Agreement in Cone Density Derived from Gaze-Directed Single Images Versus Wide-Field Montage Using Adaptive Optics Flood Illumination Ophthalmoscopy

Pearls and Pitfalls of Adaptive Optics Ophthalmoscopy in Inherited Retinal Diseases

Adaptive optics (AO) retinal imaging enables individual photoreceptors to be visualized in the clinical setting. AO imaging can be a powerful clinical tool for detecting photoreceptor degeneration at a cellular level that might be overlooked through conventional structural assessments, such as spectral-domain optical coherence tomography (SD-OCT). Therefore, AO imaging has gained significant interest in the study of photoreceptor degeneration, one of the most common causes of inherited blindness. Growing evidence supports that AO imaging may be useful for diagnosing early-stage retinal dystrophy before it becomes apparent on fundus examination or conventional retinal imaging. In addition, serial AO imaging may detect structural disease progression in early-stage disease over a shorter period compared to SD-OCT. Although AO imaging is gaining popularity as a structural endpoint in clinical trials, the results should be interpreted with caution due to several pitfalls, including the lack of standardized imaging and image analysis protocols, frequent ocular comorbidities that affect image quality, and significant interindividual variation of normal values. Herein, we summarize the current state-of-the-art AO imaging and review its potential applications, limitations, and pitfalls in patients with inherited retinal diseases.

Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects

This work presents a novel fully automated method for retinal analysis in images acquired with a flood illuminated adaptive optics retinal camera (AO-FIO). The proposed processing pipeline consists of several steps: First, we register single AO-FIO images in a montage image capturing a larger retinal area. The registration is performed by combination of phase correlation and the scale-invariant feature transform method. A set of 200 AO-FIO images from 10 healthy subjects (10 images from left eye and 10 images from right eye) is processed into 20 montage images and mutually aligned according to the automatically detected fovea center. As a second step, the photoreceptors in the montage images are detected using a method based on regional maxima localization, where the detector parameters were determined with Bayesian optimization according to manually labeled photoreceptors by three evaluators. The detection assessment, based on Dice coefficient, ranges from 0.72 to 0.8. In the next step, the corresponding density maps are generated for each of the montage images. As a final step, representative averaged photoreceptor density maps are created for the left and right eye and thus enabling comprehensive analysis across the montage images and a straightforward comparison with available histological data and other published studies. Our proposed method and software thus enable us to generate AO-based photoreceptor density maps for all measured locations fully automatically, and thus it is suitable for large studies, as those are in pressing need for automated approaches. In addition, the application MATADOR (MATlab ADaptive Optics Retinal Image Analysis) that implements the described pipeline and the dataset with photoreceptor labels are made publicly available.

Application of Adaptive Optics in Ophthalmology

The eye, the photoreceptive organ used to perceive the external environment, is of great importance to humans. It has been proven that some diseases in humans are accompanied by fundus changes; therefore, the health status of people may be interpreted from retinal images. However, the human eye is not a perfect refractive system for the existence of ocular aberrations. These aberrations not only affect the ability of human visual discrimination and recognition, but restrict the observation of the fine structures of human eye and reduce the possibility of exploring the mechanisms of eye disease. Adaptive optics (AO) is a technique that corrects optical wavefront aberrations. Once integrated into ophthalmoscopes, AO enables retinal imaging at the cellular level. This paper illustrates the principle of AO in correcting wavefront aberrations in human eyes, and then reviews the applications and advances of AO in ophthalmology, including the adaptive optics fundus camera (AO-FC), the adaptive optics scanning laser ophthalmoscope (AO-SLO), the adaptive optics optical coherence tomography (AO-OCT), and their combined multimodal imaging technologies. The future development trend of AO in ophthalmology is also prospected.

Impact of Reference Center Choice on Adaptive Optics Imaging Cone Mosaic Analysis

Purpose

Foveal center marking is a key step in retinal image analysis. We investigated the discordance between the adaptive optics (AO) montage center (AMC) and the foveal pit center (FPC) and its implications for cone mosaic analysis using a commercial flood-illumination AO camera.

Methods

Thirty eyes of 30 individuals (including 15 healthy and 15 patients with rod–cone dystrophy) were included. Spectral-domain optical coherence tomography was used to determine the FPC, and flood-illumination AO imaging was performed with overlapping image frames to create an AO montage. The AMC was determined by averaging the (0,0) coordinates in the four paracentral overlapping AO image frames. Cone mosaic measurements at various retinal eccentricities were compared between corresponding retinal loci relative to the AMC or FPC.

Results

AMCs were located temporally to the FPCs in 14 of 15 eyes in both groups. The average AMC–FPC discordance was 0.85° among healthy controls and 0.33° among patients with rod-cone dystrophy (P < 0.05). The distance of the AMC from the FPC was a significant determinant of the cone density (β estimate = 218 cells/deg²/deg; 95% confidence interval [CI], 107–330; P < 0.001) and inter-cone distance (β estimate = 0.28 arcmin/deg; 95% CI, 0.15–0.40; P < 0.001), after adjustment for age, sex, axial length, spherical equivalent, eccentricity, and disease status.

Conclusions

There is a marked mismatch between the AMC and FPC in healthy eyes that may be modified by disease process such as rod–cone dystrophy. We recommend users of AO imaging systems carefully align the AO montage with a foveal anatomical landmark, such as the FPC, to ensure precise and reproducible localization of the eccentricities and regions of interest for cone mosaic analysis.

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.

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 meridians). Cone density (CD), distribution regularity, and intercone distance (ICD) were calculated 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 < 0.01). Retinal DLS across the meridional grid changed significantly with each 1000 cells/deg² 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.

Cellular imaging of inherited retinal diseases using adaptive optics

Adaptive optics (AO) is an insightful tool that has been increasingly applied to existing imaging systems for viewing the retina at a cellular level. By correcting for individual optical aberrations, AO offers an improvement in transverse resolution from 10-15 μm to ~2 μm, enabling assessment of individual retinal cell types. One of the settings in which its utility has been recognised is that of the inherited retinal diseases (IRDs), the genetic and clinical heterogeneity of which warrants better cellular characterisation. In this review, we provide a summary of the basic principles of AO, its integration into multiple retinal imaging modalities and its clinical applications, focusing primarily on IRDs. Furthermore, we present a comprehensive summary of AO-based cellular findings in IRDs according to their associated disease-causing genes.