Faculty Bibliography

Optical coherence tomography (OCT) images of the retina are a powerful tool for diagnosing and monitoring eye disease. However, they are plagued by speckle noise, which reduces image quality and reliability of assessment. This paper introduces a novel speckle reduction method inspired by the recent successes of deep learning in medical imaging. We present two versions of the network to reflect the needs and preferences of different end-users. Specifically, we train a convolution neural network to denoise cross-sections from OCT volumes of healthy eyes using either (1) mean-squared error, or (2) a generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. We then interrogate the success of both methods with extensive quantitative and qualitative metrics on cross-sections from both healthy and glaucomatous eyes. The results show that the former approach provides state-of-the-art improvement in quantitative metrics such as PSNR and SSIM, and aids layer segmentation. However, the latter approach, which puts more weight on visual perception, outperformed for qualitative comparisons based on accuracy, clarity, and personal preference. Overall, our results demonstrate the effectiveness and efficiency of a deep learning approach to denoising OCT images, while maintaining subtle details in the images.

PURPOSE/OBJECTIVE:To evaluate the ability of OCT optic nerve head (ONH) and macular parameters to detect disease progression in eyes with advanced structural glaucomatous damage of the circumpapillary retinal nerve fiber layer (cRNFL). DESIGN/METHODS:Longitudinal study. PARTICIPANTS/METHODS:Forty-four eyes from 37 patients with advanced average cRNFL damage (≤60 μm) followed up for an average of 4.0 years. METHODS:All patients were examined with spectral-domain OCT and visual field (VF) assessment during at least 4 visits. MAIN OUTCOME MEASUREMENTS/METHODS:Visual field mean deviation (MD) and VF index. OCT cRNFL (average, superior, and inferior quadrants), ganglion cell-inner plexiform layer (GCIPL) (average, superior, and inferior), rim area, cup volume, average cup-to-disc (C:D) ratio, and vertical C:D ratio. RESULTS:/year). CONCLUSIONS:Macula GCIPL and ONH parameters may be useful in tracking progression in patients with advanced glaucoma.

Glaucoma is the second leading cause of blindness world-wide. Despite active research efforts driven by the importance of diagnosis and treatment of the optic degenerative neuropathy, the relationship between structural and functional changes along the glaucomateous evolution are still not clearly understood. Dynamic changes of the lamina cribrosa (LC) in the presence of intraocular pressure (IOP) were suggested to play a significant role in optic nerve damage, which motivates the proposed research to explore the relationship of changes of the 3D structure of the LC collagen meshwork to clinical diagnosis. We introduce a framework to quantify 3D dynamic morphological changes of the LC under acute IOP changes in a series of swept-source optical coherence tomography (SS-OCT) scans taken under different pressure states. Analysis of SS-OCT images faces challenges due to low signal-to-noise ratio, anisotropic resolution, and observation variability caused by subject and ocular motions. We adapt unbiased diffeomorphic atlas building which serves multiple purposes critical for this analysis. Analysis of deformation fields yields desired global and local information on pressure-induced geometric changes. Deformation variability, estimated with repeated images of a healthy volunteer without IOP elevation, is found to be a magnitude smaller than pressure-induced changes and thus illustrates feasibility of the proposed framework. Results in a clinical study with healthy, glaucoma suspect, and glaucoma subjects demonstrate the potential of the proposed method for non-invasive in vivo analysis of LC dynamics, potentially leading to early prediction and diagnosis of glaucoma.