Kelly A. Townsend

Effects of Age on Optical Coherence Tomography Measurements of Healthy Retinal Nerve Fiber Layer, Macula, and Optic Nerve Head

PURPOSE To determine the effects of age on global and sectoral peripapillary retinal nerve fiber layer (RNFL), macular thicknesses, and optic nerve head (ONH) parameters in healthy subjects using optical coherence tomography (OCT). DESIGN Retrospective, cross-sectional observational study. PARTICIPANTS A total of 226 eyes from 124 healthy subjects were included. METHODS Healthy subjects were scanned using the Fast RNFL, Fast Macula, and Fast ONH scan patterns on a Stratus OCT (Carl Zeiss Meditec, Dublin, CA). All global and sectoral RNFL and macular parameters and global ONH parameters were modeled in terms of age using linear mixed effects models. Normalized slopes were also calculated by dividing the slopes by the mean value of the OCT parameter for interparameter comparison. MAIN OUTCOME MEASURES Slope of each OCT parameter across age. RESULTS All global and sectoral RNFL thickness parameters statistically significantly decreased with increasing age, except for the temporal quadrant and clock hours 8 to 10, which were not statistically different from a slope of zero. Highest absolute slopes were in the inferior and superior quadrant RNFL and clock hour 1 (superior nasal). Normalized slopes showed a similar rate in all sectors except for the temporal clock hours (8-10). All macular thickness parameters statistically significantly decreased with increasing age, except for the central fovea sector, which had a slight positive slope that was not statistically significant. The nasal outer sector had the greatest absolute slope. Normalized macular slope in the outer ring was similar to the normalized slopes in the RNFL. Normalized inner ring had shallower slope than the outer ring with a similar rate in all quadrants. Disc area remained nearly constant across the ages, but cup area increased and rim area decreased with age, both of which were statistically significant. CONCLUSIONS Global and regional changes caused by the effects of age on RNFL, macula, and ONH OCT measurements should be considered when assessing eyes over time. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.

Optical Coherence Tomography Scan Circle Location and Mean Retinal Nerve Fiber Layer Measurement Variability

PURPOSE To investigate the effect on optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness measurements of varying the standard 3.4-mm-diameter circle location. METHODS The optic nerve head (ONH) region of 17 eyes of 17 healthy subjects was imaged with high-speed, ultrahigh-resolution OCT (hsUHR-OCT; 501 x 180 axial scans covering a 6 x 6-mm area; scan time, 3.84 seconds) for a comprehensive sampling. This method allows for systematic simulation of the variable circle placement effect. RNFL thickness was measured on this three-dimensional dataset by using a custom-designed software program. RNFL thickness was resampled along a 3.4-mm-diameter circle centered on the ONH, then along 3.4-mm circles shifted horizontally (x-shift), vertically (y-shift) and diagonally up to +/-500 microm (at 100-microm intervals). Linear mixed-effects models were used to determine RNFL thickness as a function of the scan circle shift. A model for the distance between the two thickest measurements along the RNFL thickness circular profile (peak distance) was also calculated. RESULTS RNFL thickness tended to decrease with both positive and negative x- and y-shifts. The range of shifts that caused a decrease greater than the variability inherent to the commercial device was greater in both nasal and temporal quadrants than in the superior and inferior ones. The model for peak distance demonstrated that as the scan moves nasally, the RNFL peak distance increases, and as the circle moves temporally, the distance decreases. Vertical shifts had a minimal effect on peak distance. CONCLUSIONS The location of the OCT scan circle affects RNFL thickness measurements. Accurate registration of OCT scans is essential for measurement reproducibility and longitudinal examination (ClinicalTrials.gov number, NCT00286637).

Imaging of the retinal nerve fibre layer for glaucoma.

Background: Glaucoma is a group of diseases characterised by retinal ganglion cell dysfunction and death. Detection of glaucoma and its progression are based on identification of abnormalities or changes in the optic nerve head (ONH) or the retinal nerve fibre layer (RNFL), either functional or structural. This review will focus on the identification of structural abnormalities in the RNFL associated with glaucoma. Discussion: A variety of new techniques have been created and developed to move beyond photography, which generally requires subjective interpretation, to quantitative retinal imaging to measure RNFL loss. Scanning laser polarimetry uses polarised light to measure the RNFL birefringence to estimate tissue thickness. Optical coherence tomography (OCT) uses low-coherence light to create high-resolution tomographic images of the retina from backscattered light in order to measure the tissue thickness of the retinal layers and intraretinal structures. Segmentation algorithms are used to measure the thickness of the retinal nerve fibre layer directly from the OCT images. In addition to these clinically available technologies, new techniques are in the research stages. Polarisation-sensitive OCT has been developed that combines the strengths of scanning laser polarimetry with those of OCT. Ultra-fast techniques for OCT have been created for research devices. The continued utilisation of imaging devices into the clinic is refining glaucoma assessment. In the past 20 years glaucoma has gone from a disease diagnosed and followed using highly subjective techniques to one measured quantitatively and increasingly objectively.

