**Purpose.**:
To demonstrate a noninvasive method to visualize and analyze the parafoveal capillary network in humans.

**Methods.**:
An adaptive optics scanning laser ophthalmoscope was used to acquire high resolution retinal videos on human subjects. Video processing tools that enhance motion contrast were developed and applied to the videos to generate montages of parafoveal retinal capillaries. The capillary network and foveal avascular zone (FAZ) were extracted using video and image analysis algorithms. The capillary densities in the zone immediately outside the FAZ were calculated and the variation in density as a function of direction was investigated. Extracted FAZ geometries were used to calculate area and effective diameters. The authors also compared their method against fluorescein angiography (FA) for one subject.

**Results.**:
The parafoveal capillaries were clearly visible when the motion contrast in noninvasive videos was enhanced. There was a marked improvement in the contrast of the parafoveal capillaries when compared to the unprocessed videos. The average FAZ area was 0.323 mm^{2}, with an average effective diameter of 633 μm. There was no variation in capillary density near the FAZ in different directions.

**Conclusions.**:
Using motion cues to enhance vessel contrast is a powerful tool for visualizing the capillary network, in the absence of contrast agents. The authors demonstrate a tool to study the microcirculation of healthy subjects noninvasively.

^{ 1 }Far from the fovea, the capillary network is multi-layered; closer to the fovea, the network thins down to two layers in the peri- to parafoveal region, and to one layer in the immediate parafoveal region. Finally, in the fovea, there are no vessels, forming the foveal avascular zone (FAZ). The average diameter of a normal FAZ as described in textbooks is around 400 to 500 μm. However, there is considerable individual variation.

^{ 2 }The FAZ has been well characterized and is of important clinical significance in a number of different diseases, including diabetes.

^{ 3 }

^{ 4 }Due to these risks, FA is not performed on normal eyes, except under special circumstances. Moreover, FA may not be able to reliably identify the smallest capillaries.

^{ 5 }An alternate method to improve capillary contrast is to use video processing tools based on flow visualization.

^{ 6 }These tools included mean, variance, min, max, range, and transition images. The variance image has been previously applied as a method for increasing vessel contrast in microvessels before applying leukocyte tracking algorithms.

^{ 7 }However, the variance image alone fails to increase the contrast of vessels in our datasets. We will demonstrate that spatial and temporal information can be used to increase the local contrast for moving objects, and enable the visualization of capillaries.

^{ 8 }Recently, an adaptive optics scanning laser ophthalmoscope (AOSLO) was used to quantify leukocyte speeds through parafoveal capillaries.

^{ 9,10 }In this article, we will combine video and image processing tools with AOSLO imaging to demonstrate an improved method that can detect even the smallest capillaries in the parafoveal region without the use of injected dyes.

^{ 10 }using a variety of imaging parameters. Briefly, videos were acquired at 512 × 512 pixels

^{2}, 30 or 60 fps, for 5 to 40 seconds in overlapping windows in the parafoveal region. The field of view ranged from 1.2° to 2.5°. The imaging wavelength was either 532 nm or 840 nm. (See Figs. 3, 9, and 10 for examples of videos and results using the 532 nm laser, and Figs. 1, 5, 6, and 10 for examples of videos and results using the 840 nm laser.)

^{ 11,12 }Next, the videos were cropped to eliminate boundary effects due to the eye motion correction, and passed through a median filter to eliminate uneven noise artifacts due to noise redistribution from the desinusoiding step. Finally, when necessary, frames where either the Signal to Noise Ratio (SNR) dropped (e.g., due to high frequency tear film–induced aberrations), or where stabilization failed (e.g., blinks and saccades) were deleted.

*D*(

*x*,

*y*) is calculated from each pair of consecutive frames as

*D*

_{j}(

*x*,

*y*) =

*I*

_{j}(

*x*,

*y*)/

*I*

_{j+1}(

*x*,

*y*), where

*I*

_{j}(

*x*,

*y*) represents the intensities of frame

*j*. Consecutive division images are averaged together to create a multi-frame division image,

*M*

_{j}(

*x*,

*y*) = [

*D*

_{j}(

*x*,

*y*) +

*D*

_{j+1}(

*x*,

*y*)]/2, with high contrast ratios between the fluid parcels and the background tissue.

*M*

_{j}gives the

*j*th frame of the multi-frame division video.

*S*(

*x*,

*y*) can be calculated using either arithmetic or geometric definitions. The arithmetic definitions are given by: The geometric definitions are given by We used both arithmetic and geometric definitions. The arithmetic definitions were more robust against noise as well as errors in stabilization, and could be used for general cases, while the geometric definitions gave higher quality results, but required excellent stabilization.

