Visible light optical coherence tomography angiography (vis-OCTA) and local microvascular retinal oximetry in human retina

We report herein the first visible light optical coherence tomography angiography (vis-OCTA) for human retinal imaging. Compared to the existing vis-OCT systems, we devised a spectrometer with a narrower bandwidth to increase the spectral power density for OCTA imaging, while retaining the major spectral contrast in the blood. We achieved a 100 kHz A-line rate, the fastest acquisition speed reported so far for human retinal vis-OCT. We rigorously optimized the imaging protocol such that a single acquisition takes <6 seconds with a field of view (FOV) of 3×7.8 mm2. The angiography enables accurate localization of microvasculature down to the capillary level and thus enables oximetry at vessels < 100 μm in diameter. We demonstrated microvascular hemoglobin oxygen saturation (sO2) at the feeding and draining vessels at the perifoveal region. The longitudinal repeatability was assessed by <5% coefficient of variation (CV). The unique capabilities of our vis-OCTA system may allow studies on the role of microvascular oxygen in various retinal pathologies.

The first demonstration of vis-OCT dates back to 2002 [22], but the first in vivo vis-OCT retinal imaging was not reported until 2013 [4]. On the other hand, the development of vis-OCT oximetry has made significant progress in the past 10 years. The feasibility of using visible light for oximetry was first demonstrated in vitro in 2010 [23,24], and in vivo in 2011 [6]. Subsequently, the first demonstration of vis-OCT retinal oximetry was reported in 2013 in a rat retina, showing the sO2 calculations in the major arterioles and venules immediately exiting and entering the optic nerve head (ONH) [4]. When combined with the Doppler OCT measurements of blood flow, the oxygen metabolic rate from the inner retinal circulation can be quantified to access the global inner retinal functions and study the dynamic interaction between the retinal and choroidal circulations [3,15,25,26]. Later, human retinal imaging by vis-OCT was demonstrated in 2015 [27] and human retinal oximetry by vis-OCT was reported in 2017 [13,17] .
So far, most reports on vis-OCT human retinal oximetry describe vessels in close proximity with the ONH with vessel diameters above 100 µm [13,17] . The arteriovenous difference in those major vessels provides a global assessment of inner retinal metabolic activity but is less informative for localized metabolic alterations. For example, primary open-angle glaucoma (POAG) frequently manifests with visual field defects in the early stage only at the peripheral retina, gradually progressing towards the central visual field while diabetic retinopathy and macular degeneration often impact the foveal region initially. Therefore, measurement of localized inner retina metabolic change with focal oximetry in smaller vessels, even down to the capillary level, is highly desirable for better disease characterization. Therefore, our overall motivation is to perform human retinal oximetry in capillary level vessels less than100 µm in diameter by vis-OCT angiography (vis-OCTA). The paper consists of two major components. First, we implemented vis-OCTA in human retina to aid 3D localization of small vessels and capillaries. Given the sensitivity of our system, we optimized the scanning protocol to achieve a 3x7.8 mm 2 field of view. Second, we performed microvascular oximetry at the macular region, focusing on individual arterioles and venules immediately above the capillary network and then the capillary network itself after spatial averaging. The measured oximetry values were validated based on the known alternating pattern of feeding arterioles and draining venules in the macula. The sO2 from the capillary network was found to be intermediate between measured values of the arterioles and venules.

System
The vis-OCT system is modified from our previous visible and near infrared OCT setup, with only the visible light channel operating (Fig. 1a) [7]. Two edge filters selected the illumination wavelength range from 545 to 580 nm (Fig. 1b, 1c). We intentionally used a narrower bandwidth than our previous design to increase the power spectral density for subsequent vis-OCTA processing. Within the bandwidth, the large spectral contrast between oxy-and deoxyhemoglobin can still be captured (Fig. 1b). The sample arm consisted of a fiber collimator (f = 7.5 mm), a pair of galvanometer mirrors (GVS002, Thorlabs, USA), and a 2:1 telescope relay system. We also installed an electronically tunable lens (EL-3-10, Optotune AG, Switzerland) for a fast-focusing adjustment. We used a custom-made spectrometer to record the spectral interferograms. The spectrometer consists of a fiber collimator (f= 60mm), a 2400 lines/mm transmission diffraction grating (Wasatch Photonics, USA), a focusing lens (f = 150mm), and a high speed line scan camera (E2V Octoplus, Teledyne, France). The laser power on the pupil was less than 0.25mW, and the A-line speed is 100 kHz with 9.7 µs exposure time for each Aline. We characterized the roll-off performance of the system using a mirror reflectance as the sample, and an ND filter (OD=2) to attenuate the sample signal (Fig. 1d). The sensitivity is estimated to be ~82dB and the roll-off speed is ~6 dB/mm in air. The axial resolution is measured to be ~5 µm close to the zero-delay line. All the experimental procedures were approved by the Boston Medical Center Institutional Review Board and adhered to the tenets of the Declaration of Helsinki. Informed consent was obtained prior to imaging.

