Faculty Bibliography

PURPOSE: Optic nerve head (ONH) structural imaging with state-of-the-art, high-speed, ultra-high-resolution optical coherence tomography (hsUHR-OCT). DESIGN: Observational cohort study. METHODS: ONH centered 3-dimensional (94,371,840 voxel measurements in a 6- x 6- x 1.4-mm tissue volume) hsUHR-OCT data were obtained in one eye from each of six males and nine females normal healthy volunteers (40 +/- 9 years of age). The presence of structures projecting anteriorly from the disk into the vitreous was noted. RESULTS: Structures were noted in 14 of 15 (93%) examined eyes, emanating from the rim of the ONH at the nasal inferior sector, presenting as thin tissue meandering into the vitreous. CONCLUSIONS: Previous technologies provided limited visualization of ONH structures. The ability to scan the entire disk using 3-dimensional OCT (3D-OCT) in a high-density raster pattern reveals a high frequency of persistence of Cloquet's canal in the normal healthy eye.

PURPOSE: To determine the effect of corneal drying on the outcome of optical coherence tomography (OCT). DESIGN: Cohort study. PARTICIPANTS: Seventeen normal participants (mean age, 39+/-12 years). METHODS: Subjects underwent a series of peripapillary circular StratusOCT scans (version 3.0; Carl Zeiss Meditec, Inc., Dublin, CA) in a randomly selected eye. Baseline scan sets were acquired, and thereafter, blinking was prevented by taping the eyelid. Eyelid taping was immediately followed by 6 to 8 serial scan sets, each separated by 20 seconds. After removing the eyelid tape, 3 additional scans were acquired at 1, 2, and 4 minutes of blinking freely. MAIN OUTCOME MEASURES: The analyzed outcome measures were scan quality as defined by signal-to-noise ratio (SNR) and signal strength (SS) provided by the built-in OCT software and mean nerve fiber layer (NFL) thickness. RESULTS: Significant reductions in SNR, SS, and NFL were noted at each scanning point in the drying phase (for each, P<0.015, paired t test) except for NFL thickness measurements acquired at 140 and 160 seconds. The reduction in NFL thickness exceeded the 95% confidence limit of the reported reproducibility error of StratusOCT after 15 seconds of corneal drying. After 1 and 2 minutes of blinking freely, there was still a significant reduction in NFL thickness compared with the baseline value, which was no longer evident at the 4-minute scan. CONCLUSIONS: Corneal dryness affects OCT scan quality and measured NFL thickness after a short exposure time. It is recommended to instruct those who are scanned to blink frequently or to instill artificial tears.

PURPOSE: To develop an eye-motion-tracking optical coherence tomographic (OCT) method and assess its effect on image registration and nerve fiber layer (NFL) thickness measurement reproducibility. METHODS: A system capable of tracking common fundus features based on reflectance changes was integrated into a commercial OCT unit (OCT II; Carl Zeiss Meditec, Inc., Dublin, CA) and tested on healthy subjects and patients with glaucoma. Twenty successive peripapillary NFL scans were obtained with tracking and 20 without tracking, for 40 images in each session for each eye. Subjects participated in one session on three different days. Composite OCT scans and composite fundus images were generated for assessment of eye tracking. NFL thickness measurement reproducibility was also assessed. RESULTS: Seven healthy and nine glaucomatous eyes of 16 subjects were recruited. A qualitative assessment of composite OCT scans and composite fundus images showed little motion artifact or blurring along edges and blood vessels during tracking; however, those structures were less clearly defined when tracking was disengaged. There was no significant reproducibility difference with and without tracking in both intra- and intersession NFL measurement SD calculations in any location. The mean retinal pixel SD was significantly smaller with tracking than without (490.9 +/- 19.3 microm vs. 506.4 +/- 31.8 microm, P = 0.005, paired t-test). CONCLUSIONS: A retinal-tracking system was successfully developed and integrated into a commercial OCT unit. Tracking OCT improved the consistency of scan registration, but did not influence NFL thickness measurement reproducibility in this small sample study.

AIM: To create a new, automated method of evaluating the quality of optical coherence tomography (OCT) images and to compare its image quality discriminating ability with the quality assessment parameters signal to noise ratio (SNR) and signal strength (SS). METHODS: A new OCT image quality assessment parameter, quality index (QI), was created. OCT images (linear macular scan, peripapillary circular scan, and optic nerve head scan) were analysed using the latest StratusOCT system. SNR and SS were collected for each image. QI was calculated based on image histogram information using a software program of our own design. To evaluate the performance of these parameters, the results were compared with subjective three level grading (excellent, acceptable, and poor) performed by three OCT experts. RESULTS: 63 images of 21 subjects (seven each for normal, early/moderate, and advanced glaucoma) were enrolled in this study. Subjects were selected in a consecutive and retrospective fashion from our OCT imaging database. There were significant differences in SNR, SS, and QI between excellent and poor images (p = 0.04, p = 0.002, and p<0.001, respectively, Wilcoxon test) and between acceptable and poor images (p = 0.02, p<0.001, and p<0.001, respectively). Only QI showed significant difference between excellent and acceptable images (p = 0.001). Areas under the receiver operating characteristics (ROC) curve for discrimination of poor from excellent/acceptable images were 0.68 (SNR), 0.89 (IQP), and 0.99 (QI). CONCLUSION: A quality index such as QI may permit automated objective and quantitative assessment of OCT image quality that performs similarly to an expert human observer.

PURPOSE: Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. METHODS: Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] > or = -6 dB) and 20 had advanced glaucoma (MD < -6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. RESULTS: The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). CONCLUSIONS: Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality.