Skip to main content
NCBI home page
International Journal of Ophthalmology logoLink to International Journal of Ophthalmology
. 2021 Jan 18;14(1):57–63. doi: 10.18240/ijo.2021.01.08

Exploratory study of non-invasive, high-resolution functional macular imaging in subjects with diabetic retinopathy

Thalmon R Campagnoli 1, Gábor Márk Somfai 1,2, Jing Tian 1, Delia Cabrera DeBuc 1, William E Smiddy 1
PMCID: PMC7790667  PMID: 33469484

Abstract

AIM

To evaluate a high-resolution functional imaging device that yields quantitative data regarding macular blood flow and capillary network features in eyes with diabetic retinopathy (DR).

METHODS

Prospective, cross-sectional comparative case-series in which blood flow velocities (BFVs) and non-invasive capillary perfusion maps (nCPMs) in macular vessels were measured in patients with DR and in healthy controls using the Retinal Functional Imager (RFI) device.

RESULTS

A total of 27 eyes of 21 subjects were studied [9 eyes nonproliferative diabetic retinopathy (NPDR), 9 eyes proliferative diabetic retinopathy (PDR) and 9 controls]. All diabetic patients were type 2. All patients with NPDR and 5 eyes with PDR also had diabetic macular edema (DME). The NPDR group included eyes with severe (n=3) and moderate NPDR (n=6), and were symptomatic. A significant decrease in venular BFVs was observed in the macular region of PDR eyes when compared to controls (2.61±0.6 mm/s and 2.92±0.72 mm/s in PDR and controls, respectively, P=0.019) as well as PDR eyes with DME compared to NPDR eyes (2.36±0.51 mm/s and 2.94±1.09 mm/s in PDR with DME and NPDR, respectively, P=0.01).

CONCLUSION

The RFI, a non-invasive imaging tool, provides high-resolution functional imaging of the retinal microvasculature and quantitative measurement of BFVs in visually impaired DR patients. The isolated diminish venular BFVs in PDR eyes compared to healthy eyes and PDR eyes with DME in comparison to NPDR eyes may indicate the possibility of more retinal vein compromise than suspected in advanced DR.

Keywords: macular blood flow, capillary perfusion map, non-invasive vascular imaging, high-resolution imaging, diabetic retinopathy, retinal function imager

INTRODUCTION

Diabetic retinopathy (DR) is the leading cause of blindness in working-age adults[1]–[2]. Changes in retinal hemodynamics are presumed to underlie its development and progression. Even though several imaging modalities have allowed improved monitoring of therapy and understanding prognosis, further knowledge would optimize these goals.

Technologies to attempt blood flow quantification in diabetics include video fluorescein angiography (FA)[3], laser Doppler velocimetry and flowmetry[4]–[10], and color Doppler ultrasound imaging techniques[11]. However, quantitative measurement of macular blood flow velocities (BFVs) is limited by the need for precise eye fixation and alignment for relatively long periods of time. In addition, only arbitrary units are yielded, and small diameter retinal vessels cannot be assessed. While en-face Doppler optical coherence tomography (OCT) has been reported for measuring macular BFVs quantitatively in DR eyes[12]–[13], only first degree vessels emanating from the optic disc could be assessed.

The Retinal Function Imager (RFI, Optical Imaging Inc., Rehovot, Israel) provides fast, non-invasive functional imaging of the macular microvasculature, including measurement of macular BFVs in absolute values at secondary and tertiary vessels. It also allows construction of high-resolution, non-invasive capillary perfusion maps (nCPMs). Its use has been reported for evaluation of a variety of posterior segment diseases[14]–[19]. The RFI uses a standard fundus camera coupled with a customized stroboscopic flash lamp and a fast (60 Hz) high-resolution (1024×1024 pixels) digital camera system that captures the moving hemoglobin-filled erythrocytes utilizing green (“red-free”) light (wavelength centered at 548 nm at a bandwidth of 17 nm) and digital subtraction processing algorithms[20]–[21] to enable BFV measurements through the generation of short movies (8 frames each) by the use of the machine's built-in software. Additionally, nCPMs are generated by the digital enhancement of alignment of those static frames. The present study investigates the BFV imaging and capabilities of the RFI within DR severity subgroups and in comparison to a healthy control group.

