the 3 × 3-mm2 scans. The same algorithm was used to detect CNV on both instruments. Two graders outlined the CNV, and the lesion areas were compared between instruments. Results Twenty-seven consecutive eyes from 23 patients were analyzed. For the 3 × 3-mm2 scans, the mean lesion areas for the SS-OCTA and SD-OCTA instruments were 1.17 and 1.01 mm2, respectively (P = 0.047). For the 6 × 6-mm2 scans, the mean lesion areas for the SS-OCTA and SD-OCTA instruments were 1.24 and 0.74 mm2 (P = 0.003). Conclusions The areas of CNV tended to be larger when imaged with SS-OCTA than with SD-OCTA, and this difference was greater for the 6 × 6-mm2 scans.

Quantitative assessment of the retinal microvasculature using optical coherence tomography angiography

Abstract. Optical coherence tomography angiography (OCTA) is clinically useful for the qualitative assessment of the macular microvasculature. However, there is a need for comprehensive quantitative tools to help objectively analyze the OCT angiograms. Few studies have reported the use of a single quantitative index to describe vessel density in OCT angiograms. In this study, we introduce a five-index quantitative analysis of OCT angiograms in an attempt to detect and assess vascular abnormalities from multiple perspectives. The indices include vessel area density, vessel skeleton density, vessel diameter index, vessel perimeter index, and vessel complexity index. We show the usefulness of the proposed indices with five illustrative cases. Repeatability is tested on both a healthy case and a stable diseased case, giving interclass coefficients smaller than 0.031. The results demonstrate that our proposed quantitative analysis may be useful as a complement to conventional OCTA for the diagnosis of disease and monitoring of treatment.

Automated Quantitation of Choroidal Neovascularization: A Comparison Study Between Spectral-Domain and Swept-Source OCT Angiograms

Purpose To compare the lesion sizes of choroidal neovascularization (CNV) imaged with spectral-domain (SD) and swept-source (SS) optical coherence tomography angiography (OCTA) and measured using an automated detection algorithm. Methods Patients diagnosed with CNV were imaged by SD-OCTA and SS-OCTA systems using 3 × 3-mm and 6 × 6-mm scans. The complex optical microangiography (OMAGC) algorithm was used to generate the OCTA images. Optical coherence tomography A datasets for imaging CNV were derived by segmenting from the outer retina to 8 μm below Bruchs membrane. An artifact removal algorithm was used to generate angiograms free of retinal vessel projection artifacts. An automated detection algorithm was developed to quantify the size of the CNV. Automated measurements were compared with manual measurements. Measurements from SD-OCTA and SS-OCTA instruments were compared as well. Results Twenty-seven eyes from 23 subjects diagnosed with CNV were analyzed. No significant differences were detected between manual and automatic measurements: SD-OCTA 3 × 3-mm (P = 0.61, paired t-test) and 6 × 6-mm (P = 0.09, paired t-test) scans and the SS-OCTA 3 × 3-mm (P = 0.41, paired t-test) and 6 × 6-mm (P = 0.16, paired t-test) scans. Bland-Altman analyses were performed to confirm the agreement between automatic and manual measurements. Mean lesion sizes were significantly larger for the SS-OCTA images compared with the SD-OCTA images: 3 × 3-mm scans (P = 0.011, paired sample t-test) and the 6 × 6-mm scans (P = 0.021, paired t-test). Conclusions The automated algorithm measurements of CNV were in agreement with the hand-drawn measurements. On average, automated SS-OCTA measurements were larger than SD-OCTA measurements and consistent with the results from using hand-drawn measurements.

A Novel Strategy for Quantifying Choriocapillaris Flow Voids Using Swept-Source OCT Angiography

Purpose To achieve reproducible imaging of the choriocapillaris and associated flow voids using swept-source OCT angiography (SS-OCTA). Methods Subjects were enrolled and SS-OCTA was performed using the 3 × 3 mm scan pattern. Blood flow was identified using the complex optical microangiography (OMAG) algorithm. The choriocapillaris was defined as a slab from the outer boundary of Bruchs membrane (BM) to approximately 20 μm below BM. Compensation for the shadowing effect caused by the RPE and BM complex on the choriocapillaris angiogram was achieved by using the structural information from the same slab. A thresholding method to calculate the percentage of flow voids from a region was developed based on a normal database. Results Twenty normal subjects and 12 subjects with drusen were enrolled. SS-OCTA identified the choriocapillaris in normal subjects as a lobular plexus of capillaries in the central macula and the lobular arrangement became more evident toward the periphery. In all eyes, signal compensation resulted in fewer choriocapillaris flow voids with improved repeatability of measurements. The best repeatability for the measurement was achieved by using 1 standard deviation (SD) for the thresholding strategy. Conclusions SS-OCTA can image the choriocapillaris in vivo, and the repeatability of flow void measurements is high in the presence of drusen. The ability to image the choriocapillaris and associated flow voids should prove useful in understanding disease onset, progression, and response to therapies.

