|Year : 2016 | Volume
| Issue : 4 | Page : 145-152
The use of adaptive optics for retinal imaging with microscopic resolution
Mortada A Abozaid
Department of Ophthalmology, Faculty of Medicine, Sohag University, Sohag, Egypt
|Date of Submission||07-Dec-2016|
|Date of Acceptance||27-Feb-2017|
|Date of Web Publication||19-Apr-2017|
Mortada A Abozaid
Department of Ophthalmology, Faculty of Medicine, Sohag University, Sohag
Source of Support: None, Conflict of Interest: None
Adaptive optics (AO) is a technology used to improve the performance of optical systems by reducing the effects of optical aberrations. Adding AO to retinal imaging tools allows noninvasive direct visualization of the photoreceptor cells, capillaries, and nerve fiber bundles by correcting the eye’s monochromatic aberrations. AO can provide new information on the early pathological changes of the retinal microstructures in various retinal diseases, can also monitor response to novel treatments at the cellular level, and can help better select candidates for such treatments. This review discusses the basics, clinical applications, and challenges of AO retinal imaging.
Keywords: adaptive optics, high resolution, noninvasive, retinal imaging
|How to cite this article:|
Abozaid MA. The use of adaptive optics for retinal imaging with microscopic resolution. J Egypt Ophthalmol Soc 2016;109:145-52
| Introduction|| |
The benefits of adaptive optics (AO) were known long before the technology was available for retinal imaging . The original problem was of image degradation arising in ground-based telescopes owing to turbulence in the earth’s atmosphere. The US army appreciated the benefits of this and funded programs during the 1970s and 1980s to develop the technology for imaging foreign satellites with ground-based telescopes . Much of the military’s information was declassified in 1992 after dissolution of the Soviet Union, a move that accelerated progress for all nonmilitary applications of AO. In 1996, AO were successfully applied to high-resolution imaging in the human eye .
AO imaging can provide clinicians with high-resolution views of the retina which allow better understanding of the early pathological changes of the retinal microstructures in various retinal diseases in addition to tracking of disease progression and following treatment response with greater sensitivity and over a much shorter time than other outcome measures such as visual acuity and visual field sensitivity.
Adaptive optics components
AO is a technology used to improve the performance of optical systems by reducing the effects of optical aberrations. By correcting the eye’s monochromatic aberrations, AO provides nearly diffraction-limited high-resolution imaging of the retina. AO by itself does not provide a retinal image, rather an AO subsystem must be incorporated into an existing imaging device, for example, flood illumination fundus camera, confocal scanning laser ophthalmoscope, or spectral-domain optical coherence tomography (OCT), with each modality having its own advantages and limitations.
AO retinal imaging system has three main components: a wavefront sensor, a corrective (adaptive) element, and a control system ([Figure 1]). The wavefront sensor (mostly a Hartman–Shack design) is used to measure the aberrations of the eye. The corrective element is used to compensate for these aberrations, most commonly by using a deformable mirror, which relies on a series of piezoelectric actuators to deflect the mirror surface. The third main component, a software system, controls the interaction between the wavefront sensor and the corrective element .
|Figure 1 Schematic diagram of an adaptive optics retinal imaging system .|
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Retinal structures visible with adaptive optics
Cone photoreceptors serve as relatively easy targets for AO imaging because of the unique waveguiding properties of their outer segments. In fact, in eyes of good optical quality, it is possible to resolve cone photoreceptors without the use of AO ,. The first images of the cone mosaic were published in 1996, using a conventional fundus camera equipped with AO ,. One of the first observations was then made in patients with color vision deficit, where AO found a reduction of one type of cones rather than total absence of particular cone type as previously thought . Another significant observation was that both cones and rods vary in their intensity over time ,. AO imaging efforts usually focus on analyzing the cone density and spacing ,,,,,,,,. In addition, assessment of mosaic geometry and regularity may detect more subtle changes of the photoreceptors .
