Abstract

Oblique scanning laser ophthalmoscopy (oSLO) is a recently developed technique to provide three-dimensional volumetric fluorescence imaging in retinas over a large field of view, without the need for depth sectioning. In this study, we present volumetric fluorescein angiography (vFA) at 200 B-scans per second in mouse retina in vivo by oSLO. By using a low-cost industrial CMOS camera, imaging speed was improved to 2 volumes per second, ∼10 times more than our previous results. Enabled by the volumetric imaging, we visualized hemodynamics at single capillary level in a depth-dependent manner, and provided methods to quantify capillary hematocrit, absolute capillary blood flow speed, and detection of capillary flow stagnancy and stalling at different vascular layers. The quantitative metrics for capillary hemodynamics enhanced by volumetric imaging can offer valuable insight into vision science and retinal pathologies.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Fluorescein angiography (FA) is a major imaging method in ophthalmology, for care of retinal vascular diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). The fluorescein solution is administered either intravenously or orally, circulated to the retinal vasculatures, and detected upon blue light excitation.

Since the invention of FA in early 1960s [1], various retinal imaging modalities have been used to improve the resolution and image quality. Fundus photography was first used to snap-shot a two-dimensional (2D) FA from the entire field-of-view (FOV) with a flood illumination [1]. The approach is convenient, offering moderate resolution and image quality. Scanning laser ophthalmoscopy (SLO) applies a flying laser focus and a confocal gating to reject the diffusive signal and significantly improves image quality [2,3]. In recent years, adaptive optics SLO (AOSLO) achieved the diffraction-limited resolution [4,5], and enabled FA down to individual capillaries with high-speed [68]. Using the adaptive optics correction, several groups have provided hemodynamics measurements in retina in small blood vessels [915].

However, all the above modalities primarily present FA as 2D images, either integrating signals from different retinal layers or from a particular depth section. It is still challenging to perform hemodynamics analysis in different retina layers at the same time. The 2D presentation of state-of-the-art FA is a major disadvantage for a three-dimensional (3D) retinal vasculature.

To overcome this disadvantage, we recently developed a novel imaging method, named oblique scanning laser ophthalmoscopy (oSLO), and demonstrated in vivo volumetric FA (vFA) over 30° viewing angle with cellular resolution in 3D [16,17]. By using an off-axis illumination, an oblique light sheet can be generated by a scanning laser [1618]. At the same time, an oblique imaging system is aligned such that a camera sensor is conjugated with the oblique light sheet to capture a cross-sectional FA image [1620]. This mechanism uniquely enabled vFA by only one raster scan, without the need for depth sectioning. Our previous in vivo demonstration further adopted a scanning protocol for optical coherence tomography angiography (OCTA) for multimodal imaging [16]. Herein in this paper, for the first time to our knowledge, we demonstrated in vivo vFA imaging on mouse retina by oSLO with significantly increased the imaging speed, achieving vFA at 2 volumes per second (vps). The improved oSLO allows us to capture and quantify volumetric hemodynamics at individual capillary level.

2. OSLO image notation

The unique feature of oSLO is to capture cross sectional fluorescent images in retina, analogous to the perspective in OCT. We therefore follow the similar image notation. The 3D vFA image is in x, y and z axis, where x and y are the fast and slow scanning directions in the transverse/lateral plane, respectively; and z is the depth dimension. One cross sectional frame in x-z by oSLO is a B-scan, consisting of numerous A-lines.

 figure: Fig. 1.

Fig. 1. Schematic of the oblique scanning laser ophthalmoscopy (oSLO). The components within the shaded area are mounted on a 2-axis translational stage.

Download Full Size | PPT Slide | PDF

3. System and imaging methods

3.1 System setup

The system setup is modified from our previous publication [8], the schematic is shown in Fig. 1. The visible light (λ<650 nm) was filtered out by a dichroic mirror (DM1) from a super-continuum laser source (Superk EXTEME, NKT Photonics). Then the beam was polarized and dispersed by a pair of identical prisms (P1, P2). A beam block passed the blue light from ∼420 nm to 490 nm. The beam was reflected and picked by a D-shaped mirror, then collimated to a single mode fiber. The fiber delivered the blue light to an f = 6 mm collimator (L1), relayed by two sets of telescope systems (L2:L3, L4:L5) to the eye pupil, and steered by two galvanometer scanning mirrors (GM1, GM2). The second 3:1 telescope system (L4:L5) is mounted on a customized dove tail slider, which offsets the optical axis to create the oblique scanning illumination [8]. The shift of the dove tail slider is ∼3 mm. The power of blue excitation on the pupil is 30 µW. The fluorescence emission from the eye was reflected to the detection optical path by a dichroic mirror (DM3) placed in the middle of the 3:1 telescope. Another telescope (L5:L6) relayed the emission light from the pupil plane to the de-scanned galvanometer mirror (GM3). Then the beam is relayed to the objective lens (OL2, Olympus UplanFL N 20 x/0.5) by another 1:1 telescope (L7:L8). The final imaging system (OL3, Olympus UplanFL N 10 x/0.3, and L9, Navitar F2.8/50 mm) is mounted on a 3 axis stage (X, Y, and angle) to capture images by a high-speed CMOS camera (BFS-U3-51S5M-C, Point Grey, US). The entire fluorescence detection optics was mounted on a 2-axis translational stage. The angle between the final imaging system and the optical axis was ∼30 degrees. The fully-dilated pupil in rodents has an equivalent NA = 0.5, and the theoretical lateral and axial resolutions are ∼4 µm and 7 µm, respectively [18].

3.2 Image acquisition protocol

The scanning protocol is modified from an OCTA scanning protocol. Briefly, an 80% duty cycle sawtooth pattern was sent by an analog output board (NI, PCIe-6731) to control the fast x scanning GM1 (GVS001, Thorlabs, USA). A ramping voltage was sent to GM2 (GVS001, Thorlabs, USA) for the slow y scanning. The period of the sawtooth is 2.5 ms. The sawtooth pattern was repeated twice for each y scanning step for one B-scan. The exposure time of the camera is 5 ms. To maximize the speed, we used 2×2 binning and output 1224×300 pixels for B-scan in x, and z. The frame rate is limited to 200 Hz due to the overhead in readout from the camera. A LabVIEW program was written to control the scanning density and FOV for different experimental protocols, as summarized in Table 1. For high-density and high-speed vFA, 512 and 100 B-scans were acquired along y direction, respectively. To continuously monitor the hemodynamics at individual capillary segments, we repeatedly performed B-scan at one y location over time. To calculate the speed of capillary blood flow, a high-density vFA scan was first performed to map vessels in 3D, and immediately followed by alternating B-scans at two closely spaced y locations (i.e. alternating B-scans) for 300 times at a 200 Hz frame rate.

