The Value of OCTA in Retinal Vascular Disease

OCT angiography (OCTA) has dramatically changed the landscape of retinal disease diagnosis because it allows clinicians to noninvasively evaluate early vascular changes at the capillary level in asymptomatic patients. This has significant clinical potential, as ischemic microangiopathies remain a leading cause of vision loss in developed countries.¹

Here, we review the clinical utility of qualitative blood flow assessment in three major retinal diseases—diabetic retinopathy (DR), AMD, and central serous retinopathy (CSR)—and highlight evidence demonstrating the utility of additional blood flow quantification (Table 1).

Click to view larger

AN OVERVIEW OF OCTA

OCTA visualizes blood flow by detecting motion contrast of individual red blood cells. This technology has high axial resolution (ie, 5-10 μm or better) and provides depth-resolved images of the retinal and choroidal vasculature at the capillary level, allowing clinicians to pinpoint the exact layer in which perfusion is present or absent.²

Currently, OCTA devices provide pseudocolor maps of retinal capillaries based on the presence or absence of red blood cell flow within a certain range of velocities (typically 0.3-3 mm/sec); they do not provide information about exact blood velocity in one region versus another. However, experimental OCT-based algorithms now allow quantification of red blood cell velocity at the capillary level.^3-5 Known as velocimetry, these techniques take advantage of faster laser scanning speeds and more sophisticated scan patterns to understand disease onset and progression at a much more granular scale than standard OCTA (Figure). OCTA-based velocimetry has demonstrated that blood velocity varies across different retinal capillary beds, laying the foundation upon which abnormal blood flow—leading to diseases such as DR and others—can be categorized (Table 2).³

DIABETIC RETINOPATHY

DR is rooted in capillary nonperfusion, endothelial dysfunction, and autoregulatory failure.^6,7 Quantitative flow measurements have demonstrated a range of retinal blood flow alterations in patients with diabetes, even in the absence of overt retinopathy.⁸ Doppler-based studies have demonstrated various changes in blood flow in the large retinal vessels among patients with diabetes but without retinopathy.^9-12 Other doppler-based studies have shown a reduction in retinal blood flow in eyes with severe nonproliferative DR (NPDR) and proliferative DR (PDR).^13,14 At the capillary level, OCTA studies show a decrease in vessel perfusion across the superficial and deep capillary plexuses correlating with disease severity and progression.¹⁵ Even patients with minimal or no retinopathy demonstrate impaired capillary perfusion on OCTA.^16,17 

A recent study using OCTA-based second-generation variable interscan time analysis revealed an increase in the red blood cell velocity in the deep capillary plexus of patients with DR, which correlated with a decreased vessel density in patients with NPDR.¹⁸ Longitudinal work also indicates that eyes with early flow deficits, especially in the deep retinal layer, are more likely to progress to vision-threatening stages of DR, including PDR.^19-21

Click to view larger

Figure. These pseudocolor flow velocity maps illustrate the age-related differences in retinal capillary flow within the superficial retinal layer. A 24-year-old healthy subject demonstrates higher capillary flow velocities, represented by higher color saturation (A), whereas a 74-year-old healthy subject shows lower overall flow (B), with differences most evident at the capillary level. Magnified views highlight the flow patterns within individual capillary networks. The color bar indicates mean frequency in Hertz (Hz), used as a surrogate measure of capillary flow velocity. Adapted with permission from Aman et al.³

Clinical Applications In DR

For clinicians, the most compelling potential of blood flow measurement in DR lies in its ability to guide both early diagnosis and personalized care. By identifying subtle flow deficits before retinopathy becomes apparent, clinicians can stratify which patients are at highest risk for progression, allowing more targeted surveillance and earlier intervention. 

Beyond screening, new quantitative flow metrics from velocimetry-based algorithms will provide a useful biomarker of treatment response for novel early interventions. For example, flow changes measured after anti-VEGF therapy or panretinal photocoagulation have been shown to correlate with anatomic and functional improvement.^22,23 

Finally, offering patients a concrete demonstration of early microvascular dysfunction could motivate tighter systemic control of diabetes.