SUBJECTS AND METHODS

Ethical Approval

This prospective study was approved by the Internal Review Board of the University of Miami Miller School of Medicine, and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from the patients.

Study and healthy control patients were evaluated at the Bascom Palmer Eye Institute between September 1, 2014 and April 30, 2015 using the RFI and OCT (Spectralis, Heidelberg Engineering Inc., Heidelberg, Germany) or Cirrus (Carl Zeiss Meditec, Dublin, CA, USA). Type 2 diabetic patients with nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) with or without diabetic macular edema (DME) were studied. Nondiabetic controls had normal cardiac rhythm, normal intraocular pressure (IOP), and otherwise normal ophthalmic exam, including best corrected visual acuity (BCVA) 20/20. The RFI scanning method utilized has been described elsewhere and the protocols were the same as for a previous study we have published in a cohort of eyes with retinal vein occlusions (RVOs)[22]–[23]. Vessel segments in the macular area that were straight enough to be evaluated were imaged and BFV measured and, as reported in our RVO study, analysis was predicated on sufficiently reliable measurements as standarized by other investigators[24].

A path-constrained cross-correlation technique was used for BFV calculation giving separate BFV results for arteries and veins, again as a standard established by other investigators[24].

Data Analysis

Data analysis was made using STATA Statistics software version 14.0 (StataCorp, College Station, Texas, USA). To assess the differences in arteriolar and venular BFVs between the DR study groups and controls we performed linear mixed effects models with random intercepts. Data from individual blood vessels were clustered within eyes, and data from each eye were clustered within patients in order to account for intra- and inter-eye correlations. Given the nature of this small pilot study, no power analysis was performed. The level of statistically significance (type-1 error) was defined as P<0.05 (the 5% level).

RESULTS

The current study evaluated 27 eyes of 21 study patients, including 9 eyes diagnosed with NPDR, 9 eyes with PDR, and 9 control eyes. All eyes with NPDR and 5 eyes with PDR had coexisting DME confirmed by OCT examination. Three eyes in the NPDR group were classified as severe; 6 eyes had moderate NPDR. Detailed demographic and clinical information of the study patients with retinopathy are shown in Table 1.

Table 1. Clinical characteristics of study subjects.

Subject Dx Age (y) Eye Gender HTN Other systemic disease Time of DME Dx (y) Past surgical history Previous DME Tx BCVA
1 NPDR 64 OD Male No None 1 NA IVB×7 20/200
2 NPDR 64 OS Male No None 1 NA IVB×6 20/20-2
3 NPDR 56 OD Female Yes None 2 NA IVR×6 20/30-2
4 NPDR 56 OS Female Yes None 2 NA IVR×6 20/40-2
5 NPDR 70 OS Female Yes HLD 10 CE/IOL, PPV/MP Laser, IVTA×1, IVB×3 20/400
6 NPDR 67 OS Male Yes HLD, CAD 4 CE/IOL, PPV/MP IVTA×5, IVB×5, IVR×5, laser 20/80
7 NPDR 68 OD Male Yes None 1 CE/IOL IVTA×1 20/50-2
8 NPDR 68 OS Male Yes None 1 CE/IOL IVI×1, IVTA×1 20/40
9 NPDR 64 OD Male Yes HLD, kidney failure, CAD 1 CE/IOL IVTA×1 20/25-1
10 PDR-no DME 63 OD Male Yes CVA, kidney failure 8 CE/IOL, PPV/MP/EL IVTA×3, IVB×1 20/200
11 PDR-no DME 67 OD Male Yes Kidney failure 10 CE/IOL Laser 20/30
12 PDR-no DME 67 OS Male Yes Kidney failure 10 CE/IOL, PPV/MP/EL Laser, IVTA×2 20/400
13 PDR-no DME 73 OS Female Yes HLD, kidney failure 14 CE/IOL Laser, IVTA×1, IVB×1 6/200
14 PDR-DME 62 OS Male Yes None UK CE/IOL IVI×11, laser 20/50-1
15 PDR-DME 69 OS Male Yes None 1 CE/IOL, PPV/EL/MP IVB×7 20/80
16 PDR-DME 71 OD Female Yes None 13 CE/IOL Laser, IVTA×4, IVB×2 20/100
17 PDR-DME 57 OS Female Yes Liver cirrhosis 4 CE/IOL IVTA×5, IVB×8, 20/70
18 PDR-DME 57 OD Female Yes Liver cirrhosis 4 CE/IOL, PPV/MP/EL IVTA×4, IVB×9 20/80
Open in a new tab