Recently, Scoles et al.  developed a new ‘split-detector’ adaptive optics scanning laser ophthalmoscope (AOSLO) method to directly visualize the cone inner segment structures in a manner independent of the waveguide properties of the photoreceptor (which conventional confocal AOSLO imaging relies on) ([Figure 2]).
|Figure 2 Confocal (a) and split-detector (b) adaptive optics scanning laser ophthalmoscope images of the photorecetotors of the left eye of a normal patient (JC_10329), taken at ∼7° eccentricity (scale bar 20 µm, crop 150×150 µm).|
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The difficulties in rod imaging are attributed to their small diameter (∼2 µm)  and poor waveguiding properties compared with cones ,,. Dubra et al.  were the first to image the smallest photoreceptors, the cones at the foveal center and the rods, using AOSLO. Rods are of great importance as they are the most abundant of photoreceptors and are often the first cells to get disrupted in many types of retinal disease.
Apart from the photoreceptors, the retinal pigment epithelium (RPE) cells can be seen using reflectance-based AO imaging . Also, with AO, we can observe individual leukocytes moving through small retinal blood vessels, which allows imaging of the parafoveal capillary network and measurement of leukocyte velocity without contrast dyes . In addition, AO can be used to identify individual nerve fiber bundles and provide high-resolution images of both the retinal nerve fiber layer (RNFL) and the optic nerve head ,,.
Clinical retinal imaging with adaptive optics
Retinal dystrophies represent a heterogeneous group of retinal degenerations which all produce progressive death of the photoreceptors. The first published application of AO to image human retinal pathology was in 2000, where images, with microscopic resolution comparable with that of histology, from a patient with cone-rod dystrophy were presented and revealed a reduction in cone density .
Patients with RP and cone-rod dystrophy show different patterns of cone loss in AO images; primary cone degenerations cause increased cone spacing centrally, whereas cone-rod degeneration causes cone cell death adjacent to scotomas beginning around 10° eccentric to fixation, the retinal region with the highest density of rods  ([Figure 3]).
|Figure 3 Confocal (a) and split-detector (b) adaptive optics scanning laser ophthalmoscope images of the right eye of a patient with retinitis pigmentosa (KS_10070), taken at ∼7° eccentricity (scale bar 20 µm, crop 150×150 µm) .|
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Because the rate of disease progression is typically slow in inherited retinal degenerations, it is estimated that patients with RP, for example, must be monitored for 7–10 years before standard clinical measures of visual function, such as visual acuity and visual field sensitivity, to show significant evidence of disease progression ,.
Talcott et al.  monitored the cone mosaic using AOSLO in three patients with retinitis pigmentosa undergoing an experimental treatment with sustained-release of ciliary neurotrophic factor over 24 months and revealed a relative preservation of cone structure despite an absence of significant functional improvement as shown by significant differences in cone spacing between the treated eye and the fellow sham treated eye. This study suggests that AO-based imaging tools could provide sensitive anatomical outcome measures for clinical trials, providing more immediate feedback as to whether the therapeutic intervention has positively affected retinal structure or not.
In addition, assessment of the relative integrity of the photoreceptor mosaic may be useful for identifying individuals who may be good candidates for experimental therapies, such as gene therapy, and may permit specific areas of retained photoreceptor structure to be targeted for treatment .
AO imaging in patients and carriers with choroideremia demonstrated abnormalities of cone morphology and spacing, suggesting simultaneous RPE and photoreceptor cell degeneration. Such findings provide insight into the effect of choroideremia mutations on macular retinal structure, with implications for the development of future treatments .
AOSLO images of patients with X-linked retinoschisis revealed increased cone spacing and abnormal packing in the macula with near-normal cone coverage and function outside the central foveal schisis cavities. The limitation of degeneration of cone photoreceptors to regions near the anatomic fovea affected by large schisis cavities makes therapies for X-linked retinoschisis to be more likely to succeed than therapies for retinal degenerations that cause photoreceptor degeneration diffusely and directly .
In both dog and mouse models of achromatopsia, it was shown that cone function could be restored using a gene therapy approach ,,,. Translation of this specific approach to human trials requires that some cone cells remain intact. AO imaging of patients with achromatopsia revealed a substantial number of foveal and parafoveal cone photoreceptors with apparently intact inner segments .
