Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

PurposeThe purpose of this study was to compare retinal nerve fiber layer (RNFL) thickness and optical coherence tomography angiography (OCT-A) retinal vasculature measurements in healthy, glaucoma suspect, and glaucoma patients.MethodsTwo hundred sixty-one eyes of 164 healthy, glaucoma suspect, and open-angle glaucoma (OAG) participants from the Diagnostic Innovations in Glaucoma Study with good quality OCT-A images were included. Retinal vasculature information was summarized as a vessel density map and as vessel density (%), which is the proportion of flowing vessel area over the total area evaluated. Two vessel density measurements extracted from the RNFL were analyzed: (1) circumpapillary vessel density (cpVD) measured in a 750-μm-wide elliptical annulus around the disc and (2) whole image vessel density (wiVD) measured over the entire image. Areas under the receiver operating characteristic curves (AUROC) were used to evaluate diagnostic accuracy.ResultsAge-adjusted mean vessel density was significantly lower in OAG eyes compared with glaucoma suspects and healthy eyes. (cpVD: 55.1 ± 7%, 60.3 ± 5%, and 64.2 ± 3%, respectively; P < 0.001; and wiVD: 46.2 ± 6%, 51.3 ± 5%, and 56.6 ± 3%, respectively; P < 0.001). For differentiating between glaucoma and healthy eyes, the age-adjusted AUROC was highest for wiVD (0.94), followed by RNFL thickness (0.92) and cpVD (0.83). The AUROCs for differentiating between healthy and glaucoma suspect eyes were highest for wiVD (0.70), followed by cpVD (0.65) and RNFL thickness (0.65).ConclusionsOptical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes. These results suggest that OCT-A measurements reflect damage to tissues relevant to the pathophysiology of OAG

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Purpose: To develop a deep learning approach to de-noise optical coherence tomography (OCT) B-scans of the optic nerve head (ONH). Methods: Volume scans consisting of 97 horizontal B-scans were acquired through the center of the ONH using a commercial OCT device (Spectralis) for both eyes of 20 subjects. For each eye, single-frame (without signal averaging), and multi-frame (75x signal averaging) volume scans were obtained. A custom deep learning network was then designed and trained with 2,328 "clean B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance of the de-noising algorithm was assessed qualitatively, and quantitatively on 1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio (CNR), and mean structural similarity index metrics (MSSIM). Results: The proposed algorithm successfully denoised unseen single-frame OCT B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB (denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm 0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from $0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with the corresponding multi-frame B-scans. Conclusions: Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort

A Novel Reporter Mouse Uncovers Endogenous Brn3b Expression.

Brn3b (Pou4f2) is a class-4 POU domain transcription factor known to play central roles in the development of different neuronal populations of the Central Nervous System, including retinal ganglion cells (RGCs), the neurons that connect the retina with the visual centers of the brain. Here, we have used CRISPR-based genetic engineering to generate a Brn3b-mCherry reporter mouse without altering the endogenous expression of Brn3b. In our mouse line, mCherry faithfully recapitulates normal Brn3b expression in the retina, the optic tracts, the midbrain tectum, and the trigeminal ganglia. The high sensitivity of mCherry also revealed novel expression of Brn3b in the neuroectodermal cells of the optic stalk during early stages of eye development. Importantly, the fluorescent intensity of Brn3b-mCherry in our reporter mice allows for noninvasive live imaging of RGCs using Scanning Laser Ophthalmoscopy (SLO), providing a novel tool for longitudinal monitoring of RGCs

In vivo laser Doppler holography of the human retina

The eye offers a unique opportunity for non-invasive exploration of cardiovascular diseases. Optical angiography in the retina requires sensitive measurements, which hinders conventional full-field laser Doppler imaging schemes. To overcome this limitation, we used digital holography to perform laser Doppler perfusion imaging of the human retina in vivo with near-infrared light. Wideband measurements of the beat frequency spectrum of optical interferograms recorded with a 39 kHz CMOS camera are analyzed by short-time Fourier transformation. Power Doppler images and movies drawn from the zeroth moment of the power spectrum density reveal blood flows in retinal and choroidal vessels over 512 $\times$ 512 pixels covering 2.4 $\times$ 2.4 mm$^2$ on the retina with a 13 ms temporal resolution.Comment: 5 pages, 5 figure

Detection and Mosaicing through Deep Learning Models for Low-Quality Retinal Images

Glaucoma is a severe eye disease that is asymptomatic in the initial stages and can lead to blindness, due to its degenerative characteristic. There isn’t any available cure for it, and it is the second most common cause of blindness in the world. Most of the people affected by it only discovers the disease when it is already too late. Regular visits to the ophthalmologist are the best way to prevent or contain it, with a precise diagnosis performed with professional equipment. From another perspective, for some individuals or populations, this task can be difficult to accomplish, due to several restrictions, such as low incoming resources, geographical adversities, and travelling restrictions (distance, lack of means of transportation, etc.). Also, logistically, due to its dimensions, relocating the professional equipment can be expensive, thus becoming not viable to bring them to remote areas. In the market, low-cost products like the D-Eye lens offer an alternative to meet this need. The D-Eye lens can be attached to a smartphone to capture fundus images, but it presents a major drawback in terms of lower-quality imaging when compared to professional equipment. This work presents and evaluates methods for eye reading with D-Eye recordings. This involves exposing the retina in two steps: object detection and summarization via object mosaicing. Deep learning methods, such as the YOLO family architecture, were used for retina registration as an object detector. The summarization methods presented and inferred in this work mosaiced the best retina images together to produce a more detailed resultant image. After selecting the best workflow from these methods, a final inference was performed and visually evaluated, the results were not rich enough to serve as a pre-screening medical assessment, determining that improvements in the actual algorithm and technology are needed to retrieve better imaging

Assessment of total retinal blood flow using Doppler Fourier Domain Optical Coherence Tomography during systemic hypercapnia and hypocapnia.

The purpose of this study was to investigate changes in total retinal blood flow (RBF) using Doppler Fourier Domain Optical Coherence Tomography (Doppler FD-OCT) in response to the manipulation of systemic partial pressure of CO2 (PETCO2). Double circular Doppler blood flow scans were captured in nine healthy individuals (mean age ± standard deviation: 27.1 ± 4.1, six males) using the RTVue(™) FD-OCT (Optovue). PETCO2 was manipulated using a custom-designed computer-controlled gas blender (RespirAct(™)) connected to a sequential gas delivery rebreathing circuit. Doppler FD-OCT measurements were captured at baseline, during stages of hypercapnia (+5/+10/+15 mmHg PETCO2), return to baseline and during stages of hypocapnia (-5/-10/-15 mmHg PETCO2). Repeated measures analysis of variance (reANOVA) and Tukeys post hoc analysis were used to compare Doppler FD-OCT measurements between the various PETCO2 levels relative to baseline. The effect of PETCO2 on TRBF was also investigated using linear regression models. The average RBF significantly increased by 15% (P < 0.0001) with an increase in PETCO2 and decreased significantly by 10% with a decrease in PETCO2 (P = 0.001). Venous velocity significantly increased by 3.11% from baseline to extreme hypercapnia (P < 0.001) and reduced significantly by 2.01% at extreme hypocapnia (P = 0.012). No significant changes were found in the average venous area measurements under hypercapnia (P = 0.36) or hypocapnia (P = 0.40). Overall, increased and decreased PETCO2 values had a significant effect on RBF outcomes (P < 0.002). In healthy individuals, altered end-tidal CO2 levels significantly changed RBF as measured by Doppler FD-OCT