UK: Unknown; Dx: Diagnosis; Tx: Treatment; DM: Diabetes mellitus; DR: Diabetic retinopathy; HTN: Systemic hypertension; HLD: Hyperlipidemia; BCVA: Best-corrected visual acuity; NPDR: Nonproliferative diabetic retinopathy; PDR: Proliferative diabetic retinopathy; DME: Diabetic macular edema; NA: Non-applicable; CAD: Coronary artery disease; CVA: Cerebrovascular accident; CE/IOL: Cataract extraction/intraocular lens implant; PPV: Pars plana vitrectomy; MP: Membrane peeling; EL: Endolaser; IVB: Intravitreal bevacizumab; IVR: Intravitreal ranibizumab; IVTA: Intravitreal triamcinolone; IVI: Intravitreal injection, unknown medicine; PRP: Panretinal photocoagulation.

The mean age of the control group was 53y, ranging from 46 to 62y; none had known systemic or ocular morbidities. Table 2 demonstrate BFVs and vessel segmentation subject's profile and Table 3 tabulates vessels segmentation and BFVs statatistical information between the study groups.

Table 2. Subjects' blood flow velocity profile.

Subject Mean arterioles velocity Total segmented arterioles Mean venules velocity Total segmented venules BCVA at RFI testinga
NPDR
 1 7.95 6 4.23 23 20/400
 2 5.16 9 3.74 16 20/30
 3 5.06 8 4.35 12 20/30 (-2)
 4 4.55 8 2.93 18 20/25 (-1)
 5 4.23 9 2.59 19 20/400
 6 2.79 17 1.57 22 20/60
 7 8.27 3 3.51 14 20/60
 8 2.77 15 1.54 17 20/50
 9 3.05 13 2.01 23 20/40 (-2)
PDR no-DME
 10 3.83 8 2.22 23 20/200
 11 7.43 3 3.67 9 20/80 (-1)
 12 2.75 2 2.93 4 3′/200″
 13 3.61 10 2.91 9 20/300
PDR-DME
 14 2.44 5 2.21 9 20/70
 15 2.60 3 1.67 7 20/200
 16 4.35 6 2.39 8 20/20
 17 10.20 1 3.10 11 20/80 (-1)
 18 3.08 4 2.44 13 20/200
Healthy
 19 4.21 12 3.01 10 20/20
 20 5.08 12 3.89 14 20/20
 21 4.72 10 3.37 9 20/20
 22 3.64 17 2.17 22 20/20
 23 4.33 12 2.97 10 20/20
 24 5.11 11 2.93 10 20/20
 25 2.86 7 2.23 11 20/20
 26 3.41 8 1.88 9 20/20
 27 4.38 4 3.85 11 20/20
Open in a new tab

aSnellen visual acuity. BCVA: Best corrected visual acuity; NPDR: Nonproliferative diabetic retinopathy; PDR: Proliferative diabetic retinopathy; DME: Diabetic macular edema.

Table 3. Blood flow velocity results among groups.

Group Arterioles velocityd Venules velocityd Arterioles number Venules number Total arterioles segmented Total venules segmented Pa
NPDR 4.87 (2.05) 2.94 (1.09) 10 (4) 18 (4) 88 164 0.84b
Overall PDR 4.48 (2.63) 2.61 (0.60) 5 (3) 10 (5) 42 93 0.019b
PDR-no DME 4.40 (2.07) 2.93 (0.59) 6 (4) 11 (8) 23 45 0.49c
PDR-DME 4.53 (3.26) 2.36 (0.51) 4 (2) 10 (2) 19 48 0.01c
Healthy 4.19 (0.76) 2.92 (0.72) 10 (4) 12 (4) 93 106
Open in a new tab

aNo stasitically significant difference was noted between arterioles velocities amongst groups; bVenular velocity compared to healthy; cVenular velocity between PDR-DME and NPDR groups; dmm/s. NPDR: Nonproliferative diabetic retinopathy; PDR: Proliferative diabetic retinopathy; DME: Diabetic macular edema.

mean (SD)

Mean arteriolar BFV in control eyes was 4.19±0.76 mm/s and venular BFV was 2.92±0.72 mm/s. Mean BFVs in the NPDR group were 4.87±2.05 mm/s for arteriolar and 2.94±1.09 mm/s for venular segments.

