Objective To evaluate the association of subretinal hyper-reflective material (SHRM) with visual acuity (VA), geographic atrophy (GA) and scar in the Comparison of Age related Macular Degeneration Treatments Trials (CATT) Design Prospective cohort study within a randomized clinical trial. Participants The 1185 participants in CATT. Methods Participants were randomly assigned to ranibizumab or bevacizumab treatment monthly or as-needed. Masked readers graded scar and GA on fundus photography and fluorescein angiography images, SHRM on time domain (TD) and spectral domain (SD) optical coherence tomography (OCT) throughout 104 weeks. Measurements of SHRM height and width in the fovea, within the center 1mm2, or outside the center 1mm2 were obtained on SD-OCT images at 56 (n=76) and 104 (n=66) weeks. VA was measured by certified examiners. Main Outcome Measures SHRM presence, location and size, and associations with VA, scar, and GA. Results Among all CATT participants, the percentage with SHRM at enrollment was 77%, decreasing to 68% at 4 weeks after treatment and 54% at 104 weeks. At 104 weeks, scar was present more often in eyes with persistent SHRM than eyes with SHRM that resolved (64% vs. 31%; p<0.0001). Among eyes with detailed evaluation of SHRM at weeks 56 (n=76) and 104 (n=66), mean [SE] VA letter score was 73.5 [2.8], 73.1 [3.4], 65.3 [3.5], and 63.9 [3.7] when SHRM was absent, present outside the central 1mm2, present within the central 1mm2 but not the foveal center, or present at the foveal center (p=0.02). SHRM was present at the foveal center in 43 (30%), within the central 1mm2 in 21 (15%) and outside the central 1mm2 in 19 (13%). When SHRM was present, the median maximum height in microns under the fovea, within the central 1 mm2 including the fovea and anywhere within the scan was 86; 120; and 122, respectively. VA was decreased with greater SHRM height and width (p<0.05). Conclusions SHRM is common in eyes with NVAMD and often persists after anti-VEGF treatment. At 2 years, eyes with scar were more likely to have SHRM than other eyes. Greater SHRM height and width were associated with worse VA. SHRM is an important morphological biomarker in eyes with NVAMD.

This work studies retinal image registration in the context of the National Institutes of Health (NIH) Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents three major challenges for the image registration task. First, small overlaps between adjacent fields lead to inadequate landmark points for feature-based methods. Second, the non-uniform contrast/intensity distributions due to imperfect data acquisition will deteriorate the performance of area-based techniques. Third, high-resolution images contain large homogeneous nonvascular/texureless regions that weaken the capabilities of both feature-based and area-based techniques. In this work, we propose a hybrid retinal image registration approach for ETDRS images that effectively combines both area-based and feature-based methods. Four major steps are involved. First, the vascular tree is extracted by using an efficient local entropy-based thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Then image quality assessment regarding the ETDRS field definition is performed based on the translation model. If the image pair is accepted, higher-order transformations will be involved. Specifically, we use two types of features, landmark points and sampling points, for affine/quadratic model estimation. Three empirical conditions are derived experimentally to control the algorithm progress, so that we can achieve the lowest registration error and the highest success rate. Simulation results on 504 pairs of ETDRS images show the effectiveness and robustness of the proposed algorithm.

Aims To evaluate the performance of the RETeval device, a handheld instrument using flicker electroretinography (ERG) and pupillography on undilated subjects with diabetes, to detect vision-threatening diabetic retinopathy (VTDR). Methods Performance was measured using a cross-sectional, single armed, non-interventional, multi-site study with Early Treatment Diabetic Retinopathy Study 7-standard field, stereo, color fundus photography as the gold standard. The 468 subjects were randomized to a calibration phase (80%), whose ERG and pupillary waveforms were used to formulate an equation correlating with the presence of VTDR, and a validation phase (20%), used to independently validate that equation. The primary outcome was the prevalence-corrected area under the receiver operating characteristic (ROC) curve for the detection of VTDR. Results The area under the ROC curve was 0.86 for VTDR; with sensitivity of 83%, specificity was 78% and negative predictive value was 99%. The average testing time was 2.3 minutes. Conclusions With a VTDR prevalence similar to that in the US, the RETeval device will identify about 75% of the population as not having VTDR with 99% accuracy. The device is simple to use, does not require pupil dilation, and has a short testing time.

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In the United States, Diabetic Retinopathy (DR) is the leading cause of blindness in diabetic adults (Garrett, 2002). DR is usually asymptomatic until it progresses to later stages, culminating in vision-threatening diabetic retinopathy (VTDR). Treatment for this condition is 95% effective if discovered early, but screening compliance is poor (Lee, et al., 2003). The Integrated Behavior Model (IBM) is a model that predicts not only the intention to behave but also environmental constraints that may affect the behavior itself. A survey was developed using this model’s framework to understand impediments and motives for eye screening of diabetic people with different types of insurance. The interviews from this study will give a thorough understanding of the potential barriers and facilitators to diabetic eye screening, for those who are at high risk of VTDR. This understanding will contribute to the development of an interface to support the process of eye care coordination regarding DR.

We study 3-D retinal curvature estimation from multiple images that provides the fundamental geometry of the human retina and could be used for 3-D retina visualization and disease diagnosis purposes. An affine camera model is used for 3-D reconstruction due to its simplicity, linearity, and robustness. A major challenge is that a series of optics is involved in the retinal imaging process, including an actual fundus camera, a digital camera, and the optics of the human eye, all of which cause significant nonlinear distortions in retinal images. In this paper, we develop a new constrained optimization method that considers both the geometric shape of the human retina and nonlinear lens distortions. Moreover, we examine a variety of lens distortion models to approximate the optics of the human eye in order to create a smooth spherical surface for curvature estimation. The experimental results on both synthetic data and real retinal images validate the proposed algorithm.

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