Tables Icon

Table 1. Summary of different vFA imaging protocols

3.3 Image processing

In order to analyze vFA images at different vascular layers, we referenced the depth to outer plexiform layer (OPL) where the deep capillary plexus is located. We manually pinpointed ∼10 capillaries at OPL within the cross sectional vFA images every 10 B-scans. Within those selected frames, a 2nd polynomial fitting was applied to the coordinates (x, z) of the annotated capillaries to identify OPL. Then another 2nd polynomial fitting was applied on the coordinates (y, z) of OPL along slow scanning direction for the entire volumetric dataset. By referencing to OPL, vFA signal at different retinal layers can be obtained by segmenting the signal from a desired depth range. When eye motion does not significantly impact high-speed imaging, the repeated vFA dataset can share the same OPL reference. Otherwise, the image processing would be required for each dataset.

3.4 Animal preparation

All procedures were approved by the Institutional Animal Care and Use Committee of Boston University Medical Center. Wild-type C57BL/6J mice (male at 8 weeks, Jackson Laboratory) were used in this study. Mice anesthetized with intraperitoneal injections of Ketamine (80 mg/kg) and Xylazine (10 mg/kg) were placed on a custom-made 5-axis (x, y, z translations, yaw and pitch) holder to allow adjustments of eye position and angle. 0.1 ml 10% FITC-dextran (Sigma-Aldrich, mol wt 40,000) was injected intravenously. 1% tropicamide ophthalmic solution was applied to the mice's eyes for two minutes to dilate the pupil. Then, commercial artificial tears were applied to keep the eyes moist during the experiment. A thermal lamp was used to provide heating during the experiment. After imaging, the animal was released and placed in a recovery box. The animal was closely monitored until it has regained sufficient consciousness to maintain sternal recumbency.

4. Results

4.1 Volumetric fluorescein angiography (vFA) in mouse retina in vivo

Figure 2(a) exemplifies a high-density vFA from a mouse retina in vivo. By using a CMOS camera, we improved the imaging speed by ∼10 times from our previous publication [16,17], and one high-density vFA can be acquired in ∼2.5 seconds over a 27° angle of view. Two cross sectional vFA images clearly show the distribution of microvasculature through the retina to choroid. We averaged vFA signal within a region of interest (ROI), and plotted it against the depth axis (Fig. 2(b)). Four major peaks appear in the plot, indicating retinal nerve fiber layer (rNFL), inner plexiform layer (INL), outer plexiform layer (ONL), and choroid. The distance from rNFL to OPL is roughly equal to the one from OPL to choroid, consistent with the mouse retina anatomy that the inner and outer retina has roughly the same thickness.

 figure: Fig. 2.

Fig. 2. Volumetric fluorescein angiography (vFA) in mouse retina in vivo. (a) A depth-encoded vFA image from a mouse retina over a 27° field-of-view. The pseudo-color is in HSV space. The depth coordinate of the maximum intensity denotes the hue, and the image intensity denotes the saturation and value. Two cross-sectional images are exemplified along the white dash lines. (b) The depth distribution of the vFA signal within the yellow region of interest in panel (a). NFL: nerve fiber layer; IPL: inner plexiform layer; OPL: outer plexiform layer. (c-f) The averaged vFA from NFL, IPL, OPL, and choroid, respectively. The contrast was adjusted separately for each layer. Scale bar = 200 µm.

Download Full Size | PPT Slide | PDF

The 3D capability of vFA allows us to segment and display individual microvascular layers in rNFL, IPL, OPL and choroid, as shown in Fig. 2(c)–2(f). The vascular branching can be observed from the major arterioles in rNFL, to intermedia plexus in OPL, and finally the capillary plexus in OPL. One interesting feature is that some capillaries appear merging to venules in OPL, and directly back to major vein (Fig. 2(e)), consistent to the retinal vascular structures observed in other reports in mice [21,22].

4.2 Capillary hematocrit via temporal averaging

The high-molecular weight FITC-dextran is confined within an intact retinal microvasculature and does not diffuse intracellularly. When blood cells pass in a single file through a capillary, the temporal FA signal at one y location exhibits an intermittent pattern, where bright and dark signal denote plasma and blood cells respectively (Fig. 3(a)) [8]. The 3D resolving capability of vFA allows high confidence of locating capillaries in OPL, and the intermittent fluorescence signals can be visualized at 200 Hz B-scan frame rate.

 figure: Fig. 3.

Fig. 3. Capillary hematocrit calculation via temporal averaging. (a) Illustration of the fluorescence signal generation with repeated B-scans at a single capillary. T is short for time. (b-c) An example of an en face and cross-sectional vFA image from a mouse retina in vivo. (d) The temporal vFA signal from three cross point between scanning line and blood vessel (C3 to C5) in OPL from panel (b). The green and yellow regions exemplified the region-of-interests (ROIs) for calculating vFA at 0% hematocrit in plasma, and virtually 100% hematocrit in a non-vascular area. (e) The quantitative hematocrit calculation based on the vFA signal intensity with temporal averaging every 0.5s over a total period of 2.5s. For C1 to C5, five measurements were averaged over a total 2.5s data (n = 5). Error bar = SEM (standard error of the mean). Scale bar = 50 µm.

Download Full Size | PPT Slide | PDF

Hematocrit (Hct, %), defined by volume percentage of blood cells in whole blood, was calculated first. It was recognized that the bright blocks in Fig. 3(d) were from plasma with 0% hematocrit, and the non-vascular area (BG) could equivalently and virtually represent 100% hematocrit when no FITC-dextran was present. We then randomly selected five large bright blocks, and averaged the image intensity of center regions (Fig. 3(d)) to quantify the vFA signal at 0% Hct, as I0% Hct. Similarly, the mean image intensity of five non-vascular areas quantifies the vFA signal at 100% Hct, as I100% Hct. After temporally averaging the vFA signals I, Hct at any capillaries can be quantified by

$$Hct = \frac{{I - {I_{0{\%\; }Hct}}}}{{{I_{100{\%\; }Hct}} - {I_{0{\%\; }Hct}}}} \times 100{\%}.$$
Figure 3(e) shows the results of Hct calculation from the five cross points shown in Fig. 3(b). The mean values were around 40%, close to the value under the normal physiological conditions. Hct from C1 to C5 were calculated every 0.5 second, over a total period of 2.5 s. The error bar represents the temporal Hct variation. C3 to C5 belong to a same capillary. Although their intersecting angles between the scanning line and the vessel are different, the Hct results are consistent.