Quantitative microvascular analysis of retinal venous occlusions by spectral domain optical coherence tomography angiography

Purpose To quantitatively evaluate the retinal microvasculature in human subjects with retinal venous occlusions (RVO) using optical coherence tomography angiography (OCTA). Design Retrospective, cross-sectional, observational case series. Participants Sixty subjects (84 eyes) were included (20 BRVO, 14 CRVO, 24 unaffected fellow eyes, and 26 controls). Methods OCTA was performed on a prototype, spectral domain-OCTA system in the 3x3mm central macular region. Custom software was used to quantify morphology and density of retinal capillaries using four quantitative parameters. The vasculature of the segmented retinal layers and nonsegmented whole retina were analyzed. Main outcome measures Fractal dimension (FD), vessel density (VD), skeletal density (SD), and vessel diameter index (VDI) within the segmented retinal layers and nonsegmented whole retina vasculature. Results Nonsegmented analysis of RVO eyes demonstrated significantly lower FD (1.64±0.01 vs 1.715±0.002; p<0.001), VD (0.32±0.01 vs 0.432±0.002; p<0.001), and SD (0.073±0.004 vs 0.099±0.001; p<0.001) compared to controls. Compared to the fellow eye, FD, VD and SD were lower (p<0.001), and VDI was higher (p<0.001). FD, VD, and SD progressively decreased as the extent (or type) of RVO increased (control vs BRVO vs CRVO; p<0.001). In the unaffected fellow eye FD, VD and SD showed significant differences when compared to control eyes or affected RVO eyes (p<0.001). Conclusions Quantitative OCTA of the central 3x3mm macular region demonstrates significant differences in capillary density and morphology among subjects with BRVO and CRVO compared to controls or unaffected fellow eyes in all vascular layers. The unaffected fellow eyes also demonstrate significant differences when compared to controls. OCTA allows for noninvasive, layer-specific, quantitative evaluation of RVO-associated microvascular changes.

Comparison of Neovascular Lesion Area Measurements From Different Swept-Source OCT Angiographic Scan Patterns in Age-Related Macular Degeneration

Purpose We compared area measurements for the same neovascular lesions imaged using swept source optical coherence tomography angiography (SS-OCTA) and enlarging scan patterns. Methods Patients with neovascular age-related macular degeneration were imaged using a 100-kHz SS-OCTA instrument (PLEX Elite 9000). The scanning protocols included the 3 × 3, 6 × 6, 9 × 9, and 12 × 12 mm fields of view. Two groups were studied. Group 1 included small lesions contained within the 3 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 3 mm scan, and Group 2 included larger lesions that were fully contained within the 6 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 6 mm scan. Results A total of 30 eyes of 26 patients were enrolled in Group 1 and 30 eyes of 25 patients were enrolled in Group 2. In Group 1, the automated mean lesion area measurements were 1.11 (SD = 0.78), 1.14 (SD = 0.80), and 1.27 (SD = 0.82) mm2 for the 3 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 3, 6 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 6, and 12 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 12 mm scans, respectively (ANOVA P < 0.001; post hoc comparisons, P = 0.184, 3 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 3 vs. 6 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 6 mm; P < 0.001 for the other two pairs). In Group 2, the automated mean lesion area measurements were 5.43 (SD = 2.56), 5.53 (SD = 2.48), and 5.49 (SD = 2.65) mm2 for the 6 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\( \times \)\end{document} 6, 9 \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bit

Complex signal-based optical coherence tomography angiography enables in vivo visualization of choriocapillaris in human choroid

Abstract. The choriocapillaris (CC) plays an essential role in maintaining the normal functions of the human eye. There is increasing interest in the community to develop an imaging technique for visualizing the CC, yet this remains underexplored due to technical limitations. We propose an approach for the visualization of the CC in humans via a complex signal-based optical microangiography (OMAG) algorithm, based on commercially available spectral domain optical coherence tomography (SD-OCT). We show that the complex signal-based OMAG was superior to both the phase and amplitude signal-based approaches in detailing the vascular lobules previously seen with histological analysis. With this improved ability to visualize the lobular vascular networks, it is possible to identify the feeding arterioles and draining venules around the lobules, which is important in understanding the role of the CC in the pathogenesis of ocular diseases. With built-in FastTrac™ and montage scanning capabilities, we also demonstrate wide-field SD-OCT angiograms of the CC with a field of view at 9×11  mm2.

