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Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in monitoring disease progression and evaluating the effectiveness of interventions targeting cognitive decline. The association between retinal OCT biomarkers and cognitive performance has been demonstrated in several studies, and their importance in cognitive assessment is increasingly being recognized. Machine learning (ML) is a branch of artificial intelligence (AI) with an exponential number of applications in the medical field, particularly its deep learning (DL) subset, which is widely used for the analysis of medical images. These techniques efficiently deal with novel biomarkers when their outcome for the applications of interest are unclear, e.g., for the diagnosis, prognosis prediction and disease staging. However, using AI-based tools for medical purposes must be approached with caution, despite the many efforts to address the black-box nature of such approaches, especially due to the general underperformance in datasets other than those used for their development. Retinal OCT biomarkers are promising as potential indicators for decline in cognitive function. The underlying mechanisms are currently being explored to gain deeper insights into this relationship linking retinal health and cognitive function. Insights from neurovascular coupling and retinal microvascular changes play an important role. Further research is needed to establish the validity and utility of retinal OCT biomarkers as early indicators of cognitive decline and neurodegenerative diseases in routine clinical practice. Retinal OCT biomarkers could then provide a new avenue for early detection, monitoring and intervention in cognitive impairment with the potential to improve patient care and outcomes., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Robust methods to compute tissue displacements in optical coherence elastography (OCE) data are paramount, as they play a significant role in the accuracy of tissue elastic properties estimation. In this study, the accuracy of different phase estimators was evaluated on simulated OCE data, where the displacements can be accurately set, and on real data. Displacement (∆d) estimates were computed from (i) the original interferogram data (Δφori) and two phase-invariant mathematical manipulations of the interferogram: (ii) its first-order derivative (Δφd) and (iii) its integral (Δφint). We observed a dependence of the phase difference estimation accuracy on the initial depth location of the scatterer and the magnitude of the tissue displacement. However, by combining the three phase-difference estimates (Δdav), the error in phase difference estimation could be minimized. By using Δdav, the median root-mean-square error associated with displacement prediction in simulated OCE data was reduced by 85% and 70% in data with and without noise, respectively, in relation to the traditional estimate. Furthermore, a modest improvement in the minimum detectable displacement in real OCE data was also observed, particularly in data with low signal-to-noise ratios. The feasibility of using Δdav to estimate agarose phantoms' Young's modulus is illustrated.

We investigated the relationship between retinal layers and normal-appearing white matter (WM) integrity in the brain of patients with relapsing-remitting multiple sclerosis (MS), using a combined diffusion tensor imaging and high resolution optical coherence tomography approach. Fifty patients and 62 controls were recruited. The patients were divided into two groups according to presence (n = 18) or absence (n = 32) of optic neuritis. Diffusion tensor data were analyzed with a voxel-wise whole brain analysis of diffusion metrics in WM with tract-based spatial statistics. Thickness measurements were obtained for each individual retinal layer. Partial correlation and multivariate regression analyses were performed, assessing the association between individual retinal layers and diffusion metrics across all groups. Region-based analysis was performed, by focusing on tracts associated with the visual system. Receiver operating characteristic (ROC) curves were computed to compare the biomarker potential for the diagnosis of MS, using the thickness of each retinal layer and diffusion metrics. In patients without optic neuritis, both ganglion cell layer (GCL) and inner plexiform layer thickness correlated with the diffusion metrics within and outside the visual system. GCL thickness was a significant predictor of diffusion metrics in the whole WM skeleton, unlike other layers. No association was observed for either controls or patients with a history of optic neuritis. ROC analysis showed that the biomarker potential for the diagnosis of MS based on the GCL was high when compared to other layers. We conclude that GCL integrity is a predictor of whole-brain WM disruption in MS patients without optic neuritis., (© 2018 Wiley Periodicals, Inc.)

We present a methodology to assess cell level alterations on the human retina responsible for functional changes observable in the Optical Coherence Tomography data in healthy ageing and in disease conditions, in the absence of structural alterations. The methodology is based in a 3D multilayer Monte Carlo computational model of the human retina. The optical properties of each layer are obtained by solving the Maxwell's equations for 3D domains representative of small regions of those layers, using a Discontinuous Galerkin Finite Element Method (DG-FEM). Here we present the DG-FEM Maxwell 3D model and its validation against Mie's theory for spherical scatterers. We also present an application of our methodology to the assessment of cell level alterations responsible for the OCT data in Diabetic Macular Edema. It was possible to identify which alterations are responsible for the changes observed in the OCT scans of the diseased groups.

We reconstruct the three-dimensional shape and location of the retinal vascular network from commercial spectral-domain (SD) optical coherence tomography (OCT) data. The two-dimensional location of retinal vascular network on the eye fundus is obtained through support vector machines classification of properly defined fundus images from OCT data, taking advantage of the fact that on standard SD-OCT, the incident light beam is absorbed by hemoglobin, creating a shadow on the OCT signal below each perfused vessel. The depth-wise location of the vessel is obtained as the beginning of the shadow. The classification of crossovers and bifurcations within the vascular network is also addressed. We illustrate the feasibility of the method in terms of vessel caliber estimation and the accuracy of bifurcations and crossovers classification.

Two-dimensional images computed from three-dimensional optical coherence tomography (OCT) data are intrinsically aligned with it, allowing to accurately position a retinal OCT scan within the ocular fundus. In this work, we aim to compute an OCT fundus reference image with improved retinal vasculature extension and contrast over traditional approaches. Based on the shadow casted by hemoglobin on the outer layers of the retina, we compute three independent images from the OCT volumetric data (including the traditional fundus reference image). Combining these images, a fourth one is created that is able to outperform the other three, both quantitatively and qualitatively (as evaluated by retina specialists). The vascular network extension provided by this method was also compared with widely used fundus imaging modalities, showing that it is similar to that achieved with color fundus photography. In this way, the proposed method is an important starting point to the segmentation of the vascular tree and provides users with a detailed fundus reference image., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.