4.3 Absolute capillary blood flow speed

The cluster and spacing between blood cells at individual capillaries give rise to the intermittent temporal pattern in vFA signals, as demonstrated in Fig. 3. When sampled at two closely spaced points from a capillary segment without branching or merging, two vFA temporal patterns are likely similar. The absolute capillary blood flow speed can be deterministically calculated, given the time delay between two temporal patterns and the distance between the two sampling points.

To implement this concept, we modified our scanning protocol for Fig. 3, such that a high-density vFA is first taken, immediately followed by alternative B-scans at two closely-spaced y locations for 3 seconds. The high-density vFA was used to locate the capillary segments in 3D. Since each B-scan took 5 ms, the sampling rate for the two alternating B-scans was 100 Hz. Figure 4(a) shows the overview of the deep capillary plexus at OPL segmented from the high-density vFA, proceeding to the alternating B-scans. The intersected capillaries can then be easily identified. The temporal patterns at two locations of the same capillary (C1 and C2) are plotted in Fig. 4(b), with similar patterns and a visible time delay. We calculated the temporal cross-correlation for the time delay of ΔT [s] (Fig. 4(c)), and the blood speed v [m/s] was calculated by

$$v = \frac{L}{{{\Delta }T + {T_c}}},$$
where Tc [s] is the time for each B-scan, 5 ms in our case, and L is the 3D Euclidean vessel length between C1 and C2. The mouse retinal vasculature is stratified, and the dimension herein is reduced to 2D after a 3D segmentation at OPL. We manually measured L, taking into account of the angle between the vessels and the B-scan direction, and the tortuosity. Importantly, the sign of ΔT can be either positive or negative, indicating the flow direction.

 figure: Fig. 4.

Fig. 4. Measurements of absolute blood speed at individual capillary level. (a) vFA image at deep capillary plexus at outer plexiform layer (OPL). Two alternating B-scan locations are labeled with two white lines. Bar = 0.2 mm. (b) The temporal vFA signal at two capillaries, C1 and C2, as pointed out in panel (a). (c) The temporal correlation of two temporal vFA signals between C1 and C2. The peak of the correlation measures the time delay. (d) Examples of vFA temporal signal from five capillaries, with various blood speed. (e) The capillary flow speed measured longitudinally from the same retina after anesthesia (n = 5). Error bar = SEM. The vertical axis of (b) and (d) is 10 µm.

Download Full Size | PPT Slide | PDF

To demonstrate the capability of longitudinally measuring capillary blood speed, we imaged a normal C57BL/6 mouse retina every 15 minutes after the Ketamine/Xylazine anesthesia over a course of 60 minutes. The capillary in OPL was separated to measure the blood speed, and the temporal signal pair from capillaries at each time point are exemplified in Fig. 4(d). While the pattern may be inconsistent sporadically, the overall correlation and time delay were rather clear. The frequency of the vFA signal intermittence decreases and the time delay increases, both indicating a slower blood speed at capillary level after prolonged anesthesia. The statistical result was shown in Fig. 4(e), the mean speed of the capillary in OPL was ∼4.5 mm/s immediately after anesthesia and dropped to ∼0.9 mm/s 60 minutes later. This reduction may be due to the body temperature drop and therefore reduced blood pressure over the course of the experiment.

4.4 Quantitative metrics for retinal capillary hemodynamics in 3D

At a 200 Hz frame rate, we achieved 2 vps vFA with 100 B-scans per volume. Figure 5 shows the capillary plexuses located in IPL and OPL at different time stamps separately, during a course of 15s acquisition. The intermittent vFA signals within capillaries are also visible due to the passing blood cells (See Visualization 1).

 figure: Fig. 5.

Fig. 5. Capillary flow dynamics in high-speed vFA at 2 volume per second segmented at the inner plexiform layer, IPL (a-d) and outer plexiform layer, OPL (e-h). Yellow arrows point to one capillary segment showing a transient ischemia, and the red arrows point to a capillary having stalled blood flow. Scale bar = 100 µm.

Download Full Size | PPT Slide | PDF

We used two metrics: hematocrit, and frame-to-frame image cross-correlation, to quantitatively characterize the capillary hemodynamics in 3D vFA. For hematocrit, the similar approach was taken as described in Fig. 3, such that we took vFA intensity from a bright segment of capillary and the non-vascular background to represent 0% and 100% Hct, respectively. We then manually segmented each capillary, and calculated the mean value of the vFA intensity within each segment to quantify Hct by Eq. (1). Fifteen capillaries from IPL and OPL were segmented (Fig. 6(a), 6(b)). A heat map of hematocrit summarized results over time (Fig. 6(c)). While the mean hematocrit is ∼40% (Fig. 6(d)) over 15 s, the temporal variation for each capillary, as well as the spatial variation among different capillaries are apparent from the heat map. For segment #3, there appears a transient ischemia for 1 s, followed by a quick recovery, which is easily appreciated in Visualization 1.

 figure: Fig. 6.

Fig. 6. (a-b) vFA segmented at inner plexiform (IPL) and outer plexiform layer (OPL) with denoted capillary segments. (c)The heat map showing longitudinal hematocrit change at each capillary segment over a total period of 15s with 0.5s interval. (d) The averaged hematocrit over 15s for each capillary segment labeled in panel (a-b) (n = 15). (e) The heat map showing the temporal image correlation between two vFA collected 0.5s apart within each capillary segment. (f) The averaged image correlation averaged over a total period of 15s (n = 14). Error bar = SEM. Scale bar = 100 µm.

