Ophthalmic Surgery, Lasers and Imaging Retina

Clinical Science 

Quantitative Comparison Between Optical Coherence Tomography Angiography and Fundus Fluorescein Angiography Images: Effect of Vessel Enhancement

Thirumalesh Mochi, MD; Neha Anegondi, MTech; Molleti Girish, MTech; Chaitra Jayadev, MD; Abhijit Sinha Roy, PhD

Abstract

BACKGROUND AND OBJECTIVE:

To compare the vascular parameters derived from optical coherence tomography angiography (OCTA) and fundus fluorescein angiography (FFA) images.

PATIENTS AND METHODS:

Twenty-two eyes of 22 patients were imaged with OCTA and FFA. FFA images were cropped to the same dimension as OCTA images after registration. Vessel enhancement using a Frangi filter and local fractal analysis was applied to the superficial layer of the OCTA and cropped FFA images. Foveal avascular zone (FAZ) area, vessel density, spacing between large vessels, and spacing between small vessels were quantified.

RESULTS:

FAZ area was similar between original OCTA, Frangi-filtered OCTA, and FFA images (P = .32). Actual OCTA images had significantly higher vessel density (35.2% ± 1.45%, P < .001) than Frangi-filtered OCTA images (29.8% ± 0.78%) and Frangi-filtered FFA images (25.5% ± 2.41%). Spacing between large vessels was significantly lower in original OCTA images (31.9% ± 1.47%, P < .001) than Frangi-filtered OCTA images (36.8% ± 1.24%) and Frangi-filtered FFA images (60.1% ± 2.68%). Further, FFA images had significantly lower spacing between small vessels (14.4% ± 0.43%, P < .001) than original OCTA images (32.7% ± 1.03%) and Frangi-filtered OCTA images (33.4% ± 0.81%).

CONCLUSION:

FAZ area was similar between OCTA and FFA, independent of vessel enhancement. However, vessel enhancement improved the agreement of vascular parameters between OCTA and FFA images of the same eye.

[Ophthalmic Surg Lasers Imaging Retina. 2018;49:e175–e181.]

Abstract

BACKGROUND AND OBJECTIVE:

To compare the vascular parameters derived from optical coherence tomography angiography (OCTA) and fundus fluorescein angiography (FFA) images.

PATIENTS AND METHODS:

Twenty-two eyes of 22 patients were imaged with OCTA and FFA. FFA images were cropped to the same dimension as OCTA images after registration. Vessel enhancement using a Frangi filter and local fractal analysis was applied to the superficial layer of the OCTA and cropped FFA images. Foveal avascular zone (FAZ) area, vessel density, spacing between large vessels, and spacing between small vessels were quantified.

RESULTS:

FAZ area was similar between original OCTA, Frangi-filtered OCTA, and FFA images (P = .32). Actual OCTA images had significantly higher vessel density (35.2% ± 1.45%, P < .001) than Frangi-filtered OCTA images (29.8% ± 0.78%) and Frangi-filtered FFA images (25.5% ± 2.41%). Spacing between large vessels was significantly lower in original OCTA images (31.9% ± 1.47%, P < .001) than Frangi-filtered OCTA images (36.8% ± 1.24%) and Frangi-filtered FFA images (60.1% ± 2.68%). Further, FFA images had significantly lower spacing between small vessels (14.4% ± 0.43%, P < .001) than original OCTA images (32.7% ± 1.03%) and Frangi-filtered OCTA images (33.4% ± 0.81%).

CONCLUSION:

FAZ area was similar between OCTA and FFA, independent of vessel enhancement. However, vessel enhancement improved the agreement of vascular parameters between OCTA and FFA images of the same eye.

[Ophthalmic Surg Lasers Imaging Retina. 2018;49:e175–e181.]