Spectral oximetry assessed with high-speed ultra-high- resolution optical coherence tomography

We use Fourier domain optical coherence tomography (OCT) data to assess retinal blood oxygen saturation. Three-dimensional disk-centered retinal tissue volumes were assessed in 17 normal healthy subjects. After removing DC and low-frequency a-scan components, an OCT fundus image was created by integrating total reflectance into a single reflectance value. Thirty fringe patterns were sampled; 10 each from the edge of an artery, adjacent tissue, and the edge of a vein, respectively. A-scans were recalculated, zeroing the DC term in the power spectrum, and used for analysis. Optical density ratios (ODRs) were calculated as ODR(Art)=ln(Tissue(855)Art(855))ln(Tissue(805)Art(805)) and ODR(Vein)=ln(Tissue(855)Vein(855))ln(Tissue(805)Vein(805)) with Tissue, Art, and Vein representing total a-scan reflectance at the 805- or 855-nm centered bandwidth. Arterial and venous ODRs were compared by the Wilcoxon signed rank test. Arterial ODRs were significantly greater than venous ODRs (1.007+/-2.611 and -1.434+/-4.310, respectively; p=0.0217) (mean+/-standard deviation). A difference between arterial and venous blood saturation was detected. This suggests that retinal oximetry may possibly be added as a metabolic measurement in structural imaging devices.

Automated Assessment of the Optic Nerve Head on Stereo Disc Photographs

PURPOSE To develop automated software for optic nerve head (ONH) quantitative assessment from stereoscopic disc photographs and to evaluate its performance in comparison with human expert assessment. METHODS A fully automated system, including three-dimensional ONH modeling, disc margin detection, cup margin detection, and calculation of stereometric ONH parameters, was developed and tested. One eye each from 54 subjects (23 healthy, 17 suspected glaucoma, and 14 glaucoma) was enrolled. The majority opinion of three experts defined disc and cup margins on the disc photographs was used for comparison. Seven ONH parameters, disc area, rim area, rim volume, cup area, cup volume, cup-to-disc (C/D) area ratio, and vertical C/D ratio, were computed based on both machine- and expert-defined margins and compared between the methods. RESULTS All automated ONH measurements showed good correlation with the expert defined margins (Pearson r = 0.90, disc area; 0.56, rim area; 0.78, rim volume; 0.88, cup area; 0.93, cup volume; 0.69, C/D area ratio; and 0.67, vertical C/D ratio; all P <or= 0.0001). No statistically significant difference was found in the glaucoma-discriminating ability of all seven ONH parameters (P >or= 0.21). The mean or median of automatically defined disc and cup areas was significantly higher than the subjective assessment (disc area P = 0.0001, t-test; cup area P = 0.036, Wilcoxon signed ranks test), although they had high correlation coefficients. The software failed to detect the disc margin for all the disc photographs with peripapillary atrophy. CONCLUSIONS The automated ONH analysis method provides an objective and quantitative ONH evaluation using widely available stereo disc photographs.

Scan quality effect on glaucoma discrimination by glaucoma imaging devices

Aim: To evaluate, within ocular imaging scans of acceptable quality as determined by manufacturers’ guidelines, the effects of image quality on glaucoma discrimination capabilities. Methods: One hundred and four healthy and 75 glaucomatous eyes from the Advanced Imaging in Glaucoma Study (AIGS) were imaged with GDx-VCC, HRT II and StratusOCT. Quality score (QS⩾8), pixel standard deviation (SD⩽50) and signal strength (SS⩾5) were used as quality parameter cut-offs, respectively. GDx nerve fibre indicator (NFI) and HRT Moorfields regression analysis (MRA) classifications and OCT mean retinal nerve fibre layer (RNFL) thickness were used as the discriminatory parameters. Logistic regression models were used to model the dichotomous clinical classification (healthy vs glaucoma) as a function of image-quality parameters and discriminatory parameters. Results: Quality parameter covariates were statistically non-significant for GDx and HRT but had an inverse effect on OCT in predicting disease (a higher SS had a lower probability of glaucoma). Age was a significant covariate for GDx and HRT, but not OCT, while ethnicity and interaction between the image quality and the institute where scans were acquired were significant covariates in the OCT models. Conclusion: Scan quality within the range recommended as acceptable by the manufacturer of each imaging device does not affect the glaucoma discriminating ability of GDx or HRT but does affect Stratus OCT glaucoma discrimination.

Clinical application of MRI in ophthalmology.