^{ 13 }(Fig. 2), in the following manner. A closed contour

*C*(

*t*) was used to mathematically represent the FAZ boundary. First, through a graphical user interface, seed points

*p*

_{i}(

*x*,

*y*) were selected by the user at points near the boundary of the FAZ. Next,

*p*

_{i}(

*x*,

*y*) were displaced toward the centerline of the nearest vessel to reduce variations due to user input. To identify vessel centerlines, we calculated the Frangi vesselness measure.

^{ 13 }A neighborhood

*N*

_{i}was generated around each

*p*

_{i}(

*x*,

*y*). The vesselness values in

*N*

_{i}, denoted as

*V*

_{i}, were used to determine the location of the new point,

*q*

_{i}(

*x*,

*y*), as

*q*

_{i}(

*x*,

*y*) = max(

*V*

_{i}). The amount of displacement from the seed point toward the centerline point was restricted by the size of the neighborhood around which to search. Finally,

*C*(

*t*) was generated using piecewise Cardinal splines between neighboring pairs of

*q*

_{i}(

*x*,

*y*), with the restriction that interpolation points needed to fall into the pixel space of the montage image.

*C*(

*t*) was used to generate a mask of the FAZ for the area calculation (Fig. 3).

^{2}and then converted to mm

^{2}using a model eye parameterized by axial length, anterior chamber depth (ACD), and corneal curvature (CC). Axial length was measured on all subjects. We used ACD and CC values from Bennett's model eye,

^{ 14 }except in the case of four subjects, where we were able to measure ACD and CC directly (IOL Master; Carl Zeiss Meditec AG, Jena, Germany). The use of additional biometry measurements, such as ACD and CC, improves the conversion from angle to distance; however, the amount of improvement is small.

^{ 15 }Using ray tracing, the posterior nodal point (PNP) of the eye was estimated from these parameters. Finally, we calculated mm/deg =

*d**tan(1 deg), where

*d*is the distance from the PNP to the retina.

*L*

_{Tot}/

*A*, where

*L*

_{Tot}was the combined length of all capillary segments in a region of interest (ROI), and

*A*the area of the ROI, as described previously.

^{ 16 }We selected a special ROI to represent the zone at which there was only a single layer of capillaries, with no major retinal vessels (arteries and veins). In our datasets, this was the zone that was 0.15° from the edge of the irregularly-shaped FAZ.

*C*(

*t*) (Fig. 4) in the following manner. The distance transform

^{ 17 }was used to calculate the distance of all pixels outside of the FAZ, as defined by

*C*(

*t*). We discarded all pixels that were outside of 0.15° from the edge of the FAZ, as well as pixels that were in the interior of

*C*(

*t*).

^{ 18 }that surrounds arterioles, which would alter measurements of capillary density. To generate the

*L*

_{Tot}/

*A*measure, we took the sum of capillary lengths in each of the quadrants and divided by the area of the ROI contained within the quadrant. To determine statistically whether there was a difference in capillary density in different directions, we used the Kruskal-Wallis one-way ANOVA.

^{ 19 }

^{2}, and the effective diameter was 633 ± 103 μm (mean ± SD), similar to results from other studies (Table).

Method | Mean Area (mm^{2}) | Mean Diameter (μm) | Subject Criterion |
---|---|---|---|

AOSLO* | 0.323 | 633 | No ocular disease |

SLO + FA^{3} | 0.231 | 542† | Non-diabetics |

FA^{25} | 0.350 | 730 | Non-diabetics |

FA^{2} | 0.221‡ | 530 | 10 < Age < 39 |

0.292‡ | 610 | Age > 40 |

^{−1}in the Superior, Inferior, Nasal, and Temporal (S, I, N, T) directions (Fig. 8), similar in magnitude to capillaries in the brain.

^{ 16 }

^{ 10,20 }Finally, one can extend the analysis to compute statistics such as vessel density, as illustrated in this article.

^{ 21 }While we found that the contrast of vessels and flow through vessels was higher for the 532 nm laser compared with the 840 nm laser (Fig. 10), we did not observe any differences in the vessels that could be identified for analysis. However, there are major advantages to using the 840 nm laser. The SNR of the photoreceptors is much higher for videos acquired at 840 nm, an important consideration when evaluating photoreceptor health; this was because we imposed conservative light exposure limits. To ensure safe light levels, the power that reached the subjects' retinas was maintained at a level that was at 10x below the Maximum Permissible Exposure limit defined by the American National Standards Institute.

^{ 22 }Since the SNR improves as the power of light increases, and since we imposed our conservative light exposure limits, the videos acquired at 532 nm had considerably lower SNRs. Finally, the lower brightness 840 nm light source (∼50 trolands) was better for subject comfort.

^{ 3,23 }the level of detail that we achieved was not observed. Previously, the only established method for noninvasive visualization of the FAZ and parafoveal capillary network was based on the entoptic blue field phenomenon.

^{ 24 }