Introduction

Analysis of abnormalities in the retinal and choroidal vasculature plays a key role in diagnosis and treatment of various retinal disorders.1 Fundus fluorescein angiography (FFA) is considered to be the gold standard for diagnosing various retinal and choroidal abnormalities.2 FFA involves intravenous injection of dye and provides two-dimensional images of blood flow with a wide field of view.3 Optical coherence tomography angiography (OCTA) is a recent, noninvasive, and dyeless imaging modality that uses motion contrast to generate angiography images.4 OCTA can be useful in diagnosing diseases (eg, diabetic retinopathy, vein occlusion, choroidal neovascularization, glaucoma).1

There have been few qualitative studies on comparison between FFA and OCTA.2,4–6 In general, OCTA images revealed more detail than FFA images due to the size of the en face area of the retina imaged in each modality; hence, vessel density from the entire FFA image may not confirm to OCTA vessel density. Quantitative studies between FFA and OCTA are still lacking. Therefore, the study aimed to analyze and compare the retinal vascular parameters in OCTA and FFA images before and after vessel enhancement (and noise reduction). The retinal vessels were enhanced using a multi-scale vesselness filter.7 Subsequently, a previously established local fractal method was used to analyze vessel parameters such as foveal avascular zone (FAZ), vessel density, spacing between large vessels, and spacing between small vessels in these images.8–10 It was hypothesized that vessel enhancement in the OCTA images would improve the degree of agreement between the OCTA and FFA images.

Patients and Methods

This study was approved by the institutional ethics committee of Narayana Nethralaya Super Specialty Eye Hospital, Bangalore, India. The research followed the tenets of the Declaration of Helsinki, and written informed consent was obtained from all subjects before imaging. Twenty-two OCTA scans of 22 patients (12 diabetic retinopathy, three central serous retinopathy, two vein occlusion, two vasculitis, one macular scar, one pigment epithelial detachment, and one nonarteritic anterior ischemic optic neuropathy) with good quality images (signal strength index > 40) were included in the study. All subjects underwent imaging with a swept-source OCT (DRI Triton Plus; Topcon Systems, Tokyo, Japan) device by a single operator. The OCT had a high scan speed of 100,000 A-scans per second. Analysis was performed on the superficial retinal vascular plexus OCTA images of scan area 3 mm × 3 mm. In addition to OCTA images, FFA images (n = 22) were also captured with the same device. The OCTA and FFA images had an image size of 320 pixels × 320 pixels and 1,932 pixels × 1,932 pixels, respectively.

A well-known Hessian-based multi-scale vesselness filter known as a Frangi filter was used to enhance the vascular structure in the OCTA and FFA images.7 The Frangi filter used Eigen vectors of the Hessian matrix to compute the likeliness of an OCTA image region to contain vascular structures. The Hessian matrix at each pixel of the image was computed by convolving the image with a Gaussian kernel having sigma values ranging between 1 and 10. The step size between the sigma values was 2. The magnitudes of the Frangi correction constants “beta” and “c” were 0.5 and 15, respectively.7 These were thresholds that controlled the sensitivity of the filter.7 The FFA images were analyzed only after applying the filter because FFA covered a larger en face area of the retina and had inferior spatial resolution compared with OCTA. Therefore, it was possible that some of the small vessels were captured as noise rather than as distinct vessels compared to OCTA. The FAZ in the FFA images was manually segmented by an experienced clinician. Based on the center of the FAZ, the FFA images were cropped to the same dimensions as the OCTA images by overlaying the OCTA image on the FFA scan. The overlay was achieved by visual examination such that the location of the large vessels in one image (OCTA) approximately coincided with the location of the same vessels in the other image (corresponding FFA). Figure 1A shows the FFA images of one of the study eyes. Figure 1B shows the cropped region around the fovea used for comparison with the OCTA image (Figure 1C) of the same eye, centered at the fovea. The overlay was registered such that large vessels in each image coincided in shape and size upon careful visual examination. In order to analyze and compare the vasculature in OCTA and cropped FFA images, the local fractal method was used.8–10 Using a window size of 3 pixels × 3 pixels, fractal dimension was computed for each pixel in the image.8–10 A normalized local fractal dimension ratio (LFDR) (range: 0.0 to 1.0) was then computed for each pixel in the OCTA image and represented as a two-dimensional contour map.8–10 FAZ was segmented. 8–10 A LFDR closer to 1 indicated a vessel, and a ratio closer to 0 indicated a non-vessel region.

Fundus fluorescein angiography (FFA) image (A) of a study eye. Cropped FFA (B) image around the fovea (shown by the red box in A). Corresponding optical coherence tomography angiography image of the same eye centered on the fovea (C).

Figure 1.