Download Full Size | PPT Slide | PDF

Within each segment, we then calculated the image cross-correlation between adjacent frames to detect the capillary blood flow stagnancy and stalling as the stagnancy would result in high image cross-correlation. A heat map was also generated to present the temporal and spatial variation (Fig. 6(e)). Capillary stalling can be observed in segment #6 with absence of blood flow for several seconds (See Visualization 1). We can also observe overall higher cross-correlation within OPL than IPL, suggesting slower blood flow in the deep capillary plexus presumably to allow better oxygen perfusion (Fig. 6(e)-(f)).

5. Discussion

Herein we presented a volumetric fluorescein angiography by oblique scanning laser ophthalmoscopy in mouse retina at 200 fps in vivo. For the first time to our knowledge, 3D fluorescein distribution within mouse retinal microvasculature at capillary level in vivo can be rapidly imaged at 2 vps. The imaging speed is significantly improved over our previous results in rat retina [16,17], and allows measurement and quantification of capillary hemodynamics at different retinal layers. We further provided quantitative metrics to measure volumetric capillary hematocrit and absolute capillary blood flow speed.

Using the intermittent fluorescein signal for capillary hemodynamics has been previously reported [8]. The advantage of using oSLO is that we now can discern capillary FA signals from different depths at a cross-sectional view, without the need for any depth sectioning. The 3D capability allows segmentation of specific retinal layers excluding the choroidal background, and the confounding signals from other inner retinal layers. This leads to high confidence of locating individual capillary segment from a relatively large FOV, as demonstrated in Fig. 4. Without the capability of depth segmentation, the temporal correlation methods at two adjacent B-scan location will be confounded by signals from overlapping capillaries from different depths.

The imaging speed for vFA ultimately defines the highest capillary blood flow speed measurable in Fig. 4. In Eq. (2), the least increment of the temporal correlation ΔT is 2 × 5 = 10 ms without signal interpolation and ${T_c}$ is 5 ms, at the speed of 200 Hz frame rate. The distance between two alternating B-scan is 36 µm in our alternating B-scan protocol, and thus the maximum projective speed vp is ∼7.2 mm/s when the vessel is perpendicular to the scanning direction. This detection range appears to be sufficient at the normal physiological conditions for mice that the average speed in Fig. 4(e) was 4.5 mm/s immediately after anesthesia. The detection range can be expanded by increasing the distance between the two alternating B-scans. However, it will reduce the probability for finding eligible vessel segment without branching or merging. The same consideration is applied in longitudinal vFA imaging in Fig. 5. At current speed of 2 vps, vFA image temporal correlation in Fig. 6(e)–6(f) does not directly measure blood flow speed. However, when the capillary flow is significantly stalled or slowed down, the temporal correlation should be correlated to the blood speed, as can be seen for capillary segment #10 in Fig. 6(e). At current speed, we did not observe significant motion artifacts during our experiments, as shown in Fig. 4(a) and Visualization 1. The respiration was suppressed by Ketamine/Xylazine anesthesia and the mouse head was further immobilized by a bite bar.

In comparison to AOSLO that tracks blood cells in real time for blood speed measurements, our method uses the temporal delay between two sampling points at individual capillary segments. Our capillary blood speed measurements were in the range of several millimeters per second, in a general agreement with the results by AOSLO [8]. As oSLO obtains a 3D vascular map, the complex capillary geometry (e.g. location, direction and tortuosity) can be taken into account in the calculation of the 3D vessel length L. However, the use of two alternating B-scans poses several limitations. First, the method is selective in vessel orientations. When a capillary segment is oriented more parallel to the scanning direction, it is less likely to be intersected by both alternating B-scans. Second, the method is susceptible to capillary branching or merging, which would disturb the temporal patterns between two sampling points. Third, the acquisition of the temporal patterns requires repetitive measurements, which can be time-consuming.

The key requirement for our method to measure blood speed is to observe intermittent fluorescence signals, which is best suited for capillaries. For large arterioles and venules, this method would likely fail for two major reasons. First, the large vessels have higher blood velocity, which may exceed the detectable range determined by the frame rate, as we discussed above. Second, in large vessels, blood cells do not travel in a single file, and it is difficult to generate the intermittent fluorescence patterns, as shown in Visualization 1. For vessels with size comparable to capillaries, this method may still be applicable as long as the contrast of the intermittent fluorescence pattern is sufficient, and the temporal correlation can be established at two sampling points.

The limitation of the current oSLO setup is the reduced image quality at the peripheral image at ∼30° viewing angle. This is largely inherent with the aberration in mouse eye, which may be effectively reduced using contact lens [23]. The imaging speed can also be improved by using more sensitive detector, and optimization of the imaging system. We also need to consider the flicker response by repetitive scanning and imaging. Since the flicker response frequency is typically <35 Hz [24,25], continuous vFA would likely introduce functional changes when we further increase the volumetric imaging rate.

VFA provides unique opportunities to characterize the hemodynamics at the capillary level over a 3D volume of view. Hematocrit, blood flow speed, and capillary stalling provides a comprehensive view of the perfusion function within the microvascular system, which is both fundamentally and pathologically important in broad range of diseases, such as cancers, neurodegeneration, diabetic retinopathy, and macular degeneration. The quantitative metrics can be valuable approach for the purpose of diagnosis and phenotyping within a living biological system.

Funding

National Institute of Health (R01CA224911, R01CA232015, R01NS108464, R21EY029412); Bright Focus Foundation (G2017077, M2018132).

Disclosures

The authors declare no conflicts of interest.

References

1. H. R. Novotny and D. L. Alvis, “A method of photographing fluorescence in circulating blood in the human retina,” Circulation 24(1), 82–86 (1961). [CrossRef]  

2. R. H. Webb, G. W. Hughes, and F. C. Delori, “Confocal scanning laser ophthalmoscope,” Appl. Opt. 26(8), 1492–1499 (1987). [CrossRef]  

3. P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015). [CrossRef]  

4. A. Roorda, F. Romero-Borja, W. J. Donnelly, H. Queener, T. J. Hebert, and M. C. W. Campbell, “Adaptive optics scanning laser ophthalmoscopy,” Opt. Express 10(9), 405–412 (2002). [CrossRef]  

5. J. Liang, D. R. Williams, and D. T. Miller, “Supernormal vision and high-resolution retinal imaging through adaptive optics,” J. Opt. Soc. Am. A 14(11), 2884–2892 (1997). [CrossRef]  

6. T. Y. P. Chui, M. Dubow, A. Pinhas, N. Shah, A. Gan, R. Weitz, Y. N. Sulai, A. Dubra, and R. B. Rosen, “Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging,” Biomed. Opt. Express 5(4), 1173–1189 (2014). [CrossRef]  

