Faculty Bibliography

PURPOSE: To describe two cases of peripapillary retinal schisis in patients with glaucoma without evidence of optic nerve pits, pseudopits, or X-linked retinoschisis. DESIGN: Two observational case reports and literature review. METHODS: Imaging of the peripapillary nerve fiber layer and schisis cavities was completed in two patients, and one patient was followed over time. RESULTS: The first patient, diagnosed with narrow angle glaucoma, was noted to have peripapillary schisis in the right eye with matching changes on visual field and optical coherence tomographic (OCT) results. Follow-up examination revealed that the schisis disappeared in the right eye while appearing in the left. The findings were verified with high-speed ultra-high-resolution OCT performed in both eyes. The second case involved a patient with anatomically narrow angles, high intraocular pressure (IOP), and peripapillary schisis extending into the macula. CONCLUSIONS: Peripapillary retinoschisis may represent a unique sequelae of intraocular fluctuations in patients with uncontrolled glaucoma. Further studies are needed to better understand this disease process.

PURPOSE: To compare the ability of the Heidelberg retina tomograph version 3 (HRT 3) and HRT version 2 (HRT 2) to discriminate between healthy and glaucomatous eyes. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: Seventy-one eyes of 71 healthy volunteers and 50 eyes of 50 glaucoma patients were studied. The average visual field mean deviation of the glaucoma group was -6.03+/-5.78 dB. INTERVENTION: All participants had comprehensive ocular examinations, perimetry, and HRT scanning within 6 months. HRT 2 data were analyzed using HRT 3 software without modifying the disc margin. MAIN OUTCOME MEASURES: Discrimination capabilities between healthy and glaucomatous eyes were determined by areas under the receiver operating characteristics (AROCs) curves. Comparisons between corresponding AROCs obtained by HRT 2 and HRT 3 analyses were performed using the nonparametric DeLong method. Agreement between classifications as defined by the different analysis methods was quantified by kappa analysis. RESULTS: The individual stereometric parameters with the best discrimination were linear cup/disc ratio (AROC = 0.897; 95% confidence interval [CI], 0.836-0.958) for standard HRT 3 analysis and horizontal retinal nerve fiber layer curvature (0.905) for HRT 3 glaucoma probability score (GPS) analysis. Areas under the receiver operating characteristics for discrimination between glaucomatous and healthy eyes of the overall classification by HRT 2 Moorfields regression analysis (MRA), HRT 3 MRA, and GPS were 0.927 (95% CI, 0.877-0.977), 0.934 (0.888-0.980), and 0.880 (0.812-0.948), respectively. The difference between the 3 AROCs was not significant (P = 0.44). The agreement between HRT 2 and HRT 3 overall MRA classification was good (kappa = 0.70; CI, 0.59-0.80) with HRT 3 tending to report more abnormalities than HRT 2 analysis. The agreement between overall HRT 3 MRA and overall GPS was kappa = 0.58 (CI, 0.45-0.70). CONCLUSIONS: The glaucoma discriminating ability of the new HRT 3 software is similar to that of the previous generation HRT 2. The GPS analysis showed promising results in differentiating between healthy and glaucomatous eyes without the need for subjective operator input.

PURPOSE: To compare visual field (VF) defects found by Swedish interactive thresholding Algorithm (SITA) perimetry and Matrix perimetry, a new VF device that utilizes frequency doubling technology in a 24-2 test pattern. DESIGN: Prospective cross-sectional study. PARTICIPANTS: Fifty eyes from 50 subjects with SITA field defects were recruited for an observational study. METHODS: Swedish Interactive Threshold Algorithm and Matrix VF testing were performed on patients from a glaucoma practice. To evaluate the learning effect on the performance of the VF, we tested subsets of each group who had previous experience with standard automated perimetry (SAP). MAIN OUTCOME MEASURES: Test duration, mean threshold, mean deviation (MD), pattern standard deviation (PSD), glaucoma hemifield test, and number of abnormal points on the pattern deviation plot were evaluated for each device. RESULTS: Test duration was significantly shorter for Matrix (SITA, 357.0+/-85.6 seconds; Matrix, 319.5+/-16.5 seconds; P = 0.0002, paired t-test). Thirty-six percent of eyes with SITA VF defects showed a normal Matrix field. In 30 of 32 eyes (94%) where both devices showed VF defects, the defects were congruent. Mean threshold value was significantly lower with Matrix compared to SITA (P<0.0001, paired t-test), as was MD (-5.34+/-5.42 dB, -4.14+/-5.29 dB, respectively; P = 0.03, paired t-test). There was no significant difference in PSD between the 2 devices (P = 0.78, paired t-test). Matrix delineated significantly smaller (P = 0.005, Wilcoxon's test) and deeper (P<0.001, Wilcoxon's test) defects than those found with SITA. Similar results were observed in the subgroups with prior SAP experience. CONCLUSIONS: The Matrix examination did not detect 36% of abnormal SITA fields. Matrix field defects were smaller and deeper than those appearing in SITA perimetry.

This paper describes a translation histogram based, hierarchical algorithm for automated three-dimensional (3-D) optic nerve head (ONH) modeling from stereoscopic ONH photographs. Recovering the depths in featureless region is still one of the problems in previous studies of 3-D ONH reconstruction. The proposed algorithm hierarchically optimized and modeled the peripheral ONH surface to solve this problem. The algorithm has various steps consisting of disparity detection, hierarchical surface modeling, weighted fusing, and depth calibration. Dual-registration algorithm is firstly applied to precisely detect the matching points which are then converted into disparities. The peripheral ONH surface is initialized and refined through hierarchical modeling and optimization from the disparities. The final 3-D ONH model is generated by fusing the modeled peripheral ONH surface and the depths measured from dual-registration together with the interpolation. The true depth is obtained after calibration of eye lens through the axial length information. The experimental results showed the proposed algorithm could successfully generate 3-D ONH model, and get good consistency with human expert in cup-to-disc (C/D) ratio evaluation. The algorithm indicates the potential usefulness for 3-D ONH modeling and evaluation.

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.