Fundus fluorescein angiography (FFA) image (A) of a study eye. Cropped FFA (B) image around the fovea (shown by the red box in A). Corresponding optical coherence tomography angiography image of the same eye centered on the fovea (C).

A scoring system was developed based on visual comparison of the normalized LFDR map with the OCTA image.8–10 The pixels within the large vessels had a normalized ratio of 0.9 to 1.0, whereas the pixels within the small vessels had a ratio between 0.7 and 0.9. Pixels in regions, which were devoid of or had minimal vascular features, had a normalized ratio between 0.0 and 0.3. By visual examination, these regions were observed to occur between or around the large vessels. In some cases, these regions were also observed between sparsely packed small vessels. In general, these were termed as “spaces between large vessels.” The only exception to this classification was the FAZ, which was common to all the images. Pixels in regions around closely packed small vessels, which may be branching out from a large vessel or surrounding small vessels, had a normalized ratio between 0.3 and 0.7. These were termed as “spaces between small vessels.” The vessel density was computed as a percentage by counting all of the pixels with a normalized ratio between 0.7 and 1.0 and then dividing by the total number of pixels in the OCTA image. Similarly, the spacing between the large vessels and the spacing between the small vessels were expressed as the percentage of the total number of pixels in the OCTA image.

Statistical Analysis

The vascular parameters were computed for the original OCTA images, OCTA images after applying Frangi filter, and the cropped FFA images after applying Frangi filter. All analyzed variables were reported as mean ± standard error of mean (SEM) after confirming normality with Kolmogorov-Smirnov test. The analyzed variables were foveal avascular zone (FAZ in mm2), vessel density (%), spacing between large vessels (%), and spacing between small vessels (%).8–10 Repeated measures analysis of variance (rANOVA) was applied between the vascular parameters computed from the OCTA images without Frangi filter, OCTA images with Frangi filter, and FFA images with Frangi filter, respectively. rANOVA adjusted the P value for multiple comparisons post hoc. Linear regression was also performed to analyze whether vascular parameters derived from the FFA image were predictive of the same derived from the OCTA image. A P value less than .05 was considered statistically significant and was Bonferroni-corrected for multiple group comparisons. All statistical analyses were performed with MedCalc v17.6 (MedCalc, Ostend, Belgium).

Results

Table 1 shows the mean ± SEM of the analyzed vascular parameters in original OCTA images, Frangi-filtered OCTA images, and Frangi-filtered FFA images. FAZ area was similar among the original OCTA images, Frangi-filtered OCTA images, and Frangi-filtered FFA images (P = .32). From Table 1, original OCTA images had significantly higher vessel density (35.2% ± 1.45%, P < .001) as compared with Frangi-filtered OCTA images (29.8% ± 0.78%) and Frangi-filtered FFA images (25.5% ± 2.41%). Vessel densities of Frangi-filtered OCTA images and Frangi-filtered FFA images were similar (P > .05). Spacing between the large vessels was significantly lower in original OCTA images (31.9% ± 1.47%, P < .001) as compared with Frangi-filtered OCTA images (36.8% ± 1.24 %) and Frangi-filtered FFA images (60.1% ± 2.68 %). Further, FFA images had significantly lower spacing between small vessels (14.4% ± 0.43%, P < .001) as compared with original OCTA images (32.7% ± 1.03%) and Frangifiltered OCTA images (33.4% ± 0.81%). Spacing between small vessels was similar (P = .66) between original OCTA images and Frangi-filtered OCTA images (Table 1).

Mean ± SEM of Retinal Vascular Parameters of OCTA of Superficial Layer (With and Without Frangi Filter, Respectively) and FFA Images

Table 1:

Mean ± SEM of Retinal Vascular Parameters of OCTA of Superficial Layer (With and Without Frangi Filter, Respectively) and FFA Images

Table 2 shows the results of the linear regression analyses. The results clearly indicate the significant positive correlation between OCTA images with and without Frangi filter (first column of Table 1). Here, spacing between large vessels and spacing between small vessels had the best correlation (r > 0.9 in Table 1). In comparison of FFA images, r was relatively lower but still statistically significant (columns 2 and 3 in Table 2). Thus, there was some variability between the individual eyes when the OCTA image was compared the FFA image. Figures 2A to 2C show the OCTA image with and without Frangi filter along with the corresponding cropped FFA image, respectively. Figures 2D to 2F show results of the LFDR analyses, when applied to each image, respectively.