7. S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016). [CrossRef]  

8. A. Guevara-Torres, A. Joseph, and J. B. Schallek, “Label free measurement of retinal blood cell flux, velocity, hematocrit and capillary width in the living mouse eye,” Biomed. Opt. Express 7(10), 4228–4249 (2016). [CrossRef]  

9. P. Bedggood and A. Metha, “Direct visualization and characterization of erythrocyte flow in human retinal capillaries,” Biomed. Opt. Express 3(12), 3264–3277 (2012). [CrossRef]  

10. J. Tam, P. Tiruveedhula, and A. Roorda, “Characterization of single-file flow through human retinal parafoveal capillaries using an adaptive optics scanning laser ophthalmoscope,” Biomed. Opt. Express 2(4), 781–793 (2011). [CrossRef]  

11. Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011). [CrossRef]  

12. Z. Y. Zhong, B. L. Petrig, X. F. Qi, and S. A. Burns, “In vivo measurement of erythrocyte velocity and retinal blood flow using adaptive optics scanning laser ophthalmoscopy,” Opt. Express 16(17), 12746–12756 (2008). [CrossRef]  

13. A. de Castro, G. Huang, L. Sawides, T. Luo, and S. A. Burns, “Rapid high resolution imaging with a dual-channel scanning technique,” Opt. Lett. 41(8), 1881–1884 (2016). [CrossRef]  

14. J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017). [CrossRef]  

15. A. Joseph, A. Guevara-Torres, and J. Schallek, “Imaging single-cell blood flow in the smallest to largest vessels in the living retina,” eLife 8, e45077 (2019). [CrossRef]  

16. L. Zhang, W. Y. Song, D. Shao, S. Zhang, M. Desai, S. Ness, S. Roy, and J. Yi, “Volumetric fluorescence retinal imaging in vivo over a 30-degree field of view by oblique scanning laser ophthalmoscopy (oSLO),” Biomed. Opt. Express 9(1), 25–40 (2018). [CrossRef]  

17. W. Y. Song, L. B. Zhou, and J. Yi, “Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (OSLO) and Optical Coherence Tomography (OCT),” J. Visualized Exp. 138, e57814 (2018). [CrossRef]  

18. L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017). [CrossRef]  

19. C. Dunsby, “Optically sectioned imaging by oblique plane microscopy,” Opt. Express 16(25), 20306–20316 (2008). [CrossRef]  

20. M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015). [CrossRef]  

21. Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014). [CrossRef]  

22. S. H. Pi, A. Camino, X. Wei, J. Simonett, W. Cepurna, D. Huang, J. C. Morrison, and Y. L. Jia, “Rodent retinal circulation organization and oxygen metabolism revealed by visible-light optical coherence tomography,” Biomed. Opt. Express 9(11), 5851–5862 (2018). [CrossRef]  

23. P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015). [CrossRef]  

24. K. R. Alexander, A. Raghuram, and J. J. McAnany, “Comparison of spectral measures of period doubling in the cone flicker electroretinogram,” Doc. Ophthalmol. 117(3), 197–203 (2008). [CrossRef]  

25. X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015). [CrossRef]  

References

  • View by:
  • |
  • |
  • |

  1. H. R. Novotny and D. L. Alvis, “A method of photographing fluorescence in circulating blood in the human retina,” Circulation 24(1), 82–86 (1961).
    [Crossref]
  2. R. H. Webb, G. W. Hughes, and F. C. Delori, “Confocal scanning laser ophthalmoscope,” Appl. Opt. 26(8), 1492–1499 (1987).
    [Crossref]
  3. P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
    [Crossref]
  4. A. Roorda, F. Romero-Borja, W. J. Donnelly, H. Queener, T. J. Hebert, and M. C. W. Campbell, “Adaptive optics scanning laser ophthalmoscopy,” Opt. Express 10(9), 405–412 (2002).
    [Crossref]
  5. J. Liang, D. R. Williams, and D. T. Miller, “Supernormal vision and high-resolution retinal imaging through adaptive optics,” J. Opt. Soc. Am. A 14(11), 2884–2892 (1997).
    [Crossref]
  6. T. Y. P. Chui, M. Dubow, A. Pinhas, N. Shah, A. Gan, R. Weitz, Y. N. Sulai, A. Dubra, and R. B. Rosen, “Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging,” Biomed. Opt. Express 5(4), 1173–1189 (2014).
    [Crossref]
  7. S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
    [Crossref]
  8. A. Guevara-Torres, A. Joseph, and J. B. Schallek, “Label free measurement of retinal blood cell flux, velocity, hematocrit and capillary width in the living mouse eye,” Biomed. Opt. Express 7(10), 4228–4249 (2016).
    [Crossref]
  9. P. Bedggood and A. Metha, “Direct visualization and characterization of erythrocyte flow in human retinal capillaries,” Biomed. Opt. Express 3(12), 3264–3277 (2012).
    [Crossref]
  10. J. Tam, P. Tiruveedhula, and A. Roorda, “Characterization of single-file flow through human retinal parafoveal capillaries using an adaptive optics scanning laser ophthalmoscope,” Biomed. Opt. Express 2(4), 781–793 (2011).
    [Crossref]
  11. Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011).
    [Crossref]
  12. Z. Y. Zhong, B. L. Petrig, X. F. Qi, and S. A. Burns, “In vivo measurement of erythrocyte velocity and retinal blood flow using adaptive optics scanning laser ophthalmoscopy,” Opt. Express 16(17), 12746–12756 (2008).
    [Crossref]
  13. A. de Castro, G. Huang, L. Sawides, T. Luo, and S. A. Burns, “Rapid high resolution imaging with a dual-channel scanning technique,” Opt. Lett. 41(8), 1881–1884 (2016).
    [Crossref]
  14. J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017).
    [Crossref]
  15. A. Joseph, A. Guevara-Torres, and J. Schallek, “Imaging single-cell blood flow in the smallest to largest vessels in the living retina,” eLife 8, e45077 (2019).
    [Crossref]
  16. L. Zhang, W. Y. Song, D. Shao, S. Zhang, M. Desai, S. Ness, S. Roy, and J. Yi, “Volumetric fluorescence retinal imaging in vivo over a 30-degree field of view by oblique scanning laser ophthalmoscopy (oSLO),” Biomed. Opt. Express 9(1), 25–40 (2018).
    [Crossref]
  17. W. Y. Song, L. B. Zhou, and J. Yi, “Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (OSLO) and Optical Coherence Tomography (OCT),” J. Visualized Exp. 138, e57814 (2018).
    [Crossref]
  18. L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
    [Crossref]
  19. C. Dunsby, “Optically sectioned imaging by oblique plane microscopy,” Opt. Express 16(25), 20306–20316 (2008).
    [Crossref]
  20. M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
    [Crossref]
  21. Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
    [Crossref]
  22. S. H. Pi, A. Camino, X. Wei, J. Simonett, W. Cepurna, D. Huang, J. C. Morrison, and Y. L. Jia, “Rodent retinal circulation organization and oxygen metabolism revealed by visible-light optical coherence tomography,” Biomed. Opt. Express 9(11), 5851–5862 (2018).
    [Crossref]
  23. P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
    [Crossref]
  24. K. R. Alexander, A. Raghuram, and J. J. McAnany, “Comparison of spectral measures of period doubling in the cone flicker electroretinogram,” Doc. Ophthalmol. 117(3), 197–203 (2008).
    [Crossref]
  25. X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
    [Crossref]