A major obstacle that faces the use of AO in clinical trials is the need for assessing the reliability and repeatability of cone density measurements in these patients before subjecting them to novel treatments  ([Figure 4]).
|Figure 4 Confocal (a, b) and split-detector (c, d) adaptive optics scanning laser ophthalmoscope images of two patients with achromatopsia; JC_10069 (a, c) and MM_0005 (b, d) .|
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Albinism is an inherited disorder of melanin biosynthesis and is associated with a disruption in normal retinal development, with foveal hypoplasia (absence of a foveal pit) being the predominant ocular phenotype.
Marmor et al.  used AO to image the parafoveal cones in four patients with unspecified foveal hypoplasia. They observed ‘normal’ cone specialization (cone packing and outer nuclear layer thickening); however, no quantitative analysis was provided.
McAllister et al.  examined six individuals with albinism and found variation in the degree of foveal hypoplasia and corresponding variation in foveal cone specialization (measuring cone packing gradients and foveal outer segment lengthening).
Wilk et al.  showed a wide range of foveal morphology and foveal cone specialization in patients with albinism and concluded that although a foveal pit is not required for foveal cone specialization, it may facilitate further cone packing.
Glaucoma is the leading cause of irreversible, preventable blindness worldwide. Primary open-angle glaucoma is a chronic optic neuropathy characterized by progressive loss of retinal ganglion cells, usually associated with ocular hypertension . AO imaging tools can visualize both the RNFL and lamina cribrosa with high resolution ,,,.
Akagi et al.  noted that the laminar pore area was affected by axial length and intraocular pressure. Nadler et al.  showed regional differences in microarchitecture in healthy lamina cribrosa as obtained in-vivo using three-dimensional AO-OCT scans. Such structural variation should prove useful in further evaluation of the role of lamina cribrosa microarchitecture in glaucoma .
Chen et al.  showed that the details near the borders between OCT measured RNFL thickness within and outside normal limits can be visualized with AOSLO. Details in these regions are difficult or impossible to see using OCT scans or to measure using perimetry. They concluded that AOSLO may have a role in clinical trials needing a measure of progression . Moreover, Hood et al.  showed that relatively similar 10–2 defects with similar OCT RNFL thickness profiles can have very different degrees of RNF bundle damage as seen on OCT and AOSLO.
AO may help recognize early glaucomatous damages and identify patients who may progress rapidly and also may benefit from more intensive observation and management.
In addition, AO imaging can have an important role in the evaluation of benefits of the neuroprotective agents.
Diabetic retinopathy (DR) is a microangiopathy resulting from blood rheological abnormalities because of chronic hyperglycemia ,,.
Rather than purely a vascular disease, it is now considered a neurovascular disorder. The neurodegeneration in DR consists of apoptosis affecting the photoreceptors, bipolar cells, and ganglion cells ,. AO imaging can provide noninvasive images of the retinal microvascular damages in patients with DM without the need of contrast agents, by detecting microaneurysms, increased foveal avascular zone (FAZ) size, and dropout of capillaries at the edge of the FAZ ,,. Tam et al.  evaluated the parafoveal capillary network in 15 patients with type 2 diabetes and no retinopathy. They showed a higher tortuosity of the arteriovenous channels in eyes of patients with diabetes and no DR than in healthy controls . In a follow-up study, assessment of the capillaries near the FAZ showed microaneurysm formation and disappearance as well as the de novo formation of tiny capillary bends similar in appearance to intraretinal microvascular abnormalities . Sun et al.  reported disruption of the cone photoreceptor mosaic in patients with type 1 diabetes using AOSLO.
Han et al.  used AO to visualize photoreceptors after macular laser photocoagulation with pattern laser and noted no evidence of reduced photoreceptor density around the laser lesions, no apparent size reduction of the lesions relative to the initial application diameters, and thus, no direct evidence of photoreceptor migration or healing was found.