Linear Regression (Correlation Coefficient [r] and Significance Level [P]) Performed Between OCTA Without Frangi Filter and OCTA With Frangi Filter, OCTA Without Frangi Filter and FFA, and OCTA With Frangi Filter and FFA

Table 2:

Linear Regression (Correlation Coefficient [r] and Significance Level [P]) Performed Between OCTA Without Frangi Filter and OCTA With Frangi Filter, OCTA Without Frangi Filter and FFA, and OCTA With Frangi Filter and FFA

Actual optical coherence tomography angiography (OCTA) image (A), Frangi-filtered OCTA image (B) and fundus fluorescein angiography image (C) of diseased eye, respectively. Corresponding contour maps generated using local fractal method (D–F).

Figure 2.

Actual optical coherence tomography angiography (OCTA) image (A), Frangi-filtered OCTA image (B) and fundus fluorescein angiography image (C) of diseased eye, respectively. Corresponding contour maps generated using local fractal method (D–F).

Discussion

In this study, a quantitative comparison was performed between OCTA and FFA images. The vessels in OCTA and FFA images were enhanced using a well-known multi-scale vessel enhancement filter known as a Frangi filter.7 After vessel enhancement, local fractal method8–10 was used to compute the vascular parameters in these images.8–10 Previous studies have compared the diagnostic agreement between OCTA and FFA.2,5,11–13 In a recent study, the morphological characteristics and efficacy of OCTA were compared with FFA in eyes facing neovascular age-related macular degeneration.13 The results of the study showed that OCTA may be useful in noninvasive diagnosis of choroidal neovascularization and in monitoring disease progress and treatment management.13 Another study analyzed and compared the classification of eyes with diabetic retinopathy using OCTA and FFA.5 The results of the study showed that OCTA allowed better visualization of parafoveal macular vasculature and was more sensitive to central macular vascular changes as compared with FFA.5 Another study compared the visualization of microaneurysms and FAZ area using OCTA and FFA in patients with diabetic macular edema.12 The study concluded that deep retinal vascular plexus OCTA images can identify microaneurysms better than superficial retinal vascular plexus OCTA images and FFA images.12 The study also observed that the FAZ appeared to be larger in FFA images than OCTA images.12 From Table 1, FAZ was similar between OCTA images (with and without filter) versus FFA scans. Thus, there could be disease-specific changes because this study population was not the same as the earlier study.12 The linear regression analyses (Table 2) shows the significant agreement between the FFA image and the OCTA image (with and without Frangi filter). Overall, there a slightly better agreement between the OCTA image with Frangi filter and the FFA image (eg, for spacing between small vessels); r in column 2 was lower than r in column 3 of Table 2.

A recent study on embolic branch retinal artery occlusion showed that OCTA was capable of demonstrating the area of flow interruption secondary to retinal artery embolus.14 The results also showed that OCTA was able to demonstrate the capillary nonperfusion in the involved region, which correlated to the FA findings.14 A case report on macular telangiectasia type 1 showed that OCTA allowed a clear visualization of most telangiectasias and aneurysms in the deep retinal vascular plexus.6 Also, capillary dropout areas were detected well in both superficial and deep layer of the OCTA images.6 These were comparable to FFA.6

However, quantitative studies on vessel parameters comparing FFA and OCTA are still lacking. A study on patients with diabetic macular ischemia quantified the FAZ area in both FA and OCTA images.11 The study showed that FAZ area was similar between FA and OCTA images.11 This observation was similar to our data, which showed that FAZ area was similar between OCTA and FFA images (Table 1). In addition to the FAZ area, the current study also computed vessel density, spacing between large vessels, and spacing between small vessels. Original OCTA images had significantly higher vessel density as compared with Frangi-filtered OCTA and FA images (Table 1). Further, spacing between large vessels was significantly lower in original OCTA images as compared with Frangi-filtered OCTA and FA images (Table 1). Frangi-filtered FA images had the highest spacing between the large vessels (Table 1). Spacing between the small vessels was significantly lower in FFA images as compared with original and Frangi-filtered FA images (Table 1). However, the spacing between small vessels was similar between original OCTA and Frangi-filtered OCTA images (Table 1).