2019 (1)

A. Joseph, A. Guevara-Torres, and J. Schallek, “Imaging single-cell blood flow in the smallest to largest vessels in the living retina,” eLife 8, e45077 (2019).
[Crossref]

2018 (3)

2017 (2)

J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017).
[Crossref]

L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
[Crossref]

2016 (3)

A. de Castro, G. Huang, L. Sawides, T. Luo, and S. A. Burns, “Rapid high resolution imaging with a dual-channel scanning technique,” Opt. Lett. 41(8), 1881–1884 (2016).
[Crossref]

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

A. Guevara-Torres, A. Joseph, and J. B. Schallek, “Label free measurement of retinal blood cell flux, velocity, hematocrit and capillary width in the living mouse eye,” Biomed. Opt. Express 7(10), 4228–4249 (2016).
[Crossref]

2015 (4)

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

2014 (2)

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

T. Y. P. Chui, M. Dubow, A. Pinhas, N. Shah, A. Gan, R. Weitz, Y. N. Sulai, A. Dubra, and R. B. Rosen, “Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging,” Biomed. Opt. Express 5(4), 1173–1189 (2014).
[Crossref]

2012 (1)

2011 (2)

J. Tam, P. Tiruveedhula, and A. Roorda, “Characterization of single-file flow through human retinal parafoveal capillaries using an adaptive optics scanning laser ophthalmoscope,” Biomed. Opt. Express 2(4), 781–793 (2011).
[Crossref]

Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011).
[Crossref]

2008 (3)

2002 (1)

1997 (1)

1987 (1)

1961 (1)

H. R. Novotny and D. L. Alvis, “A method of photographing fluorescence in circulating blood in the human retina,” Circulation 24(1), 82–86 (1961).
[Crossref]

Alexander, K. R.

K. R. Alexander, A. Raghuram, and J. J. McAnany, “Comparison of spectral measures of period doubling in the cone flicker electroretinogram,” Doc. Ophthalmol. 117(3), 197–203 (2008).
[Crossref]

Alpers, C. E.

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

Alvis, D. L.

H. R. Novotny and D. L. Alvis, “A method of photographing fluorescence in circulating blood in the human retina,” Circulation 24(1), 82–86 (1961).
[Crossref]

Bedggood, P.

Bouchard, M. B.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Bruno, R. M.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Burns, M. E.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Burns, S. A.

Camino, A.

Campbell, M. C. W.

Capilla, A.

L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
[Crossref]

Carroll, J.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

Cepurna, W.

Chang, B.

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

Chao, J. R.

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

Chui, T. Y. P.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

T. Y. P. Chui, M. Dubow, A. Pinhas, N. Shah, A. Gan, R. Weitz, Y. N. Sulai, A. Dubra, and R. B. Rosen, “Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging,” Biomed. Opt. Express 5(4), 1173–1189 (2014).
[Crossref]

Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011).
[Crossref]

Dai, X. F.

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

de Castro, A.

Delori, F. C.

Desai, M.

Donnelly, W. J.

Dubow, M.

Dubra, A.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

T. Y. P. Chui, M. Dubow, A. Pinhas, N. Shah, A. Gan, R. Weitz, Y. N. Sulai, A. Dubra, and R. B. Rosen, “Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging,” Biomed. Opt. Express 5(4), 1173–1189 (2014).
[Crossref]

Dunsby, C.

Efstathiadis, E.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

Gan, A.

Geyman, L.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

Grueber, W. B.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Gu, B. Y.

J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017).
[Crossref]

Guevara-Torres, A.

A. Joseph, A. Guevara-Torres, and J. Schallek, “Imaging single-cell blood flow in the smallest to largest vessels in the living retina,” eLife 8, e45077 (2019).
[Crossref]

A. Guevara-Torres, A. Joseph, and J. B. Schallek, “Label free measurement of retinal blood cell flux, velocity, hematocrit and capillary width in the living mouse eye,” Biomed. Opt. Express 7(10), 4228–4249 (2016).
[Crossref]

He, Y.

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

Hebert, T. J.

Hillman, E. M. C.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Huang, D.

Huang, G.

Hudkins, K. L.

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

Hughes, G. W.

Jia, Y. L.

Jian, Y.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Jian, Y. F.

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Joseph, A.

A. Joseph, A. Guevara-Torres, and J. Schallek, “Imaging single-cell blood flow in the smallest to largest vessels in the living retina,” eLife 8, e45077 (2019).
[Crossref]

A. Guevara-Torres, A. Joseph, and J. B. Schallek, “Label free measurement of retinal blood cell flux, velocity, hematocrit and capillary width in the living mouse eye,” Biomed. Opt. Express 7(10), 4228–4249 (2016).
[Crossref]

Krawitz, B.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

Lacefield, C.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Lam, K. S.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Li, Y.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Li, Y. P.

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Liang, J.

Lu, J.

J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017).
[Crossref]

Luo, T.

Mann, R. S.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

McAnany, J. J.

K. R. Alexander, A. Raghuram, and J. J. McAnany, “Comparison of spectral measures of period doubling in the cone flicker electroretinogram,” Doc. Ophthalmol. 117(3), 197–203 (2008).
[Crossref]

Mendes, C. S.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Metha, A.

