Volumetric microvascular imaging of human retina using optical coherence tomography with a novel motion contrast technique

Phase variance-based motion contrast imaging is demonstrated using a spectral domain optical coherence tomography system for the in vivo human retina. This contrast technique spatially identifies locations of motion within the retina primarily associated with vasculature. Histogram-based noise analysis of the motion contrast images was used to reduce the motion noise created by transverse eye motion. En face summation images created from the 3D motion contrast data are presented with segmentation of selected retinal layers to provide non-invasive vascular visualization comparable to currently used invasive angiographic imaging. This motion contrast technique has demonstrated the ability to visualize resolution-limited vasculature independent of vessel orientation and flow velocity

In vivo human retinal and choroidal vasculature visualization using differential phase contrast swept source optical coherence tomography at 1060 nm

A differential phase contrast (DPC) method is validated for in vivo human retinal and choroidal vasculature visualization using high-speed swept-source optical coherence tomography (SS-OCT) at 1060 nm. The vasculature was identified as regions of motion by creating differential phase variance (DPV) tomograms: multiple B-scans were collected of individual slices through the retina and the variance of the phase differences was calculated. DPV captured the small vessels and the meshwork of capillaries associated with the inner retina in en face images over 4 mm^2 in a normal subject. En face DPV images were capable of capturing the microvasculature and regions of motion through the inner retina and choroid

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

Differential intensity contrast swept source optical coherence tomography for human retinal vasculature visualization

We demonstrate an intensity-based motion sensitive method, called differential logarithmic intensity variance (DLOGIV), for 3D microvasculature imaging and foveal avascular zone (FAZ) visualization in the in vivo human retina using swept source optical coherence tomog. (SS-OCT) at 1060 nm. A motion sensitive SS-OCT system was developed operating at 50,000 A-lines/s with 5.9 μm axial resoln., and used to collect 3D images over 4 mm^2 in a normal subject eye. Multiple B-scans were acquired at each individual slice through the retina and the variance of differences of logarithmic intensities as well as the differential phase variances (DPV) was calcd. to identify regions of motion (microvasculature). En face DLOGIV image were capable of capturing the microvasculature through depth with an equal performance compared to the DPV

Optical Coherence Tomographic Angiography Imaging in Age-Related Macular Degeneration.

Optical coherence tomographic angiography (OCTA) is emerging as a rapid, noninvasive imaging modality that can provide detailed structural and flow information on retinal and choroidal vasculature. This review contains an introduction of OCTA and summarizes the studies to date on OCTA imaging in age-related macular degeneration

Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography

Spectral-domain optical coherence tomography (SD-OCT) provides volumetric images of retinal structures with unprecedented detail. Accurate segmentation algorithms and feature quantification in these images, however, are needed to realize the full potential of SD-OCT. The fully automated segmentation algorithm, FloatingCanvas, serves this purpose and performs a volumetric segmentation of retinal tissue layers in three-dimensional image volume acquired around the optic nerve head without requiring any pre-processing. The reconstructed layers are analysed to extract features such as blood vessels and retinal nerve fibre layer thickness. Findings from images obtained with the RTVue-100 SD-OCT (Optovue, Fremont, CA, USA) indicate that FloatingCanvas is computationally efficient and is robust to the noise and low contrast in the images. The FloatingCanvas segmentation demonstrated good agreement with the human manual grading. The retinal nerve fibre layer thickness maps obtained with this method are clinically realistic and highly reproducible compared with time-domain StratusOCT™