Vis-OCTA imaging and processing
The scanning protocol is conventional and modified from our previous paper [28]. The fast scanning galvanometer mirror was controlled by an 80% duty cycle triangle wave. The slow scanning was controlled by a ramping wave. At each slow scanning location, repetitive frames were taken to harness the motion contrast of blood flow for vis-OCTA. There are three essential parameters in the imaging protocol: numbers of repetitive frames (Nrep), the time interval between two adjacent frames (ΔT) for the motion contrast, and the A-line scanning density on the retina (ΣAline, μm -1 ). These three parameters altogether determine the vis-OCTA image quality and the field of view (FOV), and their optimization is discussed in the Results section.
The method to generate wavelength-dependent four-dimensional(x, y, z, λ) vis-OCTA and vis-OCT has been described before [25,29]. We combined the split-spectrum OCTA and the complex-signal-based optical microangiography to enhance the motion contrast [30,31]. The split-spectrum method also provides the spectral information for sO2 calculation.
The original raw spectrograms were pre-processed by normalizing the source spectrum, a DC removal, k-space resampling, and a digital dispersion compensation for improving the image sharpness [32]. A Gaussian spectral window in the wavenumber k domain (FWHM k= 0.11 μm -1 ) was then used to sweep the whole interferogram in 11 steps to generate the wavelengthdependent complex OCT signal after a series of Fourier transforms. The differential contrast between two adjacent frames at one slow scanning location was taken based on the complex OCT signal, and then the absolute value was taken as the OCTA signal. When Nrep is larger than 2, all the OCTA signals from two adjacent B-scans were averaged to ensemble an OCTA frame where C(x, z, λi) is the complex B-scan image at each spectral window λi. After vis-OCTA were generated for each Gaussian spectral window, we averaged them to compile one 3D vis-OCTA image.
For wavelength-dependent vis-OCT structural images, we simply average the absolute values of Nrep frames at each B-scan location, instead of taking differential contrast as in OCTA.

Data processing for oximetry in small vessels
We used the following workflow to extract the microvascular spectral signal and calculate the sO2 (Fig. 2a). The first step is to generate wavelength-dependent vis-OCT and vis-OCTA four dimensional (x, y, z, λi) datasets (Fig. 2b), as described above. The second step is to locate the blood vessels through en face and axial segmentations. The third step is spectral normalization and extraction to estimate sO2.
Vessel segmentation: For individual arterioles and venules, the en face maximum intensity projection (MIP) of vis-OCT was used to locate the lateral ROI for the vessels. The vessel ROI was manually segmented and stored as the vessel mask (Fig. 2c). The axial location of the vessel bottom was identified in the vis-OCTA B-scans. We detected the retinal surface by setting an intensity threshold from the structural vis-OCT B-scans. The same surface was used in both vis-OCT and vis-OCTA datasets. We then flattened the retina surface, and located the vessel bottom by the lower boundary of the vis-OCTA signal within each vessel segment (Fig.  2c). For each vessel segment, a constant depth selection was used for the spectral extraction assuming a consistent vessel size. the capillary flow signal from the en face MIP of vis-OCTA was used to isolate the capillary network. The larger vessels were marked out as shown in Fig. 2e. Then we binarized the image to serve as the capillary ROI. To locate the depth of the capillaries, the axial position of the maximum value along each vis-OCTA A-line within the capillary ROI was recorded.
Spectral extraction and normalization: After 3D segmentation, a spectrogram in terms of depth was generated. For individual arterioles and venules, the vessel surface was flattened and all vis-OCT A-lines within the vessel lateral ROI were averaged to generate a spectrogram in z and λ (Fig. 2d). Then the spectrum was obtained by averaging signals from a depth range of 10μm centered at the vessel bottom. To remove systemic bias due to propagation through the eye, we further performed a spectral normalization by the spectrum from the non-vascular nerve fiber layer tissues. For capillaries, we flattened all the capillaries and averaged the signal within the capillary lateral ROI to generate the spectrogram in z and λ as shown in Fig. 2f. The signals from a depth range of 10μm centered on the capillaries were averaged to extract the spectrum, which was further normalized by the spectrum averaged from the inner plexiform layer and the inner nuclear layer excluding the capillaries.
Spectral fitting to estimate sO2. After the spectra were extracted and normalized, we used a previously published method to calculate sO2 by matching the experimental spectra with the model [4,25] : in which I0(λ) is the spectrum of the light source; R0 is the reflectance of the reference arm and assumed to be a constant; and r(λ) (dimensionless) is the reflectance at the vessel wall, the scattering spectrum of which can be modelled by a power law r(λ) = Aλ -α , with A being a dimensionless constant and α modelling the decaying scattering spectrum from the vessel wall. The optical attenuation coefficient μ (mm −1 ) combines the absorption (μa) and scattering coefficients (μs) of the whole blood, which is both wavelength-and sO2-dependent: where W is a scaling factor for the scattering coefficient at W = 0.2. Hb and HbO2 denote the contribution from the deoxygenated and oxygenated blood, respectively. z denotes the lightpenetration depth through vessels. When taking a logarithmic operation, Equ 2 turns to a linear equation to μ, Then a least square fitting was performed to match experimental and model-based spectra to estimate sO2.