Miller, D. T.

Mo, S.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

Morrison, J. C.

Mostoslavsky, G.

L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
[Crossref]

Ness, S.

Novotny, H. R.

H. R. Novotny and D. L. Alvis, “A method of photographing fluorescence in circulating blood in the human retina,” Circulation 24(1), 82–86 (1961).
[Crossref]

Pang, J. J.

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

Petrig, B. L.

Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011).
[Crossref]

Z. Y. Zhong, B. L. Petrig, X. F. Qi, and S. A. Burns, “In vivo measurement of erythrocyte velocity and retinal blood flow using adaptive optics scanning laser ophthalmoscopy,” Opt. Express 16(17), 12746–12756 (2008).
[Crossref]

Pi, S. H.

Pinhas, A.

Pugh, E. N.

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Qi, X. F.

Qi, Y.

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

Queener, H.

Raghuram, A.

K. R. Alexander, A. Raghuram, and J. J. McAnany, “Comparison of spectral measures of period doubling in the cone flicker electroretinogram,” Doc. Ophthalmol. 117(3), 197–203 (2008).
[Crossref]

Romero-Borja, F.

Roorda, A.

Rosen, R. B.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

T. Y. P. Chui, M. Dubow, A. Pinhas, N. Shah, A. Gan, R. Weitz, Y. N. Sulai, A. Dubra, and R. B. Rosen, “Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging,” Biomed. Opt. Express 5(4), 1173–1189 (2014).
[Crossref]

Roy, S.

Sarunic, M. V.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Sawides, L.

Schallek, J.

A. Joseph, A. Guevara-Torres, and J. Schallek, “Imaging single-cell blood flow in the smallest to largest vessels in the living retina,” eLife 8, e45077 (2019).
[Crossref]

Schallek, J. B.

Shah, N.

Shao, D.

Simonett, J.

Song, H. X.

Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011).
[Crossref]

Song, W. Y.

W. Y. Song, L. B. Zhou, and J. Yi, “Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (OSLO) and Optical Coherence Tomography (OCT),” J. Visualized Exp. 138, e57814 (2018).
[Crossref]

L. Zhang, W. Y. Song, D. Shao, S. Zhang, M. Desai, S. Ness, S. Roy, and J. Yi, “Volumetric fluorescence retinal imaging in vivo over a 30-degree field of view by oblique scanning laser ophthalmoscopy (oSLO),” Biomed. Opt. Express 9(1), 25–40 (2018).
[Crossref]

L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
[Crossref]

Sulai, Y. N.

Tam, J.

Tiruveedhula, P.

Voleti, V.

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Wang, R. K. K.

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

Wang, X.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Wang, X. L.

J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017).
[Crossref]

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Webb, R. H.

Wei, X.

Weitz, R.

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

T. Y. P. Chui, M. Dubow, A. Pinhas, N. Shah, A. Gan, R. Weitz, Y. N. Sulai, A. Dubra, and R. B. Rosen, “Comparison of adaptive optics scanning light ophthalmoscopic fluorescein angiography and offset pinhole imaging,” Biomed. Opt. Express 5(4), 1173–1189 (2014).
[Crossref]

Wietecha, T.

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

Williams, D. R.

Yi, J.

W. Y. Song, L. B. Zhou, and J. Yi, “Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (OSLO) and Optical Coherence Tomography (OCT),” J. Visualized Exp. 138, e57814 (2018).
[Crossref]

L. Zhang, W. Y. Song, D. Shao, S. Zhang, M. Desai, S. Ness, S. Roy, and J. Yi, “Volumetric fluorescence retinal imaging in vivo over a 30-degree field of view by oblique scanning laser ophthalmoscopy (oSLO),” Biomed. Opt. Express 9(1), 25–40 (2018).
[Crossref]

L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
[Crossref]

Zam, A.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Zawadzki, R. J.

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Zhang, H.

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

Zhang, L.

L. Zhang, W. Y. Song, D. Shao, S. Zhang, M. Desai, S. Ness, S. Roy, and J. Yi, “Volumetric fluorescence retinal imaging in vivo over a 30-degree field of view by oblique scanning laser ophthalmoscopy (oSLO),” Biomed. Opt. Express 9(1), 25–40 (2018).
[Crossref]

L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
[Crossref]

Zhang, P.

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Zhang, P. F.

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

Zhang, S.

Zhang, Y. H.

J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017).
[Crossref]

Zhi, Z. W.

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

Zhong, Z. Y.

Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011).
[Crossref]

Z. Y. Zhong, B. L. Petrig, X. F. Qi, and S. A. Burns, “In vivo measurement of erythrocyte velocity and retinal blood flow using adaptive optics scanning laser ophthalmoscopy,” Opt. Express 16(17), 12746–12756 (2008).
[Crossref]

Zhou, L. B.

W. Y. Song, L. B. Zhou, and J. Yi, “Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (OSLO) and Optical Coherence Tomography (OCT),” J. Visualized Exp. 138, e57814 (2018).
[Crossref]

Appl. Opt. (1)

Biomed. Opt. Express (6)

Circulation (1)

H. R. Novotny and D. L. Alvis, “A method of photographing fluorescence in circulating blood in the human retina,” Circulation 24(1), 82–86 (1961).
[Crossref]

Doc. Ophthalmol. (1)

K. R. Alexander, A. Raghuram, and J. J. McAnany, “Comparison of spectral measures of period doubling in the cone flicker electroretinogram,” Doc. Ophthalmol. 117(3), 197–203 (2008).
[Crossref]

eLife (1)

A. Joseph, A. Guevara-Torres, and J. Schallek, “Imaging single-cell blood flow in the smallest to largest vessels in the living retina,” eLife 8, e45077 (2019).
[Crossref]

Invest. Ophthalmol. Visual Sci. (3)

Z. W. Zhi, J. R. Chao, T. Wietecha, K. L. Hudkins, C. E. Alpers, and R. K. K. Wang, “Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography,” Invest. Ophthalmol. Visual Sci. 55(2), 1024–1030 (2014).
[Crossref]

S. Mo, B. Krawitz, E. Efstathiadis, L. Geyman, R. Weitz, T. Y. P. Chui, J. Carroll, A. Dubra, and R. B. Rosen, “Imaging Foveal Microvasculature: Optical Coherence Tomography Angiography Versus Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography,” Invest. Ophthalmol. Visual Sci. 57(9), OCT130 (2016).
[Crossref]