MACULAR DEGENERATION

A central feature of early AMD is loss of choriocapillaris perfusion, which impairs metabolic support to the retinal pigment epithelium (RPE) and photoreceptors.²⁴ In wet AMD, aberrant macular neovascularization (MNV) reflects a maladaptive response to hypoxia and vascular insufficiency.²⁵

Studies using OCTA have shown that choriocapillaris, choroidal, and retinal blood flow alterations are detectable in early and intermediate AMD, often preceding active MNV or atrophy.^26-29 In addition, reduced choriocapillaris perfusion is associated with faster progression to geographic atrophy (GA), suggesting it may serve as a valuable prognostic biomarker.^30-32 OCTA has been used to detect MNV before the presence of exudation in subjects with clinically apparent and intermediate AMD.^33,34 In wet AMD, OCTA provides unique insight into MNV hemodynamics, where flow patterns can distinguish potentially active lesions from quiescent subretinal fibrosis,³⁵ and where lower choroidal perfusion can serve as a risk factor for MNV development in the fellow eye of patients with unilateral MNV.³⁶ Anti-VEGF therapy also alters MNV perfusion, and flow changes measured on OCTA correlate with treatment efficacy and recurrence risk.^37-39

Clinical Applications in AMD

Retinal blood flow measurement enables early detection of AMD by revealing subtle choriocapillaris flow deficits, which has the potential to flag high-risk eyes for closer monitoring of MNV or RPE loss. 

In addition, flow mapping provides a functional biomarker of treatment response. By capturing MNV perfusion changes alongside structural OCT metrics, clinicians can make more refined decisions about retreatment and long-term management. Clinicians can also monitor patients with nonexudative MNV more closely for progression to active exudation.

Information about blood flow abnormalities holds value in prognostic counseling, as they correlate with rates of atrophy progression and visual decline. For example, reduced choriocapillaris flow deficits on OCTA predict faster GA enlargement,⁴⁰ and baseline OCTA parameters such as fractal dimension and blood flow surface area are associated with treatment burden in wet AMD.⁴¹ 

Click to view larger

CENTRAL SEROUS RETINOPATHY

CSR is thought to occur due to dysregulation of the choroidal circulation. The hallmark is a state of choroidal hyperperfusion and hyperpermeability, which leads to leakage through the RPE and subsequent accumulation of subretinal fluid.^42-44 

Laser speckle flowgraphy studies have consistently shown elevated choroidal blood flow in acute CSR compared with unaffected eyes, with levels that normalize following spontaneous resolution or treatment.^45-47 OCT and OCTA have further demonstrated evidence of choroidal vascular dysregulation, with dilated choroidal vessels and focal zones of hyperperfusion in both acute and chronic cases.^43,48-51 Adaptive optics (AO) imaging has helped clarify regional alterations in choroidal perfusion, with evidence of significant RPE changes.⁵² OCT has also demonstrated evidence of an asymmetric vortex venous system in CSR eyes, with relative hyperperfusion in all vortex veins.⁵³ Importantly, persistent hyperperfusion is associated with disease chronicity and a higher risk of recurrence.⁴⁵

Lastly, OCTA is probably the most useful tool to assess the presence of MNV in chronic CSR lesions, while fluorescein angiography and ICG angiography often show diffuse areas of non-specific staining.

Clinical Applications in CSR

Blood flow assessment in CSR may aid in distinguishing acute from chronic disease, and quantitative flow measures could guide treatment selection. For example, normalization of choroidal hyperperfusion following photodynamic therapy has been observed, reinforcing its role in restoring hemodynamic balance.^54-56 This dynamic biomarker could allow physicians to monitor therapeutic response more directly than structural OCT alone. Flow abnormalities could serve as prognostic indicators, identifying patients more likely to experience recurrent or persistent subretinal fluid, and thereby guiding closer follow-up. In the future, integrating choroidal flow assessment into CSR care may provide a more tailored, patient-specific approach to what has historically been a challenging condition to manage. 

BARRIERS TO CLINICAL INTEGRATION

Despite significant progress in imaging technologies, the clinical adoption of quantitative retinal blood flow measurement remains limited. A major barrier is inter-device variability, with various devices differing in acquisition principles, algorithms, output parameters, and measurement sensitivity. This makes it difficult to compare results across platforms or establish standardized clinical cutoffs.

Other challenging caveats include the lack of robust normative databases and the need for standardized metrics. For example, studies use a variety of parameters, such as vessel density, perfusion density, capillary flux, red blood cell velocity, and mean blur rate, all of which are often defined differently between research groups. Such heterogeneity has led to conflicting results in the literature.

THE BENEFITS OF OCTA IN THE CLINIC

OCTA (and its velocity-based extensions) is reaching a level of maturity that allows reliable, reproducible data collection in clinical settings. Still, challenges remain. Inter-device variability, a lack of normative databases, and workflow integration are significant barriers to adoption. 

