Cone photoreceptor definition on adaptive optics retinal imaging

Muthiah, Manickam Nick; Gias, Carlos; Chen, Fred K.; Zhong, Joe; McClelland, Zoe; Sallo, Ferenc B; Pető, Tünde; Coffey, Peter; Cruz, Lyndon da

doi:10.1136/bjophthalmol-2013-304615

Cited by 54 publications

(60 citation statements)

References 28 publications

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“…29–35,49 Yet only 2 studies using AOSLO to date reported cone density or center-to-center spacing in the very foveal center, and both assessed fewer than 5 eyes. 31,36 Large inter-subject variability of foveal cones was shown by Li and co-workers who analyzed foveal centers in 4 eyes.…”

Section: Discussionmentioning

confidence: 99%

“…Recent advance in adaptive optics (AO) assisted retinal imaging 21–28 has made it possible to assess in the living human eye cone density, 29–35 which could be characterized previously only by histology. Although AO retinal imaging offers significant advantages in studying the impact of chorioretinal diseases on cones, the density and the variability of the foveal cones, especially cones in the very foveal center, have not been adequately assessed.…”

Section: Introductionmentioning

confidence: 99%

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Variability in Human Cone Topography Assessed by Adaptive Optics Scanning Laser Ophthalmoscopy

Zhang

Godara

Blanco

et al. 2015

American Journal of Ophthalmology

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Purpose To assess between- and within-individual variability of macular cone topography in the eyes of young adults. Design Observational case series. Methods Cone photoreceptors in 40 eyes of 20 subjects aged 19–29 years with normal maculae were imaged using a research adaptive optics scanning laser ophthalmoscope. Refractive errors ranged from −3.0 D to 0.63 D and differed by <0.50 D in fellow eyes. Cone density was assessed on a two-dimensional sampling grid over the central 2.4 mm × 2.4 mm. Between-individual variability was evaluated by coefficient of variation (CV). Within-individual variability was quantified by maximum difference and root-mean-square (RMS). Cones were cumulated over increasing eccentricity. Results Peak densities of foveal cones are 168,162 ± 23,529 cones/mm2 (mean ± SD) (CV = 0.14). The number of cones within the cone-dominated foveola (0.8–0.9 mm diameter) is 38,311 ± 2,319 (CV = 0.06). The RMS cone density difference between fellow eyes is 6.78%, and the maximum difference is 23.6%. Mixed model statistical analysis found no difference in the association between eccentricity and cone density in the superior/nasal (p=0.8503), superior/temporal (p=0.1551), inferior/nasal (p=0.8609), and inferior/temporal (p=0.6662) quadrants of fellow eyes. Conclusions New instrumentation imaged the smallest foveal cones, thus allowing accurate assignment of foveal centers and assessment of variability in macular cone density in a large sample of eyes. Though cone densities vary significantly in the fovea, the total number of foveolar cones are very similar both between- and within-subjects. Thus, the total number of foveolar cones may be an important measure of cone degeneration and loss.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variability in Human Cone Topography Assessed by Adaptive Optics Scanning Laser Ophthalmoscopy

Zhang

Godara

Blanco

et al. 2015

American Journal of Ophthalmology

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“…The two eccentricities were chosen to be a compromise between the resolution limit of the instrument, which does not allow cones to be resolved too close to the fovea (i.e. closer than 1 degree), and the presence of rods, which alter the cone relative spacing enough to be detectable by the rtx1 when further than 5 degrees [32]. The time between the images ranged from 45 minutes to 3 years at 2.5 degrees and from 45 minutes to 4 months at 4.0 degrees.…”

Section: Image Acquisitionmentioning

confidence: 99%

Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years

Mariotti¹,

Devaney²,

Lombardo³

et al. 2016

Biomed. Opt. Express

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Abstract:Although there is increasing interest in the investigation of cone reflectance variability, little is understood about its characteristics over long time scales. Cone detection and its automation is now becoming a fundamental step in the assessment and monitoring of the health of the retina and in the understanding of the photoreceptor physiology. In this work we provide an insight into the cone reflectance variability over time scales ranging from minutes to three years on the same eye, and for large areas of the retina (≥ 2.0 × 2.0 degrees) at two different retinal eccentricities using a commercial adaptive optics (AO) flood illumination retinal camera. We observed that the difference in reflectance observed in the cones increases with the time separation between the data acquisitions and this may have a negative impact on algorithms attempting to track cones over time. In addition, we determined that displacements of the light source within 0.35 mm of the pupil center, which is the farthest location from the pupil center used by operators of the AO camera to acquire high-quality images of the cone mosaic in clinical studies, does not significantly affect the cone detection and density estimation.

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“…Foveal cone tips are not visible on the rtx1 device because the resolution of the system is only 250 line pairs per mm. Therefore, cone identification within 2.5° from the foveal center is unreliable and not suitable for quantification [36]. …”

Section: Methodsmentioning

confidence: 99%

Enhanced Visualization of Subtle Outer Retinal Pathology by En Face Optical Coherence Tomography and Correlation with Multi-Modal Imaging

Sampson¹,

Alonso‐Caneiro

Chew³

et al. 2016

PLoS ONE

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PurposeTo present en face optical coherence tomography (OCT) images generated by graph-search theory algorithm-based custom software and examine correlation with other imaging modalities.MethodsEn face OCT images derived from high density OCT volumetric scans of 3 healthy subjects and 4 patients using a custom algorithm (graph-search theory) and commercial software (Heidelberg Eye Explorer software (Heidelberg Engineering)) were compared and correlated with near infrared reflectance, fundus autofluorescence, adaptive optics flood-illumination ophthalmoscopy (AO-FIO) and microperimetry.ResultsCommercial software was unable to generate accurate en face OCT images in eyes with retinal pigment epithelium (RPE) pathology due to segmentation error at the level of Bruch’s membrane (BM). Accurate segmentation of the basal RPE and BM was achieved using custom software. The en face OCT images from eyes with isolated interdigitation or ellipsoid zone pathology were of similar quality between custom software and Heidelberg Eye Explorer software in the absence of any other significant outer retinal pathology. En face OCT images demonstrated angioid streaks, lesions of acute macular neuroretinopathy, hydroxychloroquine toxicity and Bietti crystalline deposits that correlated with other imaging modalities.ConclusionsGraph-search theory algorithm helps to overcome the limitations of outer retinal segmentation inaccuracies in commercial software. En face OCT images can provide detailed topography of the reflectivity within a specific layer of the retina which correlates with other forms of fundus imaging. Our results highlight the need for standardization of image reflectivity to facilitate quantification of en face OCT images and longitudinal analysis.

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Cone photoreceptor definition on adaptive optics retinal imaging

Cited by 54 publications

References 28 publications

Variability in Human Cone Topography Assessed by Adaptive Optics Scanning Laser Ophthalmoscopy

Variability in Human Cone Topography Assessed by Adaptive Optics Scanning Laser Ophthalmoscopy

Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years

Enhanced Visualization of Subtle Outer Retinal Pathology by En Face Optical Coherence Tomography and Correlation with Multi-Modal Imaging

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