The mean BFV values for the PDR group included 4.48±2.63 mm/s and 2.61±0.6 mm/s for arteriolar and venules BFVs, respectively. The lower number of segmented vessels identified for study in the PDR group was due to marked capillary drop out. The PDR group consisted of patients with and without DME. When considering only the PDR subjects without DME the velocity values were 4.40±2.07 mm/s and 2.93±0.59 mm/s for arterioles and venules, respectively. For PDR eyes with DME, the arteriolar and venular BFVs were 4.53±3.26 mm/s and 2.36±0.51 mm/s. The arteriolar velocities did not statistically differ between the overall PDR group when compared to the control (P=0.32) or the NPDR (P=0.57) groups, as well as between the NPDR and the healthy controls groups (P=0.69). The independent analysis of the PDR subgroups also failed to yield statistically significant different results for arteriolar or venular BFVs between the small subgroups of PDR with DME vs PDR without DME (P=0.48 and 0.16, respectively), as well as for arteriolar BFV between PDR with DME or PDR without DME subgroups vs the control (P=0.29 and 0.77, respectively) or vs the NPDR (P=0.42 and 0.98, respectively) groups.

We found a decrease in venular BFV in the overall PDR group when compared to the control group (P=0.019). No difference in the venular BFV was seen between the NPDR vs control groups (P=0.84) as well as between overall PDR vs NPDR groups (P=0.069). However, a statistically significant decrease in the venular BFV was noticed in the PDR with DME subgroup when compared to the NPDR group (P=0.01) while no statistical differences were found in the comparison of venular BFV between the PDR without DME subgroup and the NPDR group (P=0.49).

The nCPMs provided high-resolution images of the retinal microvasculature in both the healthy, NPDR and PDR subgroups showing the fine capillary changes of the macula non-invasively (Figure 1).

Figure 1. Examples from the study subjects for the labeled fundus images (A, E, G) with the vessel segments, and the nCPMs (B, D, F, H).

Figure 1

Open in a new tab

A and B are from a healthy subject, C and D are from a subject with moderate NPDR and no macular edema, E and F from a subject with severe NPDR and diabetic macular edema, whereas G and H are from a subject with proliferative retinopathy. C is an example of the contrast map showing the movement of the single cells within the vessels, used for the measurement of blood flow velocity. Note the fine structure of the foveal avascular zone imaged noninvasively and also the zones of capillary dropout in D, F, and H.

DISCUSSION

The current study demonstrated reduced venular BFV in PDR patients when compared to healthy subjects, and also decreased venular BFV in PDR with DME subjects in comparison to NPDR with DME eyes. However, there were no demonstrable differences in arteriolar BFV among these groups. This reduction of BFV in the venules but not the arterioles suggests diabetic vascular compromise may be more important on the venous rather than on the arteriolar vasculature. Whether this finding was due to the small number of cases in this study, or whether it offers insight into the pathogenesis of advanced DR is uncertain.

RFI previously has demonstrated a capability to provide high-resolution, noninvasive, functional, and quantitative retinal vascular imaging in eyes with retinal vein occlusions, but with a panvascular compromise, that is, a similar decrease in both arteriolar and venular velocities[23]. Previous studies using various imaging techniques such as OCT angiography to study blood flow have been limited to establishing patency (blood vessel cross sectional area and dynamics information), presuming that as a surrogate for blood flow[25]–[30]. In contrast, the RFI allows quantitative blood flow velocity measurement, which is a step closer to quantitating blood flow. Previous imaging studies (which did not measure absolute flow velocities) showed mixed results in diabetic patients[3]–[4],[6]–[7],[9],[31]–[35]. The wide range of retinal changes encompassed within even the same category of DR severity degree, the likely heterogeneity in vascular dynamics and dysregulation due to multiple variables (degree of capillary closure, hypoxia levels, glycemic state, blood viscosity), and the generally pilot study nature of these reports might account for the lack of consensus. Moreover, existing technologies evaluating blood flow are constrained to evaluating first- or larger second-degree retinal vessels, which might not mirror the changes at the level of more distal macular microvasculature.