This work aims to improve the process of speckle noise reduction while preserving edges and other relevant features through filter expansion from 2-D to 3-D. Despeckling is very important for data visual inspection and as a preprocessing step for other algorithms, as they are usually notably influenced by speckle noise. To that intent, a 3-D approach is proposed for the adaptive complex-diffusion filter. This 3-D iterative filter was applied to spectral-domain optical coherence tomography medical imaging volumes of the human retina and a quantitative evaluation of the results was performed to allow a demonstration of the better performance of the 3-D over the 2-D filtering and to choose the best total diffusion time. In addition, we propose a fast graphical processing unit parallel implementation so that the filter can be used in a clinical setting.

The ramp preserving 2D nonlinear complex-diffusion filter introduced by Gilboa et al. (2004) was extended to 3D (Maduro et al., 2012). We propose a graphical processing unit implementation of the 3D filter for an overall faster processing in order to be used in a clinical setting. We perform a search for the best diffusion parameters (the number of iterations and spread of the diffusivity) for the 2D and 3D filters and compare their results resorting to synthetic spectral-domain optical coherence tomography volumetric data and several quantitative metrics. Execution time improvement of our implementation versus a single-core approach is also presented, showing that it allows for a full 3D volume to be processed under 7.5 seconds.

Optical coherence tomography is becoming one of the most important imaging modalities in the area of ophthalmology because of being noninvasive and by allowing to visualize the human retina structure in detail. It was recently proposed that OCT data embeds functional information from the human retina. Specifically, it was proposed that blood-retinal barrier status information is present within OCT data. Following this rationale, in this work we illustrate (based on support vector machines) the possibility to discriminate between eyes from healthy volunteers, eyes from type 2 diabetic patients with no signs of diabetic retinopathy (ETDRS level 10 eyes) and eyes diagnosed with diabetic macular edema, thus confirming the presence within OCT data of information on the BRB status.

Purpose: To demonstrate the association between changes in the blood-retinal barrier (BRB) identified by fluorescein leakage and those in the optical properties of the human retina determined by optical coherence tomography (OCT) and show how these changes can be quantified and their location identified within the retina., Methods: Two imaging techniques were applied: the retinal leakage analyzer, to map BRB function into intact or disrupted regions, and OCT, to measure refractive index changes along the light path within the human ocular fundus., Results: A total of 140 comparisons were made, 77 between areas of regions receiving the same classification (intact or disrupted BRB) and 63 between areas of regions receiving distinct classifications, from 4 pathological cases: 2 eyes with nonproliferative diabetic retinopathy and 2 eyes with wet age-related macular degeneration. In all cases, the distribution of OCT data between regions of intact and regions of disrupted BRB, identified by the retinal leakage analyzer, was quantified and was statistically significantly different (p < 0.001). In addition, it was found that the differences could be localized in the retina to specific structural sequences., Conclusions: Using a novel method to analyze OCT data, we showed that it may be possible to quantify differences in the extracellular compartment in eyes with retinal disease and alterations of the BRB. Based on quantitative techniques, our findings demonstrate the presence of indirect information on the BRB status within noninvasive OCT data., (Copyright © 2011 S. Karger AG, Basel.)

Despeckling optical coherence tomograms from the human retina is a fundamental step to a better diagnosis or as a preprocessing stage for retinal layer segmentation. Both of these applications are particularly important in monitoring the progression of retinal disorders. In this study we propose a new formulation for a well-known nonlinear complex diffusion filter. A regularization factor is now made to be dependent on data, and the process itself is now an adaptive one. Experimental results making use of synthetic data show the good performance of the proposed formulation by achieving better quantitative results and increasing computation speed.

Purpose: To describe the development of a technique that enhances spatial resolution of retinal thickness maps of the Stratus OCT (Carl Zeiss Meditec, Inc., Dublin, CA). A retinal thickness atlas (RT-atlas) template was calculated, and a macular coordinate system was established, to pursue this objective., Methods: The RT-atlas was developed from principal component analysis of retinal thickness analyzer (RTA) maps acquired from healthy volunteers. The Stratus OCT radial thickness measurements were registered on the RT-atlas, from which an improved macular thickness map was calculated. Thereafter, Stratus OCT circular scans were registered on the previously calculated map to enhance spatial resolution., Results: The developed technique was applied to Stratus OCT thickness data from healthy volunteers and from patients with diabetic retinopathy (DR) or age-related macular degeneration (AMD). Results showed that for normal, or close to normal, macular thickness maps from healthy volunteers and patients with DR, this technique can be an important aid in determining retinal thickness. Efforts are under way to improve the registration of retinal thickness data in patients with AMD., Conclusions: The developed technique enhances the evaluation of data acquired by the Stratus OCT, helping the detection of early retinal thickness abnormalities. Moreover, a normative database of retinal thickness measurements gained from this technique, as referenced to the macular coordinate system, can be created without errors induced by missed fixation and eye tilt.

Optical coherence tomography (OCT) poor mapping resolution has been pointed out as the biggest disadvantage of this technique when compared to others, e.g., retinal thickness analyzer. In this work we were able to solve this problem by developing an atlas of macular thickness of the human retina into which OCT scans were thereafter registered. This atlas is used to allow registering OCT scans from the Fast Macular Protocol, thus bringing OCT scans into the atlas coordinates, therefore correcting for misfixations, while simultaneously allowing to perform OCT inter-scan registration. From this initial registration, we were able to compute a thickness map into which Fast RNFL Protocol scans were merged, thus allowing for increased OCT mapping resolution.