Burns et al.  used large aperture AOSLO imaging to visualize directly numerous capillary abnormalities in patients with mild and moderate nonproliferative diabetic retinopathy, and suggested that these changes could contribute to the variability in response to treatment in diabetic patients and could be useful for improving clinical classification of diabetic patients, better understanding of the mechanisms of DR, and development of more effective therapies through better patient monitoring of pharmacological intervention.
Age-related macular degeneration
Age-related macular degeneration (AMD) is a multifactorial disease involving ocular, systemic, and genetic risk factors that can cause severe vision loss owing to either tissue loss in the macula or development of subfoveal choroidal neovascular membranes. AMD is the leading cause of blindness in the elderly across the developed world .
Although the changes in late stages of AMD are known, it is important to detect very early stages of the disease to predict its course ,. AO imaging can monitor drusen over time and assess their effect on the overlying photoreceptor mosaic. Preservation of cones over the drusen was observed in a patient with early-onset large colloid drusen , as well as in a patient with basal laminar drusen . Boretsky et al.  identified several small drusen deposits that were not observed with wide-field fundus imaging or spectral-domain OCT in early AMD. They also investigated large coalescent drusen and areas of geographic atrophy in advanced stages of dry AMD and showed a significant decrease in photoreceptor density. Nakashima et al.  showed disruptions of the photoreceptor mosaic outside the clinically visible geographic atrophy lesions and tracked the progression of the geographic atrophy (GA) lesions over time.
Soudry et al.  measured cone spacing at the margin of GA and over drusen in eyes with non-neovascular AMD and found that cone spacing was normal at baseline and remained normal over time. However, these regions showed focal areas of abnormal morphologic features and decreased cone reflectivity suggesting that changes in cone spacing may not represent a primary structural change in AMD progression .
Challenges to adaptive optics imaging
Although established as a powerful research tool, AO still lacks a widespread clinical use. This is owing to lack of clinical access and availability because of the high cost, large size of the imaging machine, and the time and expertise required for acquisition registration, montaging, and quantitative analysis of the images obtained by AO. However, numerous compact designs and commercial clinical prototypes have been developed ,,,,, and some companies having developed AO machines (Boston Micromachines Corporation).
Patient factors, such as unstable fixation, small pupil size, and media opacities, can add to the difficulties with image stabilization and light scatter, resulting in image blur. Another challenge facing AO technology is the need for characterizing the properties of normal photoreceptor mosaic, with establishment of normative databases for the cone density and spacing and mosaic geometry. Such databases are critical to detect deviation from normality. Similar databases are also needed for quantifying blood flow, determining capillary density, measuring RNFL bundles, measuring lamina cribrosa pore size, and assessing intrinsic RPE autofluorescence. Many studies to assess the reliability and repeatability of methods used to quantify these variables are also needed.
In addition, there is a lack of systematic studies that examine the same diseases with different AO imaging modalities (flood, SLO, and OCT) to detect the relative information provided by each modality and hence the advantages and disadvantages of each, which would certainly vary on a disease-by-disease basis.
| Conclusion|| |
Although there are many challenges to the clinical application of AO retinal imaging, the current opportunities are many. The early diagnosis of retinal diseases and the monitoring of treatment efficacy at a cellular level provides promising clinical applications of AO technology. The use of AO imaging in clinical trials could potentially enable targeting of treatments to specific patients or specific retinal areas and allow for more sensitive evaluation of treatment effects.
The author thanks Joseph Carroll, PhD, Lynn Sun, MD, PhD, and Christopher S. Langlo, MD, Eye Institute, Medical College of Wisconsin, Milwaukee, USA, for providing the figures.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Babcock HW. The possibility of compensating astronomical seeing. Pub Astr Soc Pac 1953; 65:229–236.
Benedict R, Breckinridge JB, Fried DL. Atmospheric-compensation technology: introduction. J Opt Soc Am 1994; 11:257–262.
Liang J, Williams DR, Miller D. Supernormal vision and high-resolution retinal imaging through adaptive optics. J Opt Soc Am A Opt Image Sci Vis 1997; 14:2884–2892.
Carroll J, Kay DB, Scoles D, Dubra A, Lombardo M. Adaptive optics retinal imaging – clinical opportunities and challenges. Curr Eye Res 2013; 38:709–721.
