Enhanced Visualization of Subtle Outer Retinal Pathology by <i>En Face</i> Optical Coherence Tomography and Correlation with Multi-Modal Imaging - Fig 1

<p><i>En face</i> optical coherence tomography images of the ellipsoid zone (A-I:V), interdigitation zone (B-I:V), retinal pigment epithelium (C-I:V) and Bruch’s membrane (D-I:V). Images from the first 3 columns are from a normal subject and the last column is from a patient with Bietti crystalline dystrophy. The scanning protocol covers a 15° (horizontal) × 10° (vertical) field of view on the retina. Each row (I:V) corresponds to increasing separation between consecutive B-scans: 11, 30, 60, 120 and 240 μm.</p

Evaluation of focus and deep learning methods for automated image grading and factors influencing image quality in adaptive optics ophthalmoscopy

Adaptive optics flood illumination ophthalmoscopy (AO-FIO) is an established imaging tool in the investigation of retinal diseases. However, the clinical interpretation of AO-FIO images can be challenging due to varied image quality. Therefore, image quality assessment is essential before interpretation. An image assessment tool will also assist further work on improving the image quality, either during acquisition or post processing. In this paper, we describe, validate and compare two automated image quality assessment methods; the energy of Laplacian focus operator (LAPE; not commonly used but easily implemented) and convolutional neural network (CNN; effective but more complex approach). We also evaluate the effects of subject age, axial length, refractive error, fixation stability, disease status and retinal location on AO-FIO image quality. Based on analysis of 10,250 images of 50 × 50 μm size, at 41 retinal locations, from 50 subjects we demonstrate that CNN slightly outperforms LAPE in image quality assessment. CNN achieves accuracy of 89%, whereas LAPE metric achieves 73% and 80% (for a linear regression and random forest multiclass classifier methods, respectively) compared to ground truth. Furthermore, the retinal location, age and disease are factors that can influence the likelihood of poor image quality

Multimodal imaging showing the left eye of Case 1: angioid streaks secondary to pseudoxanthoma elasticum.

<p>Near-infrared reflectance NIR (A), infrared autofluorescence IRAF (B), microperimetry (C), blue-light autofluorescence BAF (D), and adaptive optics flood illumination ophthalmoscopy AO-FIO cone montage and density maps overlaid on NIR image (E, F) of the central 15° × 10° showing the angioid streaks as irregular linear hypo-reflective lesions. The occasional streaks showing hypo-autofluorescence, relative scotoma in regions unaffected by angioid streaks and cone tip reflexes within the region of angioid streaks. Microperimetry overlaid on cone tip reflex montage (G) showed reduced cone densities compared with normative values, both in regions with reduced (H:I) and normal sensitivity (H:II-III). B-scans flattened to the base of retinal pigment epithelium RPE (I) are used for generating e<i>n face</i> OCT images of the ellipsoid zone (J), apical portion of the RPE (K) and basal portion of the RPE (L). Inaccuracy in automated segmentation of the basal RPE by HE resulted in artefact in the ellipsoid zone and RPE <i>en face</i> OCT images (red arrow in K, L and M). The linear pattern of angioid streaks were only visible in the <i>en face</i> OCT image derived from the basal portion of the RPE (L). HE, Heidelberg Eye Explorer software (Heidelberg Engineering); CU, Custom-built software using graph-search theory algorithm. White dotted rectangle corresponds to region for which <i>en face</i> OCT images are generated.</p

Description of illustrative cases.

<p>Description of illustrative cases.</p

Volumetric scanning protocols available in Spectralis SD-OCT instrument.

<p>Volumetric scanning protocols available in Spectralis SD-OCT instrument.</p

Multimodal imaging showing the left eye of Case 4: Bietti crystalline dystrophy.

<p>Although crystals are seen, the boundary of retinal degeneration is not clearly visualized on Near-infrared reflectance NIR (A), infrared autofluorescence IRAF (B) or blue-light autofluorescence BAF (C). Microperimetry (D) demonstrates preserved retinal sensitivity within 1° of the center of fixation indicating the presence of foveal photoreceptor cells supported by an island of RPE. Crystals are also visualized with adaptive optics flood-illumination ophthalmoscopy AO-FIO (E) but the poor signal from cones precluded AO-FIO cone density mapping (F). The OCT B-scans (G) reveal atrophy of the RPE and outer retinal layers. As a direct consequence, the errors in automated segmentation of the Bruch’s membrane by HE lead to artefacts in the <i>en face</i> OCT images of the ellipsoid-RPE slab (H-HE) and the Bruch’s membrane (I-HE). Our custom algorithm was able to generate ellipsoid-RPE (H-CU) and Bruch’s membrane (I-CU) <i>en face</i> OCT images. The well-defined central island of ellipsoid-RPE correlated well with the region of retinal sensitivity on microperimetry (J). The distribution of crystals seen on AO imaging (E, yellow arrows) mirrors the lesions seen on <i>en face</i> OCT of Bruch’s membrane (I-CU, K, yellow arrows).</p

Retinal differential light sensitivity variation across the macula in healthy subjects: Importance of cone separation and loci eccentricity

Purpose: Microperimetry measures differential light sensitivity (DLS) at specific retinal locations. The aim of this study is to examine the variation in DLS across the macula and the contribution to this variation of cone distribution metrics and retinal eccentricity. Methods: Forty healthy eyes of 40 subjects were examined by microperimetry (MAIA) and adaptive optics imaging (rtx1). Retinal DLS was measured using the grid patterns: foveal (2°–3°), macular (3°–7°), and meridional (2°–8° on horizontal and vertical meridi-ans). Cone density (CD), distribution regularity, and intercone distance (ICD) were calcu-lated at the respective test loci coordinates. Linear mixed-effects regression was used to examine the association between cone distribution metrics and loci eccentricity, and retinal DLS. Results: An eccentricity-dependent reduction in DLS was observed on all MAIA grids, which was greatest at the foveal-parafoveal junction (2°–3°) (−0.58 dB per degree, 95% confidence interval [CI]; −0.91 to −0.24 dB, P 2 change in CD (0.85 dB, 95% CI; 0.10 to 1.61 dB, P = 0.03), but not with each arcmin change in ICD (1.36 dB, 95% CI; −2.93 to 0.20 dB, P = 0.09). Conclusions: We demonstrate significant variation in DLS across the macula. Topographical change in cone separation is an important determinant of the variation in DLS at the foveal-parafoveal junction. We caution the extrapolation of changes in DLS measurements to cone distribution because the relationship between these variables is complex. Translational Relevance: Cone density is an independent determinant of DLS in the foveal-parafoveal junction in healthy eyes.</p