Faculty Bibliography

Eye movement artifacts occurring during 3-D optical coherence tomography (OCT) scanning is a well-recognized problem that may adversely affect image analysis and interpretation. A particle filtering algorithm is presented in this paper to correct motion in a 3-D dataset by considering eye movement as a target tracking problem in a dynamic system. The proposed particle filtering algorithm is an independent 3-D alignment approach, which does not rely on any reference image. 3-D OCT data is considered as a dynamic system, while the location of each A-scan is represented by the state space. A particle set is used to approximate the probability density of the state in the dynamic system. The state of the system is updated frame by frame to detect A-scan movement. The proposed method was applied on both simulated data for objective evaluation and experimental data for subjective evaluation. The sensitivity and specificity of the x-movement detection were 98.85% and 99.43%, respectively, in the simulated data. For the experimental data (74 3-D OCT images), all the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3-D OCT volume data and correct the eye movement without using references.

PURPOSE: We compared retinal nerve fiber layer (RNFL) bias and imprecision among three spectral-domain optical coherence tomographs (SD-OCT). METHODS: A total of 152 eyes of 83 subjects (96 healthy and 56 glaucomatous eyes) underwent peripapillary RNFL imaging using at least 2 of the following 3 SD-OCT devices on the same day: Cirrus HD-OCT (optic nerve head [ONH]) cube 200 x 200 protocol), RTVue-100 (ONH protocol [12 radial lines and 13 concentric circles]), and 3D OCT-1000 (3D Scan 256 x 256 protocol). Calibration equations, bias and imprecision of RNFL measurements were calculated using structural equation models. RESULTS: The calibration equations for healthy and glaucoma RNFL thickness measurements among the 3 devices were: Cirrus = 2.136 + 0.831*RTVue; Cirrus = -15.521 + 1.056*3D OCT-1000; RTVue = -21.257 + 1.271*3D OCT-1000. Using Cirrus bias as an arbitrary reference, RTVue bias was 1.20 (95% CI 1.09-1.32, P < 0.05) times larger and 3D OCT-1000 was 0.95 (0.87-1.03, P > 0.05) times smaller. Relative to 3D OCT-1000, the RTVue bias was 1.27 (1.13-1.42, P < 0.05). RTVue imprecision (healthy eyes 7.83, 95% CI 6.43-9.58; glaucoma cases 5.71, 4.19-7.64) was statistically significantly higher than both Cirrus (healthy eyes 3.23, 2.11-4.31; glaucoma cases 3.53, 0.69-5.24) and 3D OCT-1000 (healthy eyes 4.07, 3.11-5.35; glaucoma cases 5.33, 3.77-7.67) in healthy eyes. The imprecision also was significantly higher for RTVue measurements in healthy compared to glaucomatous eyes. None of the other comparisons was statistically significant. CONCLUSIONS: RTVue-100 showed higher imprecision (or higher measurement variability) than Cirrus HD-OCT and 3D OCT-1000 RNFL measurements. Three-dimensional cube scanning with post-hoc data sampling may be a factor reducing imprecision.

Purpose. Understanding the effects of IOP on the optic nerve head (ONH) is important in understanding glaucoma and ONH structure and function. The authors tested the hypothesis that the ONH is a robust biomechanical structure wherein various factors combine to produce a relatively stable response to IOP. Methods. The authors generated two populations of 100,000 ONH numerical models each with randomly selected values, but controlled distributions, either uniform or Gaussian, of ONH geometry and mechanical properties. The authors predicted the lamina cribrosa displacement (LCD), scleral canal expansion (SCE), and the stresses (forces) and deformations (strains) produced by a 10 mm Hg increase in IOP. The authors analyzed the distributions of the responses. Results. The responses were distributed nonuniformly, with the majority of the models having a response within a small region, often less than 30% of the size of the overall response region. This concentration of responses was more marked in the Gaussian population than in the uniform population. All the responses were positively skewed. Whether a particular case was typical or not depended on the response used for classification and on whether the decision was made using one-dimensional or two-dimensional criteria. Conclusions. Despite wide variations in ONH characteristics and responses to IOP, some responses were much more common than others. This supports conceiving of the eye as a robust structure, particularly for LCD and SCE, which is tolerant to variations in tissue geometry and mechanical properties. The authors also provide the first estimates of the typical mechanical response of the ONH to variations in IOP over a large population of ONHs.

The broadening frontier of technology used in ocular imaging is continuously affecting the landscape of clinical eye care. With each wave of enhanced imaging modalities, the field faces the difficulties of optimally incorporating these devices into the clinic. Ocular imaging devices have been widely incorporated into clinical management after their diagnostic capabilities have been documented in a wide range of ocular disease. In this review, we are presenting the main commercially available devices for imaging of the posterior segment of the eye.

Optical coherence tomography captures a major role in clinical assessment in eye care. Innovative hardware and software improvements in the technology would further enhance its usefulness. In this review, we present several promising initiatives currently in development or early phase of assessment that we expect to have a future impact on optical coherence tomography.