Godara P, Dubis AM, Roorda A, Duncan JL, Carroll J. Adaptive optics retinal imaging: emerging clinical applications. Optom Vis Sci 2010; 87:930–941.
Miller DT, Williams DR, Morris GM, Liang J. Images of cone photoreceptors in the living human eye. Vision Res 1996; 36:1067–1079.
Pircher M, Kroisamer JS, Felberer F, Sattmann H, Gotzinger E, Hitzenberger CK. Temporal changes of human cone photoreceptors observed in vivo with SLO/OCT. Biomed Opt Express 2010; 2:100–112.
Carroll J, Neitz M, Hofer H, Neitz J, Williams DR. Functional photoreceptor loss revealed with adaptive optics: an alternate cause of color blindness. Proc Natl Acad Sci USA 2004; 101:8461–8466.
Jonnal RS, Besecker JR, Derby JC, Kocaoglu OP, Cense B, Gao W et al.
Imaging outer segment renewal in living human cone photorecptors. Opt Express 2010; 18:5257–5270.
Cooper RF, Dubis AM, Pavaskar A, Rha J, Dubre A, Carrol J. Spatial and temporal variation of rod photoreceptor reflectance in the human retina. Biomed Opt Express 2011; 2:2577–2589.
Chui TY, Song H, Burns S. Individual variations in human cone photoreceptor packing density: variations with refractive error. Invest Ophthalmol Vis Sci 2008; 49:4679–4687.
Li KY, Tiruveedhula P, Roorda A. Intersubject variability of foveal cone photoreceptor density in relation to eye length. Invest Ophthalmol Vis Sci 2010; 51:6858–6867.
Chui TY, Song H, Burns SA. Adaptive-optics imaging of human cone photoreceptor distribution. J Opt Soc Am A Opt Image Sci Vis 2008; 25:3021–3029.
Song H, Chui TYP, Zhong Z, Elsner AE, Burns SA. Variation of cone photoreceptor packing density with retinal eccentricity and age. Invest Ophthalmol Vis Sci 2011; 52:7376–7384.
Curcio CA, Sloan KR, Kalina RE, Hendrickson AE. Human photoreceptor topography. J Comp Neurol 1990; 292:497–523.
Curcio CA, Sloan KR. Packing geometry of human cone photoreceptors: variation with eccentricity and evidence of local anisotropy. Vis Neurosci 1992; 9:169–180.
Curcio CA, Sloan KR, Packer O, Hendrickson AE, Kalina RE. Distribution of cones in human and monkey retina: individual variability and radial asymmetry. Science 1987; 236:579–582.
Østerberg GA. Topography of the layer of rods and cones in the human retina. Acta Ophthalmol 1935; 13:1–97.
Jonas JB, Schneider U, Naumann GO. Count and density of human retinal photoreceptors. Graef Arch Clin Exp Ophthal 1992; 230:505–510.
Sun JK, Prager S, Radwan S, Ramsey DJ, Silva PS, Kwak H et al.
Photoreceptor mosaic changes in diabetic eye disease assessed by adaptive optics scanning laser ophthalmoscopy (AOSLO). Invest Ophth Vis Sci 2012; 53:4647.
Scoles D, Sulai YN, Langlo CS, Fishman GA, Curcio CA, Carroll J, Dubra A. In vivo imaging of human cone photoreceptor inner segments. Invest Ophthamol Vis Sci 2014; 55:4244–4251.
Polyak SL. The retina: the anatomy and the histology of the retina in man, ape, and monkey, including the consideration of visual functions, the history of physiological optics, and the histological laboratory technique. Chicago, IL: The University of Chicago Press; 1941.
Alpern M, Ching CC, Kitahara K. The directional sensitivity of retinal rods. J Physiol 1983; 343:577–592.
Van Loo JA Jr, Enoch JM. The scotopic Stiles-Crawford effect. Vision Res 1975; 15:1005–1009.
Nordby K, Sharpe LT. The directional sensitivity of the photoreceptors in the human achromat. J Physiol 1988; 399:267–281.