Z. Y. Zhong, H. X. Song, T. Y. P. Chui, B. L. Petrig, and S. A. Burns, “Noninvasive Measurements and Analysis of Blood Velocity Profiles in Human Retinal Vessels,” Invest. Ophthalmol. Visual Sci. 52(7), 4151–4157 (2011).
[Crossref]

J. Biomed. Opt. (2)

P. F. Zhang, A. Zam, Y. F. Jian, X. L. Wang, Y. P. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy-optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Muller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

P. Zhang, A. Zam, Y. Jian, X. Wang, Y. Li, K. S. Lam, M. E. Burns, M. V. Sarunic, E. N. Pugh, and R. J. Zawadzki, “In vivo wide-field multispectral scanning laser ophthalmoscopy–optical coherence tomography mouse retinal imager: longitudinal imaging of ganglion cells, microglia, and Müller glia, and mapping of the mouse retinal and choroidal vasculature,” J. Biomed. Opt. 20(12), 126005 (2015).
[Crossref]

J. Opt. Soc. Am. A (1)

J. Visualized Exp. (1)

W. Y. Song, L. B. Zhou, and J. Yi, “Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy (OSLO) and Optical Coherence Tomography (OCT),” J. Visualized Exp. 138, e57814 (2018).
[Crossref]

Nat. Photonics (1)

M. B. Bouchard, V. Voleti, C. S. Mendes, C. Lacefield, W. B. Grueber, R. S. Mann, R. M. Bruno, and E. M. C. Hillman, “Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms,” Nat. Photonics 9(2), 113–119 (2015).
[Crossref]

Opt. Express (3)

Opt. Lett. (1)

PLoS One (2)

J. Lu, B. Y. Gu, X. L. Wang, and Y. H. Zhang, “High-speed adaptive optics line scan confocal retinal imaging for human eye,” PLoS One 12(3), e0169358 (2017).
[Crossref]

X. F. Dai, H. Zhang, Y. He, Y. Qi, B. Chang, and J. J. Pang, “The Frequency-Response Electroretinogram Distinguishes Cone and Abnormal Rod Function in rd12 Mice,” PLoS One 10(2), e0117570 (2015).
[Crossref]

Sci. Rep. (1)

L. Zhang, A. Capilla, W. Y. Song, G. Mostoslavsky, and J. Yi, “Oblique scanning laser microscopy for simultaneously volumetric structural and molecular imaging using only one raster scan,” Sci. Rep. 7(1), 8591 (2017).
[Crossref]

Supplementary Material (1)

NameDescription
» Visualization 1       Quantitative metrics for retinal capillary hemodynamics in 3D

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (6)

Fig. 1.
Fig. 1. Schematic of the oblique scanning laser ophthalmoscopy (oSLO). The components within the shaded area are mounted on a 2-axis translational stage.
Fig. 2.
Fig. 2. Volumetric fluorescein angiography (vFA) in mouse retina in vivo. (a) A depth-encoded vFA image from a mouse retina over a 27° field-of-view. The pseudo-color is in HSV space. The depth coordinate of the maximum intensity denotes the hue, and the image intensity denotes the saturation and value. Two cross-sectional images are exemplified along the white dash lines. (b) The depth distribution of the vFA signal within the yellow region of interest in panel (a). NFL: nerve fiber layer; IPL: inner plexiform layer; OPL: outer plexiform layer. (c-f) The averaged vFA from NFL, IPL, OPL, and choroid, respectively. The contrast was adjusted separately for each layer. Scale bar = 200 µm.
Fig. 3.
Fig. 3. Capillary hematocrit calculation via temporal averaging. (a) Illustration of the fluorescence signal generation with repeated B-scans at a single capillary. T is short for time. (b-c) An example of an en face and cross-sectional vFA image from a mouse retina in vivo. (d) The temporal vFA signal from three cross point between scanning line and blood vessel (C3 to C5) in OPL from panel (b). The green and yellow regions exemplified the region-of-interests (ROIs) for calculating vFA at 0% hematocrit in plasma, and virtually 100% hematocrit in a non-vascular area. (e) The quantitative hematocrit calculation based on the vFA signal intensity with temporal averaging every 0.5s over a total period of 2.5s. For C1 to C5, five measurements were averaged over a total 2.5s data (n = 5). Error bar = SEM (standard error of the mean). Scale bar = 50 µm.
Fig. 4.
Fig. 4. Measurements of absolute blood speed at individual capillary level. (a) vFA image at deep capillary plexus at outer plexiform layer (OPL). Two alternating B-scan locations are labeled with two white lines. Bar = 0.2 mm. (b) The temporal vFA signal at two capillaries, C1 and C2, as pointed out in panel (a). (c) The temporal correlation of two temporal vFA signals between C1 and C2. The peak of the correlation measures the time delay. (d) Examples of vFA temporal signal from five capillaries, with various blood speed. (e) The capillary flow speed measured longitudinally from the same retina after anesthesia (n = 5). Error bar = SEM. The vertical axis of (b) and (d) is 10 µm.
Fig. 5.
Fig. 5. Capillary flow dynamics in high-speed vFA at 2 volume per second segmented at the inner plexiform layer, IPL (a-d) and outer plexiform layer, OPL (e-h). Yellow arrows point to one capillary segment showing a transient ischemia, and the red arrows point to a capillary having stalled blood flow. Scale bar = 100 µm.
Fig. 6.
Fig. 6. (a-b) vFA segmented at inner plexiform (IPL) and outer plexiform layer (OPL) with denoted capillary segments. (c)The heat map showing longitudinal hematocrit change at each capillary segment over a total period of 15s with 0.5s interval. (d) The averaged hematocrit over 15s for each capillary segment labeled in panel (a-b) (n = 15). (e) The heat map showing the temporal image correlation between two vFA collected 0.5s apart within each capillary segment. (f) The averaged image correlation averaged over a total period of 15s (n = 14). Error bar = SEM. Scale bar = 100 µm.

Tables (1)

Tables Icon

Table 1. Summary of different vFA imaging protocols

Equations (2)

Equations on this page are rendered with MathJax. Learn more.

H c t = I I 0 % H c t I 100 % H c t I 0 % H c t × 100 % .
v = L Δ T + T c ,

Metrics