BFV is believed to decrease commensurate with increase in DR severity[6],[9],[36]. A comparative evaluation by Burgansky-Eliash et al[37] including 51 healthy vs 58 moderate and severe NPDR eyes utilizing the RFI found significant decrease in both arteriolar and venular BFVs in the NPDR group, but no significant BFV changes between eyes with (33 eyes) and without (25 eyes) clinically-significant DME. Other previous studies comparing NPDR vs healthy subjects BFV have also showed decreased velocities in NPDR eyes[6],[38]–[39]. Landa et al[40] found a positive correlation between venular BFV by RFI and central retinal thickening by OCT imaging evaluation; given all our NPDR subjects presented DME. Landa et al[40] findings may justify the lack of velocity difference amongst the current study's NPDR and healthy controls groups. Konno et al[41] have also suggested a transition from decrease to increase in arteriolar BFV in diabetic eyes depending upon the duration and severity of retinopathy with increasing BFVs in more advanced cases; they postulated this to be secondary to the occurrence of anastomotic channels (shunts) traversing areas of capillary closure deviating blood directly from the arterioles to the venules. The present study corroborates statistically significantly decreased venular BFV in more severe DR stages (i.e. in the PDR group), but not in NPDR-DME eyes when compared to healthy subjects.

The current study is limited by the sample size because eyes with mild media opacities were excluded since the RFI imaging requires clear media. Additionally, some patients with other coexisting disease had to be eliminated from the study since the RFI's imaging acquisition is limited by a very intense light which is not tolerated by some individuals. Hence the current study cohort may have represented a selected group. Also, we utilized a semi-automated (partially manual) segmentation method to delineate the vasculature of interest and several blood vessels did not meet the ideal coefficient of variance levels. The RFI in new versions of the software, which is currently under development, might allow inclusion of a broader range of patients and vessels per patient once the need for manual processing of the areas of interest can be automated.

The RFI offers a quantitative assessment of blood flow velocity, a step closer to measuring absolute blood flow volume[42]. The authors believe the RFI provide superior macular microvasculature BFV measurements, which can be important in understanding retinal diseases when in conjunction with precise blood vessel dynamics information. The current study in its pilot nature provided enough evidence to suggest that DR, at least initially might be more of a venular rather than arteriolar disease.

Acknowledgments

Conflicts of Interest: Campagnoli TR, None; Somfai GM, None; Tian J, None; DeBuc DC, None; Smiddy WE, None.