PURPOSE: Commercial optical coherence tomography (OCT) systems use global signal quality indices to quantify scan quality. Signal quality can vary throughout a scan, contributing to local retinal nerve fibre layer segmentation errors (SegE). The purpose of this study was to develop an automated method, using local scan quality, to predict SegE. METHODS: Good-quality (global signal strength (SS) >/= 6; manufacturer specification) peripapillary circular OCT scans (fast retinal nerve fibre layer scan protocol; Stratus OCT; Carl Zeiss Meditec, Dublin, California, USA) were obtained from 6 healthy, 19 glaucoma-suspect and 43 glaucoma subjects. Scans were grouped based on SegE. Quality index (QI) values were computed for each A-scan using software of our own design. Logistic mixed-effects regression modelling was applied to evaluate SS, global mean and SD of QI, and the probability of SegE. RESULTS: The difference between local mean QI in SegE regions and No-SegE regions was -5.06 (95% CI -6.38 to 3.734) (p<0.001). Using global mean QI, QI SD and their interaction term resulted in the model of best fit (Akaike information criterion=191.8) for predicting SegE. Global mean QI >/= 20 or SS >/= 8 shows little chance for SegE. Once mean QI<20 or SS<8, the probability of SegE increases as QI SD increases. CONCLUSIONS: When combined with a signal quality parameter, the variation of signal quality between A-scans provides significant information about the quality of an OCT scan and can be used as a predictor of segmentation error.

PURPOSE: To investigate whether asymmetry in hemifield macular thickness can serve as an early indicator of glaucomatous structural damage using spectral domain optical coherence tomography. METHODS: Five zones in the macular thickness map were defined. Each zone included reciprocal areas in the superior and inferior hemifield. Differences in average retinal thickness (DRT) between corresponding regional pairs were measured in each of the five zones in 50 healthy eyes. An abnormality was defined as the DRT value lying outside the 95% confidence intervals. An eye was considered to yield an "abnormal macular hemifield test" (MHT) if abnormality was evident in any zone. The sensitivity and specificity for glaucoma detection of MHT and average circumpapillary retinal nerve fiber layer (cRNFL) classification were determined. RESULTS: A total of 114 healthy, 103 glaucoma-suspect, and 74 glaucomatous eyes were included. Overall, 5.8%, 36.9%, 88.4%, and 77.4% of the eyes of the healthy, glaucoma-suspect (GS), early glaucoma (EG), and advanced glaucoma (AG) groups yielded abnormal MHT results, respectively. In EG eyes, the sensitivity of an abnormal MHT result was significantly greater than that of abnormal average cRNFL classification (P=0.008). In the GS and AG groups, the sensitivity did not significantly differ between an abnormal MHT result and an average cRNFL classification (P=0.880, 0.180). Compared with sectoral cRNFL thickness measurements, MHT showed a similar level of diagnostic performance. Specificity was not different between an abnormal MHT result and an average cRNFL classification (P=0.687). CONCLUSIONS: Evaluation of asymmetry in hemifield macular thickness may serve as an assessment tool in the early diagnosis of glaucoma.

AIMS: To determine the retinal nerve fibre layer (RNFL) thickness at which visual field (VF) damage becomes detectable and associated with structural loss. METHODS: In a prospective cross-sectional study, 72 healthy and 40 glaucoma subjects (one eye per subject) recruited from an academic institution had VF examinations and spectral domain optical coherence tomography (SD-OCT) optic disc cube scans (Humphrey field analyser and Cirrus HD-OCT, respectively). Comparison of global mean and sectoral RNFL thicknesses with VF threshold values showed a plateau of threshold values at high RNFL thicknesses and a sharp decrease at lower RNFL thicknesses. A 'broken stick' statistical model was fitted to global and sectoral data to estimate the RNFL thickness 'tipping point' where the VF threshold values become associated with the structural measurements. The slope for the association between structure and function was computed for data above and below the tipping point. RESULTS: The mean RNFL thickness threshold for VF loss was 75.3 mum (95% CI: 68.9 to 81.8), reflecting a 17.3% RNFL thickness loss from age-matched normative value. Above the tipping point, the slope for RNFL thickness and threshold value was 0.03 dB/mum (CI: -0.02 to 0.08) and below the tipping point, it was 0.28 dB/mum (CI: 0.18 to 0.38); the difference between the slopes was statistically significant (p<0.001). A similar pattern was observed for quadrant and clock-hour analysis. CONCLUSIONS: Substantial structural loss ( approximately 17%) appears to be necessary for functional loss to be detectable using the current testing methods.

We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the associated localization loss. This S-SVM formulation directly minimizes the empirical risk of localization error, and makes efficient use of all available training data. It deals with the localization problem in a more principled way compared to the conventional binary classifier learning that uses zero-one loss and random sampling of negative examples. A total of 170 scans were collected for the experiment. Our method localized 95.1% of testing scans within the anatomical area of the foveola. Our experimental results show that the proposed method can effectively identify the location of the foveola, facilitating diagnosis around this important landmark.