Dubra A, Sulai Y, Norris JL, Cooper RF, Dubis AM, Williams DR, Carroll J. Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope. Biomed Opt Express 2011; 2:1864–1876.
Roorda A, Zhang Y, Duncan JL. High-resolution in vivo imaging of the RPE mosaic in eyes with retinal disease. Invest Ophthalmol Vis Sci 2007; 48:2297–2303.
Roorda A, Romero-Borja F, Donnelly W 3rd, Queener H, Hebert T, Campbell M. Adaptive optics scanning laser ophthalmoscopy. Opt Express 2002; 10:405–412.
Takayama K, Ooto S, Hangai M, Arakawa N, Oshima S, Shibata N et al.
High-Resolution imaging of the retinal nerve fiber layer in normal eyes using adaptive optics scanning laser ophthalmoscopy. PLoS One 2012; 7:e33158.
Huang G, Qi X, Chui TY, Zhong Z, Burns SA. A clinical planning module for adaptive optics SLO imaging. Optom Vis Sci 2012; 89:593–601.
Kocaoglu OP, Cense B, Jonnal RS, Wang Q, Lee S, Gao W, Miller DT. Imaging retinal nerve fiber bundles using optical coherence tomography with adaptive optics. Vis Res 2011; 51:1835–1844.
Roorda A. Adaptive optics ophthalmoscopy. J Refract Surg 2000; 16:S602–S607.
Duncan JL, Zhang Y, Gandhi J, Nakanishi C, Othman M, Branham KE et al.
High-resolution imaging with adaptive optics in patients with inherited retinal degeneration. Invest Ophthalmol Vis Sci 2007; 48:3283–3291.
Sun LW, Johnson RD, Langlo C, Cooper RF, Razeen M, Russillo MC et al.
Assessing photoreceptor structure in retinitis pigmentosa and usher syndrome. Invest Ophthalmol Vis Sci 2016; 57:2428–2442.
Grover S, Fishman GA, Anderson RJ, Alexander KR, Derlacki DJ. Rate of visual field loss in retinitis pigmentosa. Ophthalmology 1997; 104:460–465.
Fishman GA, Bozbeyoglu S, Massof RW, Kimberling W. Natural course of visual field loss in patients with Type 2 Usher syndrome. Retina 2007; 27:601–608.
Talcott KE, Ratnam K, Sundquist SM, Lucero AS, Lujan BJ, Tao W et al.
Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment. Invest Ophthalmol Vis Sci 2011; 52:2219–2226.
Jacobson SG, Aleman TS, Cideciyan AV, Sumaroka A, Schwartz SB, Windsor EA et al.
Identifying photoreceptors in blind eyes caused by RPE65 mutations: Prerequisite for human gene therapy success. Proc Natl Acad Sci USA 2005; 102:6177–6182.
Syed R, Sundquist SM, Ratnam K, Zayit-Soudry S, Zhang Y, Crawford JB et al.
High-resolution images of retinal structure in patients with choroideremia. Invest Ophthalmol Vis Sci 2013; 54:950–961.
Duncan JL, Ratnam K, Birch DG, Sundquist SM, Lucero AS, Zhang Y et al.
Abnormal cone structure in foveal schisis cavities in X-linked retinoschisis from mutations in exon 6 of the RS1 gene. Invest Ophthalmol Vis Sci 2011; 52:9614–9623.
Alexander JJ, Umino Y, Everhart D, Chang B, Min SH, Li Q et al.
Restoration of cone vision in a mouse model of achromatopsia. Nat Med 2007; 13:685–687.
Komaromy AM, Alexander JJ, Chiodo VA, Hauswirth WW, Acland GM, Aguirre GD. Cone directed gene therapy with rAAV leads to restoration of cone function in a canine model of achromatopsia. Invest Ophth Vis Sci 2007; 48:4614.
Komaromy A, Alexander JJ, Rowlan JS, Garcia MM, Chiodo VA, Kaya A et al.
Gene therapy rescues cone function in congenital achromatopsia. Hum Mol Genet 2010; 19:2581–2593.