REFERENCES

  • 1.Thylefors B, Négrel AD, Pararajasegaram R, Dadzie KY. Global data on blindness. Bull World Health Organ. 1995;73(1):115–121. [PMC free article] [PubMed] [Google Scholar]
  • 2.Engelgau MM, Geiss LS, Saaddine JB, Boyle JP, Benjamin SM, Gregg EW, Tierney EF, Rios-Burrows N, Mokdad AH, Ford ES, Imperatore G, Venkat Narayan KM. The evolving diabetes burden in the United States. Ann Intern Med. 2004;140(11):945. doi: 10.7326/0003-4819-140-11-200406010-00035. [DOI] [PubMed] [Google Scholar]
  • 3.Cunha-Vaz JG, Fonseca JR, de Abreu JR, Lima JJ. Studies on retinal blood flow. II. Diabetic retinopathy. Arch Ophthalmol. 1978;96(5):809–811. doi: 10.1001/archopht.1978.03910050415001. [DOI] [PubMed] [Google Scholar]
  • 4.Patel V, Rassam S, Newsom R, Wiek J, Kohner E. Retinal blood flow in diabetic retinopathy. BMJ. 1992;305(6855):678–683. doi: 10.1136/bmj.305.6855.678. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Nagaoka T, Sato E, Takahashi A, Yokota H, Sogawa K, Yoshida A. Impaired retinal circulation in patients with type 2 diabetes mellitus: retinal laser Doppler velocimetry study. Invest Ophthalmol Vis Sci. 2010;51(12):6729–6734. doi: 10.1167/iovs.10-5364. [DOI] [PubMed] [Google Scholar]
  • 6.Grunwald JE, Riva CE, Sinclair SH, Brucker AJ, Petrig BL. Laser Doppler velocimetry study of retinal circulation in diabetes mellitus. Arch Ophthalmol. 1986;104(7):991–996. doi: 10.1001/archopht.1986.01050190049038. [DOI] [PubMed] [Google Scholar]
  • 7.Cuypers MH, Kasanardjo JS, Polak BC. Retinal blood flow changes in diabetic retinopathy measured with the Heidelberg scanning laser Doppler flowmeter. Graefes Arch Clin Exp Ophthalmol. 2000;238(12):935–941. doi: 10.1007/s004170000207. [DOI] [PubMed] [Google Scholar]
  • 8.Feke GT, Tagawa H, Yoshida A, Goger DG, Weiter JJ, Buzney SM, McMeel JW. Retinal circulatory changes related to retinopathy progression in insulin-dependent diabetes mellitus. Ophthalmology. 1985;92(11):1517–1522. doi: 10.1016/s0161-6420(85)33827-7. [DOI] [PubMed] [Google Scholar]
  • 9.Yoshida A, Feke GT, Morales-Stoppello J, Collas GD, Goger DG, McMeel JW. Retinal blood flow alterations during progression of diabetic retinopathy. Arch Ophthalmol. 1983;101(2):225–227. doi: 10.1001/archopht.1983.01040010227008. [DOI] [PubMed] [Google Scholar]
  • 10.Grunwald JE, Riva CE, Baine J, Brucker AJ. Total retinal volumetric blood flow rate in diabetic patients with poor glycemic control. Invest Ophthalmol Vis Sci. 1992;33(2):356–363. [PubMed] [Google Scholar]
  • 11.Goebel W, Lieb WE, Ho A, Sergott RC, Farhoumand R, Grehn F. Color Doppler imaging: a new technique to assess orbital blood flow in patients with diabetic retinopathy. Invest Ophthalmol Vis Sci. 1995;36(5):864–870. [PubMed] [Google Scholar]
  • 12.Lee B, Novais EA, Waheed NK, Adhi M, de Carlo TE, Cole ED, Moult EM, Choi W, Lane M, Baumal CR, Duker JS, Fujimoto JG. En face Doppler optical coherence tomography measurement of total retinal blood flow in diabetic retinopathy and diabetic macular edema. JAMA Ophthalmol. 2017;135(3):244. doi: 10.1001/jamaophthalmol.2016.5774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Pechauer AD, Hwang TS, Hagag AM, Liu L, Tan O, Zhang XB, Parker M, Huang D, Wilson DJ, Jia YL. Assessing total retinal blood flow in diabetic retinopathy using multiplane en face Doppler optical coherence tomography. Br J Ophthalmol. 2018;102(1):126–130. doi: 10.1136/bjophthalmol-2016-310042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Gameiro GR, Jiang H, Liu Y, Deng YQ, Sun XY, Nascentes B, Baumel B, Rundek T, Wang JH. Retinal tissue hypoperfusion in patients with clinical Alzheimer's disease. Eye Vis (Lond) 2018;5:21. doi: 10.1186/s40662-018-0115-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Henderson AD, Jiang H, Wang JH. Characterization of retinal microvasculature in acute non-arteritic anterior ischemic optic neuropathy using the retinal functional imager: a prospective case series. Eye Vis (Lond) 2019;6:3. doi: 