Faculty Bibliography

Three-dimensional optical coherence tomography (OCT) is a new ophthalmic imaging technique offering more detailed quantitative analysis of the retinal structure. Eye movement during 3D OCT scanning, however, creates significant spatial distortions that may adversely affect image interpretation and analysis. Current software solutions must use additional reference images or B-scans to correct eye movement in a certain direction. The proposed particle filtering algorithm is an independent 3D alignment approach, which does not rely on any reference image. 3D OCT data is considered as a dynamic system, while location of A-scan is represented by the state space. A particle set is generated to approximate the probability density of the state. The state of the system is updated frame by frame to detect A-scan movement. Seventy-four 3D OCT images with eye movement were tested and subjectively evaluated by comparing them with the original 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 3D OCT volume data and correct the eye movement without using references.

We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular hole, macular edema, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local descriptors are dimension-reduced Local Binary Pattern histograms, which are capable of encoding texture information from OCT images of the retina. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class Support Vector Machine classifiers to identify the presence of normal macula and each of the three pathologies. We conducted extensive experiments on a large dataset consisting of 326 OCT scans from 136 patients. The results show that the proposed method is very effective.

We have developed a new image-based guidance system for microsurgery using optical coherence tomography (OCT), which presents a virtual image in its correct location inside the scanned tissue. Applications include surgery of the cornea, skin, and other surfaces below which shallow targets may advantageously be displayed for the naked eye or low-power magnification by a surgical microscope or loupes (magnifying eyewear). OCT provides real-time high-resolution (3 micron) images at video rates within a two or more millimeter axial range in soft tissue, and is therefore suitable for guidance to various shallow targets such as Schlemm's canal in the eye (for treating Glaucoma) or skin tumors. A series of prototypes of the "OCT penlight" have produced virtual images with sufficient resolution and intensity to be useful under magnification, while the geometrical arrangement between the OCT scanner and display optics (including a half-silvered mirror) permits sufficient surgical access. The two prototypes constructed thus far have used, respectively, a miniature organic light emitting diode (OLED) display and a reflective liquid crystal on silicon (LCoS) display. The OLED has the advantage of relative simplicity, satisfactory resolution (15 micron), and color capability, whereas the LCoS can produce an image with much higher intensity and superior resolution (12 micron), although it is monochromatic and more complicated optically. Intensity is a crucial limiting factor, since light flux is greatly diminished with increasing magnification, thus favoring the LCoS as the more practical system.

PURPOSE: To map ganglion cell complex (GCC) thickness with high-speed Fourier-domain optical coherence tomography (FD-OCT) and compute novel macular parameters for glaucoma diagnosis. DESIGN: Observational, cross-sectional study. PARTICIPANTS: One hundred seventy-eight participants in the Advanced Imaging for Glaucoma Study, divided into 3 groups: 65 persons in the normal group, 78 in the perimetric glaucoma group (PG), and 52 in the preperimetric glaucoma group (PPG). METHODS: The RTVue FD-OCT system was used to map the macula over a 7 x 6 mm region. The macular OCT images were exported for automatic segmentation using software we developed. The program measured macular retinal (MR) thickness and GCC thickness. The GCC was defined as the combination of nerve fiber, ganglion cell, and inner plexiform layers. Pattern analysis was applied to the GCC map and the diagnostic powers of pattern-based diagnostic parameters were investigated. Results were compared with time-domain (TD) Stratus OCT measurements of MR and circumpapillary nerve fiber layer (NFL) thickness. MAIN OUTCOME MEASURES: Repeatability was assessed by intraclass correlation, pooled standard deviation, and coefficient of variation. Diagnostic power was assessed by the area under the receiver operator characteristic (AROC) curve. Measurements in the PG group were the primary measures of performance. RESULTS: The FD-OCT measurements of MR and GCC averages had significantly better repeatability than TD-OCT measurements of MR and NFL averages. The FD-OCT GCC average had significantly (P = 0.02) higher diagnostic power (AROC = 0.90) than MR (AROC = 0.85 for both FD-OCT and TD-OCT) in differentiating between PG and normal. One GCC pattern parameter, global loss volume, had significantly higher AROC (0.92) than the overall average (P = 0.01). The diagnostic powers of the best GCC parameters were statistically equal to TD-OCT NFL average. CONCLUSIONS: The higher speed and resolution of FD-OCT improved the repeatability of macular imaging compared with standard TD-OCT. Ganglion cell mapping and pattern analysis improved diagnostic power. The improved diagnostic power of macular GCC imaging is on par with, and complementary to, peripapillary NFL imaging. Macular imaging with FD-OCT is a useful method for glaucoma diagnosis and has potential for tracking glaucoma progression.

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/=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.

BACKGROUND/AIMS: To investigate retinal nerve fibre layer (RNFL) thickness measurement reproducibility using conventional time-domain optical coherence tomography (TD-OCT) and spectral-domain OCT (SD-OCT), and to evaluate two methods defining the optic nerve head (ONH) centring: Centred Each Time (CET) vs Centred Once (CO), in terms of RNFL thickness measurement variability on SD-OCT. METHODS: Twenty-seven eyes (14 healthy subjects) had three circumpapillary scans with TD-OCT and three raster scans (three-dimensional or 3D image data) around ONH with SD-OCT. SD-OCT images were analysed in two ways: (1) CET: ONH centre was defined on each image separately and (2) CO: ONH centre was defined on one image and exported to other images after scan registration. After defining the ONH centre, a 3.4 mm diameter virtual circular OCT was resampled on SD-OCT images to mimic the conventional circumpapillary RNFL thickness measurements taken with TD-OCT. RESULTS: CET and CO showed statistically significantly better reproducibility than TD-OCT except for 11:00 with CET. CET and CO methods showed similar reproducibility. CONCLUSIONS: SD-OCT 3D cube data generally showed better RNFL measurement reproducibility than TD-OCT. The choice of ONH centring methods did not affect RNFL measurement reproducibility.