Carvalho LS, Xu J, Pearson R, Smith AJ, Bainbridge JW, Morris LM et al.
Long-term and age-dependent restoration of visual function in a mouse model of CNGB3-associated achromatopsia following gene therapy. Hum Mol Genet 2011; 20:3161–3175.
Abozaid MA, Langlo CS, Dubis AM, Michaelides M, Tarima S, Carroll J. Reliability and repeatability of cone density measurements in patients with congenital achromatopsia. Adv Exp Med Biol 2016; 854:277–283.
Marmor MF, Choi SS, Zawadzki RJ, Werner JS. Visual insignificance of the foveal pit: reassessment of foveal hypoplasia as fovea plana. Arch Ophthalmol 2008; 126:907–913.
McAllister JT, Dubis AM, Tait DM, Ostler S, Rha J, Stepien KE et al.
Arrested development: high-resolution imaging of foveal morphology in albinism. Vision Res 2010; 50:810–817.
Wilk MA, McAllister JT, Cooper RF, Dubis AM, Patitucci TN, Summerfelt P et al.
Relationship Between Foveal Cone Specialization and Pit Morphology in Albinism. Invest Ophthalmol Vis Sci 2014; 55:4186–4198.
Quigley HA. Glaucoma. Lancet 2011; 377:1367–1377.
Scoles D, Gray DC, Hunter JJ, Wolfe R, Gee BP, Geng Y et al.
In-vivo imaging of retinal nerve fiber layer vasculature: imaging − histology comparison. BMC Ophthalmol 2009; 9:9.
Ivers KM, Li C, Patel N, Sredar N, Luo X, Quenner H et al.
Reproducibility of measuring lamina cribrosa pore geometry in human and nonhuman primates with in vivo adaptive optics imaging. Invest Ophth Vis Sci 2011; 52:5473–5480.
Akagi T, Hangai M, Takayama K, Nonaka A, Ooto S, Yoshimura N. In vivo imaging of lamina cribrosa pores by adaptive optics scanning laser ophthalmoscopy. Invest Ophthalmol Vis Sci 2012; 53:4111–4119.
Nadler Z, Wang B, Schuman JS, Ferguson RD, Patel A, Hammer DX et al.
In vivo three-dimensional characterization of the healthy human lamina cribrosa with adaptive optics spectral-domain optical coherence tomography. Invest Ophthalmol Vis Sci 2014; 55:6459–6466.
Chen MF, Chui TYP, Alhadeff P, Rosen RB, Ritch R, Dubra A et al.
Adaptive optics imaging of healthy and abnormal regions of retinal nerve fiber bundles of patients with glaucoma. Invest Ophthalmol Vis Sci 2015; 56:674–681.
Hood DC, Chen MF, Lee D, Epstein B, Alhadeff P, Rosen RB et al.
Confocal adaptive optics imaging of peripapillary nerve fiber bundles: implications for glaucomatous damage seen on circumpapillary OCT scans. Trans Vis Sci Tech 2015; 4:12.
Cunha-Vaz JG. Pathophysiology of diabetic retinopathy. Br J Ophthalmol 1978; 62:351–355.
Kern TS, Engerman RL. Vascular lesions in diabetes are distributed non-uniformly within the retina. Exp Eye Res 1995; 60:545–549.
Moore J, Bagley S, Ireland G, McLeod D, Boulton ME. Three dimensional analysis of microaneurysms in the human diabetic retina. J Anat 1999; 194:89–110.
Lieth E, Gardner TW, Barber AJ, Antonetti DA. Retinal neurodegeneration: early pathology in diabetes. Clin Exp Ophthalmol 2000; 28:3–8.
Barber AJ. A new view of diabetic retinopathy: a neurodegenerative disease of the eye. Prog Neuropsychopharmacol Biol Psychiatry 2003; 27:283–290.
Tam J, Dhamdhere KP, Tiruveedhula P, Manzanera S, Barez S, Bearse MA Jr et al.
Disruption of the retinal parafoveal capillary newtork in type 2 diabetes before the onset of diabetic retinopathy. Invest Ophth Vis Sci 2011; 52:9257–9266.
