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High quality visualization of the retinal microvasculature can improve our understanding of the onset and development of retinal vascular diseases, which are a major cause of visual morbidity and are increasing in prevalence. Optical Coherence Tomography Angiography (OCT-A) images are acquired over multiple seconds and are particularly susceptible to motion artifacts, which are more prevalent when imaging patients with pathology whose ability to fixate is limited. The acquisition of multiple OCT-A images sequentially can be performed for the purpose of removing motion artifact and increasing the contrast of the vascular network through averaging. Due to the motion artifacts, a robust registration pipeline is needed before feature preserving image averaging can be performed. In this report, we present a novel method for a GPU-accelerated pipeline for acquisition, processing, segmentation, and registration of multiple, sequentially acquired OCT-A images to correct for the motion artifacts in individual images for the purpose of averaging. High performance computing, blending CPU and GPU, was introduced to accelerate processing in order to provide high quality visualization of the retinal microvasculature and to enable a more accurate quantitative analysis in a clinically useful time frame. Specifically, image discontinuities caused by rapid micro-saccadic movements and image warping due to smoother reflex movements were corrected by strip-wise affine registration estimated using Scale Invariant Feature Transform (SIFT) keypoints and subsequent local similarity-based non-rigid registration. These techniques improve the image quality, increasing the value for clinical diagnosis and increasing the range of patients for whom high quality OCT-A images can be acquired.

We present a Graph Cut based image segmentation that was implemented on a Graphics Processing Unit for acceleration of processing retinal images acquired with OCT. We applied this work to generate a retinal thickness map, and for retinal layer segmentation to enhance the visualization of vasculature networks from distinct retinal capillary beds during acquisition using speckle variance OCT.

High-resolution OCT retinal imaging is important in providing visualization of various retinal structures to aid researchers in better understanding the pathogenesis of vision-robbing diseases. However, conventional optical coherence tomography (OCT) systems have a trade-off between lateral resolution and depth-of-focus. In this report, we present the development of a focus-stacking optical coherence tomography (OCT) system with automatic optimization for high-resolution, extended-focal-range clinical retinal imaging. A variable-focus liquid lens was added to correct for de-focus in real-time. A GPU-accelerated segmentation and optimization was used to provide real-time layer-specific enface visualization as well as depth-specific focus adjustment. After optimization, multiple volumes focused at different depths were acquired, registered, and stitched together to yield a single, high-resolution focus-stacked dataset. Using this system, we show high-resolution images of the ONH, from which we extracted clinically-relevant parameters such as the nerve fiber layer thickness and lamina cribrosa microarchitecture.

Retinal imaging with optical coherence tomography (OCT) has rapidly advanced in ophthalmic applications with the broad availability of Fourier domain (FD) technology in commercial systems. The high sensitivity afforded by FD-OCT has enabled imaging of the choroid, a layer of blood vessels serving the outer retina. Improved visualization of the choroid and the choroid-sclera boundary has been investigated using techniques such as enhanced depth imaging (EDI), and also with OCT systems operating in the 1060-nm wavelength range. We report on a comparison of imaging the macular choroid with commercial and prototype OCT systems, and present automated 3D segmentation of the choroid-scleral layer using a graph cut algorithm. The thickness of the choroid is an important measurement to investigate for possible correlation with severity, or possibly early diagnosis, of diseases such as age-related macular degeneration.

In this paper we present imaging and morphometric analysis of a myopic Optic Nerve Head (ONH) using an 830nm wavelength Fourier domain optical coherence tomography system. The thinner prelaminar neural tissue and shallower optic cup in the myopic subject allows visualization of the tissue structures such as anterior laminar surface and lamina cribrosa that are often challenging to image due to their depth. From these structures we measured volumetric anatomical parameters and topographical tissue thickness correlated with glaucomatous structural damage in the ONH.