10.1186/s40662-018-0126-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wang L, Kwakyi O, Nguyen J, Ogbuokiri E, Murphy O, Caldito NG, Balcer L, Frohman E, Frohman T, Calabresi PA, Saidha S. Microvascular blood flow velocities measured with a retinal function imager: inter-eye correlations in healthy controls and an exploration in multiple sclerosis. Eye Vis (Lond) 2018;5:29. doi: 10.1186/s40662-018-0123-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bates NM, Tian J, Smiddy WE, Lee WH, Somfai GM, Feuer WJ, Shiffman JC, Kuriyan AE, Gregori NZ, Kostic M, Pineda S, Cabrera DeBuc D. Relationship between the morphology of the foveal avascular zone, retinal structure, and macular circulation in patients with diabetes mellitus. Sci Rep. 2018;8:5355. doi: 10.1038/s41598-018-23604-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Burgansky-Eliash Z, Bartov E, Barak A, Grinvald A, Gaton D. Blood-flow velocity in glaucoma patients measured with the retinal function imager. Curr Eye Res. 2016;41(7):965–970. doi: 10.3109/02713683.2015.1080278. [DOI] [PubMed] [Google Scholar]
  • 19.Birkhoff WAJ, van Manen L, Dijkstra J, de Kam ML, van Meurs JC, Cohen AF. Correction to: Retinal oximetry and fractal analysis of capillary maps in sickle cell disease patients and matched healthy volunteers. Graefes Arch Clin Exp Ophthalmol. 2020;258(1):219–220. doi: 10.1007/s00417-019-04512-x. [DOI] [PubMed] [Google Scholar]
  • 20.Grinvald A, Bonhoeffer T, Vanzetta I, Pollack A, Aloni E, Ofri R, Nelson D. High-resolution functional optical imaging: from the neocortex to the eye. Ophthalmol Clin North Am. 2004;17(1):53–67. doi: 10.1016/j.ohc.2003.12.003. [DOI] [PubMed] [Google Scholar]
  • 21.Izhaky D, Nelson DA, Burgansky-Eliash Z, Grinvald A. Functional imaging using the retinal function imager: direct imaging of blood velocity, achieving fluorescein angiography-like images without any contrast agent, qualitative oximetry, and functional metabolic signals. Jpn J Ophthalmol. 2009;53(4):345–351. doi: 10.1007/s10384-009-0689-0. [DOI] [PubMed] [Google Scholar]
  • 22.Somfai GM, Tian J, DeBuc DC. Assessment of potential vessel segmentation pitfalls in the analysis of blood flow velocity using the Retinal Function Imager. Graefes Arch Clin Exp Ophthalmol. 2016;254(6):1075–1081. doi: 10.1007/s00417-015-3166-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Campagnoli TR, Somfai GM, Tian J, DeBuc DC, Smiddy WE. Noninvasive, high-resolution functional macular imaging in subjects with retinal vein occlusion. Ophthalmic Surg Lasers Imaging Retina. 2017;48(10):799–809. doi: 10.3928/23258160-20170928-04. [DOI] [PubMed] [Google Scholar]
  • 24.Nelson DA, Krupsky S, Pollack A, Aloni E, Belkin M, Vanzetta I, Rosner M, Grinvald A. Special report: noninvasive multi-parameter functional optical imaging of the eye. Ophthalmic Surg Lasers Imaging Retina. 2005;36(1):57–66. [PubMed] [Google Scholar]
  • 25.Lei JQ, Pei C, Wen C, Abdelfattah NS. Repeatability and reproducibility of quantification of superficial peri-papillary capillaries by four different optical coherence tomography angiography devices. Sci Rep. 2018;8(1):17866. doi: 10.1038/s41598-018-36279-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Al-Sheikh M, Tepelus TC, Nazikyan T, Sadda SR. Repeatability of automated vessel density measurements using optical coherence tomography angiography. Br J Ophthalmol. 2017;101(4):449–452. doi: 10.1136/bjophthalmol-2016-308764. [DOI] [PubMed] [Google Scholar]
  • 27.Zhao Q, Yang WL, Wang XN, Wang RK, You QS, Chu ZD, Xin C, Zhang MY, Li DJ, Wang ZY, Chen W, Li YF, Cui R, Shen L, Wei WB. Repeatability and reproducibility of quantitative assessment of the retinal microvasculature using optical coherence tomography angiography based on optical microangiography. Biomed Environ Sci. 2018;31(6):407–412. doi: 10.3967/bes2018.054. [DOI] [PubMed] [Google Scholar]
  • 28.Hu R, Ding C. Quantification of vessel density in retinal optical coherence tomography angiography images using local fractal dimension. Invest Ophthalmol Vis Sci. 2016;57(4):2262. doi: 10.1167/iovs.16-19256. [DOI] [PubMed] [Google Scholar]
