Optical Coherence Tomography Angiography (OCT-A) is an emerging imaging modality with which the retinal circulation can be visualized by computing the decorrelation signal on a pixel-by-pixel basis. This non-invasive, in vivo visualization of the retinal microvasculature can be instrumental in studying the onset and development of retinal vascular diseases. Quantitative measurements, such as capillary density, can be used to stratify the risk of disease progression, visual loss, and also for monitoring the course of disease. Due to projection artifact and poor contrast, it is often difficult to trace individual vessels when only one en face image is visualized. Averaging of up to 10 serially acquired OCT-A images with parallel strip-wise microsaccadic noise removal and localized nonrigid registration is presented. Additionally, the use of a deep learning method for the quantification of Foveal Avascular Zone (FAZ) parameters and perifoveal capillary density of prototype and commercial OCT-A platforms in both healthy and diabetic eyes is evaluated.
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Thesis advisor: Sarunic, Marinko
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