Despite these limitations, clinical trials incorporating flow endpoints, AI-driven image analysis, and growing interest in multimodal integration are all accelerating the pathway to clinical translation.

1. Bourne RRA, Jonas JB, Bron AM, et al. Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe in 2015: magnitude, temporal trends and projections. Br J Ophthalmol. 2018;102(5):575-585. 

2. Kashani AH, Chen CL, Gahm JK, et al. Optical coherence tomography angiography: A comprehensive review of current methods and clinical applications. Prog Retin Eye Res. 2017;60:66-100. 

3. Aman S, Xia Y, Faghani ML, et al. Regional and layer specific retinal capillary red blood cell velocimetry in healthy human subjects. Transl Vis Sci Technol. 2025;14(7):21. 

4. Ploner SB, Moult EM, Choi W, et al. Toward quantitative optical coherence tomography angiography: visualizing blood flow speeds in ocular pathology using variable interscan time analysis. Retina. 2016;36(Supplement 1):S118-S126. 

5. Wang RK, Zhang Q, Li Y, Song S. Optical coherence tomography angiography-based capillary velocimetry. J Biomed Opt. 2017;22(6):66008. 

6. Burns SA, Elsner AE, Chui TY, et al. In vivo adaptive optics microvascular imaging in diabetic patients without clinically severe diabetic retinopathy. Biomed Opt Express. 2014;5(3):961-974. 

7. Altmann C, Schmidt M. The role of microglia in diabetic retinopathy: inflammation, microvasculature defects and neurodegeneration. Int J Mol Sci. 2018;19(1):110. 

8. Burgansky-Eliash Z, Nelson DA, Bar-Tal OP, Lowenstein A, Grinvald A, Barak A. Reduced retinal blood flow velocity in diabetic retinopathy. Retina Phila Pa. 2010;30(5):765-773. 

9. 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. 

10. Grunwald JE, DuPont J, Riva CE. Retinal haemodynamics in patients with early diabetes mellitus. Br J Ophthalmol. 1996;80(4):327-331. 

11. Ludovico J, Bernardes R, Pires I, Figueira J, Lobo C, Cunha-Vaz J. Alterations of retinal capillary blood flow in preclinical retinopathy in subjects with type 2 diabetes. Graefes Arch Clin Exp Ophthalmol. 2003;241(3):181-186. 

12. Meng N, Liu J, Zhang Y, Ma J, Li H, Qu Y. Color doppler imaging analysis of retrobulbar blood flow velocities in diabetic patients without or with retinopathy. J Ultrasound Med. 2014;33(8):1381-1389. 

13. Srinivas S, Tan O, Nittala MG, et al. Assessment of retinal blood flow in diabetic retinopathy using doppler fourier-domain optical coherence tomography. Retina. 2017;37(11):2001-2007. 

14. Lee B, Novais EA, Waheed NK, et al. En face doppler optical coherence tomography measurement of total retinal blood flow in diabetic retinopathy and diabetic macular edema. JAMA Ophthalmol. 2017;135(3):244-251.

15. Kushner-Lenhoff S, Kogachi K, Mert M, et al. Capillary density and caliber as assessed by optical coherence tomography angiography may be significant predictors of diabetic retinopathy severity. Plos One. 2022;17(1):e0262996. 

16. de Carlo TE, Chin AT, Bonini Filho MA, et al. Detection of microvascular changes in eyes of patients with diabetes but not clinical diabetic retinopathy using optical coherence tomography angiography. Retina. 2015;35(11):2364-2370. 

17. Kim AY, Chu Z, Shahidzadeh A, Wang RK, Puliafito CA, Kashani AH. Quantifying microvascular density and morphology in diabetic retinopathy using spectral-domain optical coherence tomography angiography. Invest Ophthalmol Vis Sci. 2016;57(9):OCT362-370.

18. Hwang Y, Takahashi H, Won J, et al. quantification of capillary blood flow speeds in diabetic retinopathy using variable interscan time analysis optical coherence tomography angiography. Retina. 2025;45(1):35. 

19. Custo Greig E, Brigell M, Cao F, et al. Macular and peripapillary optical coherence tomography angiography metrics predict progression in diabetic retinopathy: a sub-analysis of TIME-2b study data. Am J Ophthalmol. 2020;219:66-76. 