MRI has long been applied to clinical medical and neurological cases for the structural assessment of tissues as well as their physiological and functional needs and processes. These uses are at a variety of developmental stages in ophthalmology, from common use of clinical structural assessment for neuro‐ophthalmology and evaluation of space‐occupying lesions to the beginning stages of experimentally measuring functional activation of specific layers within the retina and measurement of physiological oxygen responses. New MRI methodologies, such as the use of orbital coils and Gd‐DTPA image enhancement, have been researched, developed, and validated in the eye, opening new possibilities for this technology to enter the clinic. This review aims to summarize the clinical ophthalmological uses of MRI, focusing on the current use of the technology and future applications. Copyright

Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

Aims: To assess performance of classifiers trained on Heidelberg Retina Tomograph 3 (HRT3) parameters for discriminating between healthy and glaucomatous eyes. Methods: Classifiers were trained using HRT3 parameters from 60 healthy subjects and 140 glaucomatous subjects. The classifiers were trained on all 95 variables and smaller sets created with backward elimination. Seven types of classifiers, including Support Vector Machines with radial basis (SVM-radial), and Recursive Partitioning and Regression Trees (RPART), were trained on the parameters. The area under the ROC curve (AUC) was calculated for classifiers, individual parameters and HRT3 glaucoma probability scores (GPS). Classifier AUCs and leave-one-out accuracy were compared with the highest individual parameter and GPS AUCs and accuracies. Results: The highest AUC and accuracy for an individual parameter were 0.848 and 0.79, for vertical cup/disc ratio (vC/D). For GPS, global GPS performed best with AUC 0.829 and accuracy 0.78. SVM-radial with all parameters showed significant improvement over global GPS and vC/D with AUC 0.916 and accuracy 0.85. RPART with all parameters provided significant improvement over global GPS with AUC 0.899 and significant improvement over global GPS and vC/D with accuracy 0.875. Conclusions: Machine learning classifiers of HRT3 data provide significant enhancement over current methods for detection of glaucoma.

Three-Dimensional Optical Coherence Tomography (3D-OCT) Image Enhancement with Segmentation-Free Contour Modeling C-Mode

PURPOSE To develop a semiautomated method to visualize structures of interest (SoIs) along their contour within three-dimensional, spectral domain optical coherence tomography (3D SD-OCT) data, without the need for segmentation. METHODS With the use of two SD-OCT devices, the authors obtained 3D SD-OCT data within 6 x 6 x 1.4-mm and 6 x 6 x 2-mm volumes, respectively, centered on the fovea in healthy eyes and in eyes with retinal pathology. C-mode images were generated by sampling a variable thickness plane semiautomatically modeled to fit the contour of the SoI. Unlike published and commercialized methods, this method did not require retinal layer segmentation, which is known to fail frequently in the presence of retinal pathology. Four SoIs were visualized for healthy eyes: striation of retinal nerve fiber (RNF), retinal capillary network (RCN), choroidal capillary network (CCN), and major choroidal vasculature (CV). Various SoIs were visualized for eyes with retinal pathology. RESULTS Seven healthy eyes and seven eyes with retinal pathology (cystoid macular edema, central serous retinopathy, vitreoretinal traction, and age-related macular degeneration) were imaged. CCN and CV were successfully visualized in all eyes, whereas RNF and RCN were visualized in all healthy eyes and in 42.8% of eyes with pathologies. Various SoIs were successfully visualized in all eyes with retinal pathology. CONCLUSIONS The proposed C-mode contour modeling may provide clinically useful images of SoIs even in eyes with severe pathologic changes in which segmentation algorithms fail.

Assessing the Relationship Between Central Corneal Thickness and Retinal Nerve Fiber Layer Thickness in Healthy Subjects

PURPOSE To determine the relationship between central corneal thickness (CCT) and retinal nerve fiber layer (RNFL) thickness obtained by scanning laser polarimetry (GDx-VCC; Carl Zeiss Meditec, Dublin, California, USA), confocal scanning laser ophthalmoscopy (HRT II; Heidelberg Engineering, Heidelberg, Germany), and optical coherence tomography (Stratus OCT; Carl Zeiss Meditec). DESIGN Multicenter clinical trial, retrospective cross-sectional study. METHODS One hundred and nine healthy subjects from the Advanced Imaging in Glaucoma Study were enrolled in this study. All subjects had a standard clinical examination, including visual field (VF) and good-quality scans from all three imaging devices. CCT was measured using an ultrasonic pachymeter. A linear mixed-effects model was used to assess the relationship between RNFL thickness and CCT, accounting for clustering of eyes within subjects, testing site, ethnicity, family history of glaucoma, axial length intraocular pressure, and VF global indices. RESULTS For OCT and GDx, there was a slight nonstatistically significant positive relationship between CCT and RNFL thickness. For HRT, there was a slight nonstatistically significant negative relationship between CCT and RNFL thickness. Relationships for each device were found to differ between sites. CONCLUSIONS CCT was not statistically significantly related to RNFL thickness in healthy eyes.