Ultra-wide optical coherence tomography angiography in diabetic retinopathy

Background To implement an ultra-wide optical coherence tomography angiography imaging (UW-OCTA) modality in eyes with diabetic retinopathy (DR) with the aim of quantifying the burden of microvascular disease at baseline and subsequent clinic visits. Methods UW-OCTA was implemented on a 1,060 nm swept source (SS) OCTA engine running at 100 kHz A-line rate with a motion tracking mechanism. A montage scanning protocol was used to capture a 100-degree field of view (FOV) using a 4×4 grid of sixteen total individual 6×6 mm2 scans. Typical OCTA images with a FOV of 3×3, 6×6 and 12×12 mm2 were obtained for comparison. DR patients were scanned at baseline and follow-up. They were treated at the clinicians discretion. Vessel density and non-perfusion area maps were calculated based on the UW-OCTA images. Results Three proliferative DR patients were included in the study. UW-OCTA images provided more detailed visualization of vascular networks compared to 50-degree fluorescein angiography (FA) and showed higher burden of pathology in the retinal periphery that was not captured by typical OCTA. Neovascularization complexes were clearly detected in the two patients with active PDR. Vessel density and non-perfusion maps were used to measure progressive capillary non-perfusion and regression of neovascularization between visits. Conclusions UW-OCTA provides approximately 100-degree OCTA images of the fundus comparable to that of wide-angle fundus photography, and may be more applicable in conditions such as DR which affect the peripheral retina in contrast to standard OCTA.

Accurate estimation of choriocapillaris flow deficits beyond normal intercapillary spacing with swept source OCT angiography

Background To estimate choriocapillaris flow deficits beyond normal intercapillary distance with swept source optical coherence tomography angiography (SS-OCTA). Methods Subjects were enrolled and repeated SS-OCTA scans were performed using the 3 mm × 3 mm scan pattern. Blood flow was identified using the complex optical microangiography (OMAGc) algorithm. The choriocapillaris (CC) was defined as a 20 µm slab of the flow volume beneath the outer boundary of Bruchs membrane (BM) and was compensated with the corresponding structural image for flow deficits measurement. Flow deficits were segmented based on one mean standard deviation from a normal database. A histogram based thresholding method was developed to remove small flow deficits that were determined by examining intercapillary spacing within normal CC networks. A registration method based on affine and B-spline transformation was utilized for the CC angiogram averaging. Four repeated scans were averaged, and results were compared with and without removal of small flow deficits after averaging a different number of scans (N=1, group 1; N=2, group 2; N=3, group 3 and N=4, group 4). Results Seven normal subjects were enrolled. Intercapillary distance was found to be 24 µm for the CC networks under OCTA, which was used as the threshold to exclude small flow deficits for CC quantification. After averaging, significant reduction in background noise and improvement in continuity of blood vessel networks were observed both on retinal and choriocapillaris angiograms. Flow deficit percentages of the choriocapillaris were significantly reduced with averaging (group 1 vs. group 2: P<0.0001; group 2 vs. group 3: P<0.001; group 3 vs. group 4: P<0.001). The flow deficit percentages were also significantly reduced after removing the small flow deficits (≤24 µm in diameter) in all groups (P<0.01). A statistically significant difference was found after removing small flow deficits (≤24 µm in diameter) between group 1 and group 2 (P<0.001), between group 2 and group 3 (P<0.05), and between group 3 and group 4 (P<0.05). However, the significance was decreased compared to that without small flow deficits removal. Conclusions A method was developed to improve the robust estimation of choriocapillaris flow deficits by removing the small flow deficits corresponding to normal intercapillary spacing. After the removal of small flow deficits, fewer repeats were required for image averaging to achieve comparable accuracy of flow deficit measurements with SS-OCTA.