  • 29.Shen CY, Yan S, Du M, Zhao H, Shao L, Hu YB. Assessment of capillary dropout in the superficial retinal capillary plexus by optical coherence tomography angiography in the early stage of diabetic retinopathy. BMC Ophthalmol. 2018;18(1):113. doi: 10.1186/s12886-018-0778-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Hirst A, Greenberg MJ. Euclidean and non-euclidean geometries - development and history. Math Gazette. 1995;79(484):236. [Google Scholar]
  • 31.Clermont AC, Aiello LP, Mori F, Aiello LM, Bursell SE. Vascular endothelial growth factor and severity of nonproliferative diabetic retinopathy mediate retinal hemodynamics in vivo: a potential role for vascular endothelial growth factor in the progression of nonproliferative diabetic retinopathy. Am J Ophthalmol. 1997;124(4):433–446. doi: 10.1016/s0002-9394(14)70860-8. [DOI] [PubMed] [Google Scholar]
  • 32.Cunha-Vaz JG, Fonseca JR, Abreu JF. Vitreous fluorophotometry and retinal blood flow studies in proliferative retinopathy. Graefes Arch Clin Exp Ophthalmol. 1978;207(2):71–76. doi: 10.1007/BF00414303. [DOI] [PubMed] [Google Scholar]
  • 33.Tayyari F, Khuu LA, Flanagan JG, Singer S, Brent MH, Hudson C. Retinal blood flow and retinal blood oxygen saturation in mild to moderate diabetic retinopathy. Invest Ophthalmol Vis Sci. 2015;56(11):6796–6800. doi: 10.1167/iovs.15-17481. [DOI] [PubMed] [Google Scholar]
  • 34.Lorenzi M, Feke GT, Cagliero E, Pitler L, Schaumberg DA, Berisha F, Nathan DM, McMeel JW. Retinal haemodynamics in individuals with well-controlled type 1 diabetes. Diabetologia. 2008;51(2):361–364. doi: 10.1007/s00125-007-0872-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Blair NP, Feke GT, Morales-Stoppello J, Riva CE, Goger DG, Collas G, McMeel JW. Prolongation of the retinal mean circulation time in diabetes. Arch Ophthalmol. 1982;100(5):764–768. doi: 10.1001/archopht.1982.01030030768009. [DOI] [PubMed] [Google Scholar]
  • 36.Arend O, Wolf S, Jung F, Bertram B, Postgens H, Toonen H, Reim M. Retinal microcirculation in patients with diabetes mellitus: dynamic and morphological analysis of perifoveal capillary network. Br J Ophthalmol. 1991;75(9):514–518. doi: 10.1136/bjo.75.9.514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Burgansky-Eliash Z, Nelson DA, Bar-Tal OP, Lowenstein A, Grinvald A, Barak A. Reduced retinal blood flow velocity in diabetic retinopathy. Retina. 2010;30(5):765–773. doi: 10.1097/IAE.0b013e3181c596c6. [DOI] [PubMed] [Google Scholar]
  • 38.Arend O, Remky A, Harris A, Bertram B, Reim M, Wolf S. Macular microcirculation in cystoid maculopathy of diabetic patients. Br J Ophthalmol. 1995;79(7):628–632. doi: 10.1136/bjo.79.7.628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Hudson C, Flanagan JG, Turner GS, Chen HC, Rawji MH, McLeod D. Exaggerated relative nasal-temporal asymmetry of macular capillary blood flow in patients with clinically significant diabetic macular oedema. Br J Ophthalmol. 2005;89(2):142–146. doi: 10.1136/bjo.2003.037317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Landa G, Garcia PM, Rosen RB. Correlation between retina blood flow velocity assessed by retinal function imager and retina thickness estimated by scanning laser ophthalmoscopy/optical coherence tomography. Ophthalmologica. 2009;223(3):155–161. doi: 10.1159/000189819. [DOI] [PubMed] [Google Scholar]
  • 41.Konno S, Feke GT, Yoshida A, Fujio N, Goger DG, Buzney SM. Retinal blood flow changes in type I diabetes. A long-term follow-up study. Invest Ophthalmol Vis Sci. 1996;37(6):1140–1148. [PubMed] [Google Scholar]
  • 42.Su D, Garg S. The retinal function imager and clinical applications. Eye Vis (Lond) 2018;5:20. doi: 10.1186/s40662-018-0114-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from International Journal of Ophthalmology are provided here courtesy of Press of International Journal of Ophthalmology

RESOURCES