The Frangi filter was used to enhance the vasculature in the OCTA and FFA images.7 Frangi filter has proved to be useful in vessel enhancement and background noise suppression in various angiography images.7,15,16 The application of Frangi filter to the OCTA image converted the same to an image similar to the FFA scan of the same eye because Frangi filter eliminated the smaller vessels and capillaries from the OCTA image to some extent. Because FFA inherently captured the larger vessels and capillaries better, vessel enhancement may be necessary to quantitatively compare the vessel density derived from FFA and OCTA images. However, other parameters (Table 1) may still differ between them for the same reason as described above. These highlight or quantify the technological differences between the two modalities (OCTA vs. FFA). It may help in better detection of abnormal areas such as macular ischemia or edema in diseased eyes. However, further studies on diseased eyes are needed to understand the role of Frangi filter in detection of abnormal areas in OCTA images. The results of the study show that local fractal method could be useful in analyzing the vessels in not just OCTA images but also FFA images.8–10 In previous studies, local fractal method was used on only OCTA images to compute the vessel parameters in normal, diabetic retinopathy, and glaucoma eyes.8–10 This study showed that local fractal method could be equally useful for evaluation of FFA images as well. It is important to understand that the method is dependent on the quality of the images. Because all of the study eyes were diseased eyes with poor vision, OCTA scans completely devoid of motion artifacts were difficult. Therefore, OCTA and FA images with large motion artifacts were excluded. Further, the study compared only the superficial layer of the OCTA images with FA images. Projection artifacts of the superficial layer on the deep layer would not allow true quantification of disease-related features in the deep layer. Further, depth information in FFA images is not explicitly known. The linear regressions could be used to estimate the OCTA vascular parameters from the FFA image, when OCTA is not accessible to the clinician. Repeatability of manual segmentation of FAZ area in FFA images needs to be assessed in study with greater sample size.

In summary, vessel enhancement and local fractal method were useful in comparing vascular parameters in OCTA and FFA images to establish equivalence between them. For a specific retinal disease, further study with larger sample size is required to improve the correlations between the OCTA image and the FFA image.

References

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Mean ± SEM of Retinal Vascular Parameters of OCTA of Superficial Layer (With and Without Frangi Filter, Respectively) and FFA Images

Vascular ParametersOCTA Without Frangi FilterOCTA With Frangi FilterFFAP Value
FAZ Area (mm2)0.43 ± 0.030.43 ± 0.030.39 ± 0.02.32
Vessel Density (%)35.2 ± 1.4529.8 ± 0.7825.5 ± 2.41.001*
Spacing Between Large Vessels (%)31.9 ± 1.4736.8 ± 1.2460.1 ± 2.68.001*
Spacing Between Small Vessels (%)32.7 ± 1.0333.4 ± 0.8114.4 ± 0.43.001*

Linear Regression (Correlation Coefficient [r] and Significance Level [P]) Performed Between OCTA Without Frangi Filter and OCTA With Frangi Filter, OCTA Without Frangi Filter and FFA, and OCTA With Frangi Filter and FFA

Vascular Parametersra, Parb, Pbrc, Pc
FAZ Area (mm2)1.0, 1e–50.70, .0020.70, .002
Vessel Density (%)0.74, .0010.62, .0230.70, .001
Spacing Between Large Vessels (%)0.93, .0010.61, .0130.66, .006
Spacing Between Small Vessels (%)0.92, .0010.52, .0280.62, .006
Authors

From the Retina Department, Narayana Nethralaya, Bangalore, India (TM, CJ); the Imaging, Biomechanics and Mathematical Modeling Solutions Lab, Narayana Nethralaya Foundation, Bangalore, India (NA, ASR); and VIT University, Vellore, India (MG).

Drs. Mochi and Anegondi have contributed equally as first authors on this study.

The authors report no relevant financial disclosures.

Address correspondence to Abhijit Sinha Roy, PhD, Narayana Nethralaya Foundation, #258A Hosur Road, Bommasandra, Bangalore-560100, India; email: asroy27@yahoo.com.

Received: October 30, 2017
Accepted: March 26, 2018

10.3928/23258160-20181101-15

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