Imaging protocol optimization
We first evaluate the effect of Nrep on vis-OCTA's signal-to-noise ratio (SNR), defined by where Iv is the vis-OCTA intensity on blood vessels, and SDnv is the image intensity standard deviation within the background non-vascular areas [33]. We collected a dataset by Nrep = 12, and processed vis-OCTA images from the first Nrep frames for Nrep = 2, 4, 6, 8, 12. For example, the case of Nrep = 2 only processes the first two frames and so on. This approach ensures that the other experimental conditions are virtually identical. We can see the image quality improvement when averaging more vis-OCTA frames (Fig. 3a-3e). The image SNR increases more rapidly in the beginning and slows down when Nrep > 6 (Fig. 3f). While the image quality improves with increasing Nrep, the acquisition time proportionally increases as well. Therefore, we chose Nrep = 3 for the final imaging protocol to balance the image SNR and the total scanning time. The image is 320 by 128 pixels. We next evaluated the effect of ΔT on the image quality. The A-line scanning density was kept constant at 0.13 μm-1, while ΔT increased from 1 ms to 7 ms by increasing the number of Alines per frames from 80 to 560. As a result, the length in the fast scanning direction increases as well (Fig. 4a-4d) from 0.6 to 4.3 mm, respectively. The same FOV was imaged in all cases in Fig. 4a, so that the SNR calculation could be repeated at the same imaging location. We noticed that the image contrast changes non-monotonically, increasing until ΔT = 5ms and decreasing at ΔT = 7 ms. The initial SNR increase may be due to the larger decorrelation between two repeated frames when ΔT increases. The contrast then deteriorates when ΔT becomes longer than 5ms as clear motion artifacts start to appear (e.g. vertical stripes in Fig.  4c, d). To balance the FOV and the image quality, we chose ΔT = 5ms for our imaging protocol. At 100 kHz A-line rate and 80% duty cycle of the fast scanning, ΔT = 5ms determines the number of A-line per B-scan in our protocol to be 400, resulting in a ~3mm scanning range in fast axis.  Fig. 5 shows, the image SNR decreases with the decreased A-line scanning density. This effect is likely due to the fringe washout effect as each exposure integrate signals from the scanning length per pixel, and becomes more prominent as the scanning length increases. Then, we fixed ΣA-line = 0.13 μm -1 , ΔT = 5 ms, and 400 A-lines per frame for the final imaging protocol, which yielded an imaging length on the fast scanning direction of ~ 3 mm.

Human retina vis-OCTA
After optimizing the protocol, we performed vis-OCTA on a human volunteer as shown in Fig. 6. The FOV at one single acquisition takes 400 x 400 pixels to cover ~3x7.8 mm in the retina. The Nrep = 3 and the total time for one acquisition is 6 s. Figure 6a shows a wide field vis-OCTA mosaic including 11 acquisitions. Detailed microvasculature down to individual capillary-level can be visualized in two magnified images (Fig. 6b, 6c). After the split-spectra processing, the depth resolution was relaxed to ~27 μm, sufficient to isolate the superficial, intermediate, and deep capillary plexuses at the macula ( Fig. 1 in Appendix). The capillary contrast is higher within the nerve fiber arcade, which is consistent with the high metabolic demand in the retinal nerve fiber layer and the ganglion cells.

