Advanced image processing for optical coherence tomographic angiography of macular diseases

Diabetic retinopathy (DR) is the leading cause of visual loss in working-age adults worldwide. Previous studies have found hemodynamic changes in the diabetic eyes, which precede clinically evident pathological alterations of the retinal microvasculature. There is a pressing need for new methods to allow greater understanding of these early hemodynamic changes that occur in DR. In this study, we propose a noninvasive method for the assessment of hemodynamics around the fovea (a region of the eye of paramount importance for vision). The proposed methodology combines adaptive optics scanning laser ophthalmoscopy and computational fluid dynamics modeling. We compare results obtained with this technique with in vivo measurements of blood flow based on blood cell aggregation tracking. Our results suggest that parafoveal hemodynamics, such as capillary velocity, wall shear stress, and capillary perfusion pressure can be noninvasively and reliably characterized with this method in both healthy and diabetic retinopathy patients.

Section: Discussionmentioning

confidence: 99%

Computational fluid dynamics assisted characterization of parafoveal hemodynamics in normal and diabetic eyes using adaptive optics scanning laser ophthalmoscopy

Bernabéu¹,

Lammer²,

Cai³

et al. 2016

“…The OCT processing procedures were similar to those published previously [42]. In brief, we removed the DC component of the interferometric fringe, resampled it to be linear in the k-domain and digitally compensated the remaining chromatic dispersion.…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

“…A directional graph search technique was employed to determine retinal layer boundaries [42]. This method takes advantage of two features of tissue layers: (1) they are primarily horizontal structures on B-scan images and (2) boundaries propagate assuming an anisotropic probability for neighboring voxels.…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

Angiographic and structural imaging using high axial resolution fiber-based visible-light OCT

Pi¹,

Camino²,

Zhang³

et al. 2017

Self Cite

Optical coherence tomography using visible-light sources can increase the axial resolution without the need for broader spectral bandwidth. Here, a high-resolution, fiberbased, visible-light optical coherence tomography system is built and used to image normal retina in rats and blood vessels in chicken embryo. In the rat retina, accurate segmentation of retinal layer boundaries and quantification of layer thicknesses are accomplished. Furthermore, three distinct capillary plexuses in the retina and the choriocapillaris are identified and the characteristic pattern of the nerve fiber layer thickness in rats is revealed. In the chicken embryo model, the microvascular network and a venous bifurcation are examined and the ability to identify and segment large vessel walls is demonstrated. References and links1. J. M. Schmitt, "Optical coherence tomography (OCT): a review," IEEE J. Sel. Top. Quantum Electron. 5(4), 1205-1215 (1999 457-463 (2002). 5. W. Drexler, U. Morgner, R. K. Ghanta, F. X. Kärtner, J. S. Schuman, and J. G. Fujimoto, "Ultrahigh-resolution ophthalmic optical coherence tomography," Nat. Med. 7(4), 502-507 (2001). 6. V. J. Srinivasan, D. C. Adler, Y. Chen, I. Gorczynska, R. Huber, J. S. Duker, J. S. Schuman, and J. G. Fujimoto, "Ultrahigh-speed optical coherence tomography for three-dimensional and en face imaging of the retina and optic nerve head," Invest. Ophthalmol. Vis. Sci. 49(11), 5103-5110 (2008).

“…OCTA data was calculated using the SSADA algorithm [1]. Segmentation of the vitreous and inner limiting membrane (ILM) interface, outer plexiform layer (OPL) and outer nuclear layer (ONL) interface as well as Bruch's membrane and choroid interface was automatically performed by a directional graph-search algorithm developed by Zhang et al [39]. The region contained between ILM and OPL is the vascularized inner retina.…”

Section: Data Processingmentioning

confidence: 99%

Regression-based algorithm for bulk motion subtraction in optical coherence tomography angiography

Camino¹,

Jia²,

Liu³

et al. 2017

Self Cite

Abstract:We developed an algorithm to remove decorrelation noise due to bulk motion in optical coherence tomography angiography (OCTA) of the posterior eye. In this algorithm, OCTA B-frames were divided into segments within which the bulk motion velocity could be assumed to be constant. This velocity was recovered using linear regression of decorrelation versus the logarithm of reflectance in axial lines (A-lines) identified as bulk tissue by percentile analysis. The fitting parameters were used to calculate a reflectance-adjusted upper bound threshold for bulk motion decorrelation. Below this threshold, voxels are identified as non-flow tissue, their flow values are set to zeros. Above this threshold, the voxels are identified as flow voxels and bulk motion velocity is subtracted from each using a nonlinear decorrelation-velocity relationship previously established in laboratory flow phantoms. Compared to the simpler median-subtraction method, the regression-based bulk motion subtraction improved angiogram signal-to-noise ratio, contrast, vessel density repeatability, and bulk motion noise cleanup in the foveal avascular zone, while preserving the connectivity of the vascular networks in the angiogram.