20. Sun Z, Tang F, Wong R, et al. OCT angiography metrics predict progression of diabetic retinopathy and development of diabetic macular edema: a prospective study. Ophthalmology. 2019;126(12):1675-1684. 

21. Tsai ASH, Jordan-Yu JM, Gan ATL, et al. Diabetic macular ischemia: influence of optical coherence tomography angiography parameters on changes in functional outcomes over one year. Invest Ophthalmol Vis Sci. 2021;62(1):9. 

22. Abu El-Asrar AM, Alsarhani WK, AlBloushi AF, Alzubaidi A, Gikandi P, Stefánsson E. Effect of panretinal photocoagulation on retinal oxygen metabolism and ocular blood flow in diabetic retinopathy. Acta Ophthalmol (Copenh). 2025;103(4):380-387. 

23. Sorour OA, Mehta N, Baumal CR, et al. Morphological changes in intraretinal microvascular abnormalities after anti-VEGF therapy visualized on optical coherence tomography angiography. Eye (London). 2020;7:29. 

24. Fleckenstein M, Keenan TDL, Guymer RH, et al. Age-related macular degeneration. Nat Rev Dis Primer. 2021;7(1):1-25. 

25. Bhutto I, Lutty G. Understanding age-related macular degeneration (AMD): Relationships between the photoreceptor/retinal pigment epithelium/Bruch’s membrane/choriocapillaris complex. Mol Aspects Med. 2012;33(4):295.

26. Linton EF, Ahmad NUS, Filister R, Wang JK, Sohn EH, Kardon RH. Laser speckle flowgraphy reveals widespread reductions in ocular blood flow in nonexudative age-related macular degeneration. Am J Ophthalmol. 2025;273:92-106. 

27. Harris A, Chung HS, Ciulla TA, Kagemann L. Progress in measurement of ocular blood flow and relevance to our understanding of glaucoma and age-related macular degeneration. Prog Retin Eye Res. 1999;18(5):669-687. 

28. Moult EM, Alibhai AY, Rebhun C, et al. Spatial distribution of choriocapillaris impairment in eyes with choroidal neovascularization secondary to age-related macular degeneration: a quantitative OCT angiography study. Retina. 2020;40(3):428-445. 

29. Moult EM, Waheed NK, Novais EA, et al. Swept source OCT angiography reveals choriocapillaris alterations in eyes with nascent geographic atrophy and drusen-associated atrophy. Retina. 2016;36(Suppl 1):S2-S11. 

30. Zhang Q, Shi Y, Shen M, et al. Does the outer retinal thickness around geographic atrophy represent another clinical biomarker for predicting growth? Am J Ophthalmol. 2022;244:79-87. 

31. Shen M, Li J, Shi Y, et al. Decreased central macular choriocapillaris perfusion correlates with increased low luminance visual acuity deficits. Am J Ophthalmol. 2023;253:1-11. 

32. Berni A, Foti C, Ulla L, et al. choriocapillaris in age-related macular degeneration: a systematic review of optical coherence tomography angiography-based assessments and challenges in standardization. Am J Ophthalmol. 2025;277:482-496. 

33. Palejwala NV, Jia Y, Gao SS, et al. Detection of nonexudative choroidal neovascularization in age-related macular degeneration with optical coherence tomography angiography. Retina. 2015;35(11):2204-2211. 

34. Laiginhas R, Yang J, Rosenfeld PJ, Falcão M. nonexudative macular neovascularization - a systematic review of prevalence, natural history, and recent insights from OCT angiography. Ophthalmol Retina. 2020;4(7):651-661. 

35. Moult E, Choi W, Waheed NK, et al. Ultrahigh-speed swept-source OCT angiography in exudative AMD. Ophthalmic Surg Lasers Imaging Retina. 2014;45(6):496-505. 

36. Boltz A, Luksch A, Wimpissinger B, et al. Choroidal blood flow and progression of age-related macular degeneration in the fellow eye in patients with unilateral choroidal neovascularization. Invest Ophthalmol Vis Sci. 2010;51(8):4220-4225.

37. Böhni SC, Howell JP, Bittner M, et al. Blood flow velocity measured using the Retinal Function Imager predicts successful ranibizumab treatment in neovascular age-related macular degeneration: early prospective cohort study. Eye. 2015;29(5):630-636. 

38. Micieli JA, Tsui E, Lam WC, Brent MH, Devenyi RG, Hudson C. Retinal blood flow in response to an intravitreal injection of ranibizumab for neovascular age-related macular degeneration. Acta Ophthalmol. 2012;90(1):e13-20. 