Microvascular sO2 in human retina in vivo
We performed vis-OCT microvascular oximetry at the foveal region as demonstrated in Fig. 7. We color-coded the vis-OCTA images with sO2 values for small vessel segments and the whole capillary bed (Fig. 7a). The alternating pattern of feeding arterioles and draining venules is apparent around fovea as demonstrated by the sO2 estimations. As expected, the averaged sO2 value over the capillary bed is intermediate between the values of the arterioles and venules. We plotted a representative spectrogram from an arterial segment, a venule segment, and the underlying capillary network (Fig. 7b-7d). Figures 7b and 7c have a high intensity at the vessel surface that attenuates when penetrating through the vessels. The depth at the vessel bottom provides sufficient signal contrast to extract reliable spectra. Figure 7d exhibits a different pattern with the strongest signal appearing within the capillaries as expected based on the stronger capillary contrast in structural vis-OCT images [34,35] .
In Fig. 7e-7g, we plotted the spectra from all the arterioles, venules, and capillary beds displayed in Fig. 7a. The spectral contrast of the blood resembles that seen with the oxygenated blood plotted in Fig. 1b, showing a peak around 560 nm. The value of sO2 estimation by spectral fitting is then plotted in Fig. 7h as the 1 st subject. We further performed the same method on two additional healthy eyes, and their results are also summarized in the same graph, and Table 1. Overall, the mean values of arterial, venous, and capillary sO2 are quite consistent among three different subjects. We also tested the repeatability of the microvascular sO2 by vis-OCTA (Fig. 7i). A total of 5 measurements were taken from the same eye on three different dates. The first three repetitions are taken on the same day, and the other two are taken on different days spanning over 5 weeks. The coefficients of variation (CV) for all measured vessels are < 3% within the same day and < 5% over all the repetitions.

Discussion
In this paper, we describe the first vis-OCTA imaging of human retina using a redesigned spectrometer and optimized imaging protocol allowing for enhanced vascular signal and localization of the microvasculature. Using our methods, we are capable of measuring sO2 in vessels smaller than 100 μm, all the way down to the capillary network. We demonstrate the robustness and repeatability o sO2 calculation. The unique advantage of vis-OCTA oximetry is the capability of excluding confounding factors from other retinal layers through 3D segmentation. In addition, the accurate vessel localization of vis-OCTA allows for measurement of capillary sO2, which is unattainable by fundus-based oximetry.
The limitation of this study is the lack of independent validation of our sO2 calculation. In our previous studies on rodent retinal oximetry, a pulse oximetry was used to serve as a gold standard for arterial sO2 close to the ONH [25,29]. sO2 alterations due to oxygen diffusion as blood travels through the retina would limit this approach for validation of measurements in microvasculature distant from the ONH. From our calculations, we observe the clear separation of alternating feeding arterioles and draining venules, which serves as a physiologic and anatomic validation. Yet we still lack a gold standard for capillary sO2. Further physiologic (e.g. hypoxia challenge) or pathologic validations with known impact on oxygen levels are warranted to rigorously validate the accuracy and sensitivity of microvascular sO2 by vis-OCTA.
We should also emphasize that all the spectral extraction was performed on structural vis-OCT signals with vis-OCTA used to provide vessel localization. Because of the stringent requirements for imaging speed and SNR for vis-OCTA, the FOV is limited to 3x7.8 mm 2 at each acquisition with 6s total acquisition time at the current system characterization. This may be a limiting factor for large-scale clinical applications. Alternatively, we could use a dualchannel system with both visible and conventional near infrared (NIR) wavelengths to simultaneously acquire OCTA images [7,36]. While the NIR channel could provide rapid OCTA with a larger FOV for vessel localization, the co-registered vis-OCT structural data could provide spectral contrast for sO2.
To summarize, we have demonstrated first visible light optical coherence tomography angiography for human retinal imaging. By achieving a 100 kHz A-line rate, the fastest acquisition speed reported so far for human retinal vis-OCT, we can acquire a field of view (FOV) of 3x7.8 mm 2 image within 6 seconds. The angiography also enables accurate localization of microvasculature to allow us to perform human retinal oximetry in vessels less than100 µm in diameter. This technical advance lays a foundation for assessing human retinal local oxygen extraction and metabolism.

Funding
The work contained in this paper has been supported by Bright Focus Foundation (G2017077, M2018132), and National Institute of Health (NIH) (R01NS108464-01, R21EY029412, R01CA232015).