Tam J, Dhamdhere KP, Tiruveedhula P, Lujan BJ, Johnson RN, Bearse MA Jr et al.
Subclinical capillary changes in non-proliferative diabetic retinopathy. Optom Vis Sci 2012; 89:E692–E703.
Lombardo M, Parravano M, Serrao S, Ducoli P, Stirpe M, Lombardo G. Analysis of retinal capillaries in patients with type 1 diabetes and non proliferative diabetic retinopathy using adaptive optics imaging. Retina 2013; 33:1630–1639.
Han DP, Croskrey JA, Dubis AM, Schroeder B, Rha J, Carroll J. Adaptive optics and spectral domain optical coherence tomography of human photoreceptor structure after short-duration [corrected] pascal macular grid and panretinal laser photocoagulation. Arch Ophthalmol 2012; 130:518–521.
Burns SA, Elsner AE, Chui TY, VanNasdale DA Jr, Clark CA, Gast TJ et al.
In vivo adaptive optics microvascular imaging in diabetic patients without clinically severe diabetic retinopathy. Biomed Opt Express 2014; 5:961–974.
Bressler NM. Age-related macular degeneration is the leading cause of blindness. JAMA 2004; 291:1900–1901.
Freeman SR, Kozak I, Cheng L, Bartsch DU, Mojana F, Nigam N et al.
Optical coherence tomography raster scanning and manual segmentation in determining drusen volume in age-related macular degeneration. Retina 2010; 30:431–435.
Greenberg JP, Duncker T, Woods RL, Smith RT, Sparrow JR, Delori FC. Quantitative fundus autofluorescence in healthy eyes. Invest Ophthalmol Vis Sci 2013; 54:5684–5693.
Querques G, Massamba N, Guigui B, Lea Q, Lamory B, Soubrane G et al.
In vivo evaluation of photoreceptor mosaic in early onset large colloid drusen using adaptive optics. Acta Ophthalmol (Copenh) 2012; 90:e327–e328.
Godara P, Siebe C, Rha J, Michaelides M, Carroll J. Assessing the photoreceptor mosaic over drusen using adaptive optics and SD-OCT. Ophthalmic Surg Lasers Imaging 2010; 41:S104–S108.
Boretsky A, Khan F, Burnett G, Hammer DX, Ferguson RD, van Kuijk F et al.
In vivo imaging of photoreceptor disruption associated with age-related macular degeneration: a pilot study. Lasers Surg Med 2012; 44:603–610.
Nakashima K, Ullern M, Benchaboune M, Sahel JA, Paques M. Adaptive optics imaging of geographic atrophy. Invest Ophth Vis Sci 2012; 53:2052.
Soudry SZ, Duncan JL, Syed R, Menghini M, Roorda AJ. Cone structure imaged with adaptive optics scanning laser ophthalmoscopy in eyes with nonneovascular age-related macular degeneration. Invest Ophthalmol Vis Sci 2013; 54:7498–7509.
Zhang Y, Poonja S, Roorda A. MEMS-based adaptive optics scanning laser ophthalmoscopy. Opt Lett 2006; 31:1268–1270.
Mujat M, Ferguson RD, Patel AH, Iftimia N, Lue N, Hammer DX. High resolution multimodal clinical ophthalmic imaging system. Opt Express 2010; 18:11607–11621.
Bigelow CE, Iftimia NV, Ferguson RD, Ustun TE, Bloom B, Hammer DX. Compact multimodal adaptive-optics spectral-domain optical coherence tomography instrument for retinal imaging. J Opt Soc Am A Opt Image Sci Vis 2007; 24:1327–1336.
Burns SA, Tumbar R, Elsner AE, Ferguson D, Hammer DX. Large-field-of-view, modular, stabilized, adaptive-optics-based scanning laser ophthalmoscope. J Opt Soc Am A Opt Image Sci Vis 2007; 24:1313–1326.
Mujat M, Ferguson RD, Iftimia N, Hammer DX. Compact adaptive optics line scanning ophthalmoscope. Opt Express 2009; 17:10242–10258.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]