39. Told R, Reiter GS, Mittermüller TJ, et al. Profiling neovascular age‐related macular degeneration choroidal neovascularization lesion response to anti‐vascular endothelial growth factor therapy using SSOCTA. Acta Ophthalmol. 2021;99(2):e240-e246. 

40. Borrelli E, Souied EH, Freund KB, et al. Reduced choriocapillaris flow in eyes with type 3 neovascularization and age-related macular degeneration. Retina. 2018;38(10):1968-1976. 

41. Cabral D, Coscas F, Pereira T, et al. Quantitative optical coherence tomography angiography biomarkers in a treat-and-extend dosing regimen in neovascular age-related macular degeneration. Transl Vis Sci Technol. 2020;9(3):18. 

42. Kogo T, Muraoka Y, Ishikura M, et al. Pigment epithelial detachment and leak point locations in central serous chorioretinopathy. Am J Ophthalmol. 2024;261:19-27. 

43. Sahoo NK, Maltsev DS, Goud A, Kulikov AN, Chhablani J. Choroidal Changes at the Leakage Site in Acute Central Serous Chorioretinopathy. Semin Ophthalmol. 2019;34(5):380-385. 

44. Spaide RF, Gemmy Cheung CM, Matsumoto H, et al. Venous overload choroidopathy: A hypothetical framework for central serous chorioretinopathy and allied disorders. Prog Retin Eye Res. 2022;86:100973. 

45. Abu El-Asrar AM, AlBloushi AF, Abouammoh MA, et al. Comparisons of choroidal blood flow velocity between initial-onset acute uveitis associated with Vogt–Koyanagi–Harada disease and acute central serous chorioretinopathy. Eye. 2024;38(7):1269-1275. 

46. Saito M, Noda K, Saito W, Ishida S. Relationship between choroidal blood flow velocity and choroidal thickness in patients with regression of acute central serous chorioretinopathy. Graefes Arch Clin Exp Ophthalmol. 2018;256(1):227-229. 

47. Saito M, Saito W, Hashimoto Y, et al. Macular choroidal blood flow velocity decreases with regression of acute central serous chorioretinopathy. Br J Ophthalmol. 2013;97(6):775-780. 

48. Cardillo Piccolino F, Lupidi M, Cagini C, et al. Choroidal vascular reactivity in central serous chorioretinopathy. Invest Ophthalmol Vis Sci. 2018;59(10):3897-3905. 

49. Chan SY, Wang Q, Wei WB, Jonas JB. Optical coherence tomographic angiography in central serous chorioretinopathy. Retina. 2016;36(11):2051. 

50. Scarinci F, Patacchioli FR, Costanzo E, Parravano M. Relationship of choroidal vasculature and choriocapillaris flow with alterations of salivary α-amylase patterns in central serous chorioretinopathy. Invest Ophthalmol Vis Sci. 2021;62(15):19. 

51. Yun C, Huh J, Ahn SM, et al. Choriocapillaris flow features and choroidal vasculature in the fellow eyes of patients with acute central serous chorioretinopathy. Graefes Arch Clin Exp Ophthalmol. 2019;257(1):57-70. 

52. Govindahari V, Dornier R, Ferdowsi S, et al. High-resolution adaptive optics-trans-scleral flood illumination (AO-TFI) imaging of retinal pigment epithelium (RPE) in central serous chorioretinopathy (CSCR). Sci Rep. 2024;14(1):13689. 

53. Luo Z, Xu Y, Xu K, et al. Choroidal vortex vein drainage system in central serous chorioretinopathy using ultra-widefield optical coherence tomography angiography. Transl Vis Sci Technol. 2023;12(9):17. 

54. Gallice M, Daruich A, Matet A, et al. Effect of eplerenone on choroidal blood flow changes during isometric exercise in patients with chronic central serous chorioretinopathy. Acta Ophthalmol (Copenh). 2021;99(8):e1375-e1381. 

55. Mohabati D, Rijssen TJ van, Dijk EH van, et al. Clinical characteristics and long-term visual outcome of severe phenotypes of chronic central serous chorioretinopathy. Clin Ophthalmol. 2018;12:1061-1070.

56. Wakatsuki Y, Tanaka K, Mori R, Furuya K, Kawamura A, Nakashizuka H. Morphological changes and prognostic factors before and after photodynamic therapy for central serous chorioretinopathy. Pharmaceuticals. 2021;14(1):53.