3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks.

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Diabetic retinopathy (DR) is a significant microvascular complication of diabetes mellitus and a leading cause of vision impairment in working age adults. Optical coherence tomography (OCT) is a routinely used clinical tool to observe retinal structural and thickness alterations in DR. Pathological changes that alter the normal anatomy of the retina, such as intraretinal edema, pose great challenges for conventional layer-based analysis of OCT images. We present an alternative approach for the automated analysis of OCT volumes in DR research based on nonlinear registration. In our work, we first obtain an anatomically consistent volume of interest (VOI) in different OCT images via carefully designed masking and affine registration. After that, efficient B-spline transformations are computed using stochastic gradient descent optimization. Using the OCT volumes of normal controls, for which layer-based segmentation works well, we demonstrate the accuracy of our registration-based analysis in aligning layer boundaries. By nonlinearly registering the OCT volumes of DR subjects to an atlas constructed from normal controls and measuring the Jacobian determinant of the deformation, we can simultaneously visualize tissue contraction and expansion due to DR pathology. Tensor-based morphometry (TBM) can also be performed for quantitative analysis of local structural changes. In our experimental results, we apply our method to a dataset of 105 subjects and demonstrate that volumetric OCT registration and TBM analysis can successfully detect local retinal structural alterations due to DR.

Optical Coherence Tomography Angiography (OCTA) is a novel, non-invasive imaging modality of retinal capillaries at micron resolution. While OCTA generates 3D image volumes, current analytic methods rely on 2D en face projection images for quantitative analysis. This obscures the 3D vascular geometry and prevents accurate characterization of retinal vessel networks. In this paper, we have developed an automated analysis framework that preserves the 3D geometry of OCTA data. This framework uses curvelet-based denoising, optimally oriented flux (OOF) vessel enhancement and projection artifact removal, as well as the generation of 3D vessel length from the Hamilton-Jacobi skeleton. We implement this method on a dataset of 338 OCTA scans from human subjects with diabetic retinopathy (DR) which is known to cause decrease in capillary density and compare them to healthy controls. Our results indicate that 3D vessel-skeleton-length (3D-VSL) captures differences in both superficial and deep capillary density that are not apparent in 2D vessel skeleton analyses. In statistical analysis, we show that the 3D small-vessel-skeleton-length (3D-SVSL), which is computed after the removal of the large vessels and associated projection artifacts, provides a novel metric to detect group differences between healthy controls and progressive stages of DR.This work was supported in part by NIH grants UH3NS100614, R21EY027879, U01EY025864, K08EY027006, P41EB015922, P30EY029220, Research to Prevent Blindness, and UL1TR001855 and UL1TR000130 from the National Center for Advancing Translational Science (NCATS) of the U.S. National Institutes of Health.