In ophthalmology, Optical Coherence Tomography (OCT) is becoming one of the dominant imaging technologies for both clinical diagnostics and vision research. In a previous bachelor’s thesis prior to this project, we described a highly optimized Graphics Processing Unit (GPU) implementation of the Fourier Domain (FD)OCT processing and visualization pipeline that was capable of real-time volume rendering at video rate.In this thesis, we describe three applications that build upon the GPU-based processing software: Speckle Variance (sv)OCT, Compressive Sample (CS)OCT, and Wavefront Sensorless Adaptive Optics (WSAO)OCT. We first demonstrate that svOCT can be a powerful fundus imaging technique for visualizing the retinal vasculature network, which may be comparable to the gold-standard technique called Fluorescein Angiography (FA). The strongest attribute of svOCT is that it bypasses the use of intravenous fluorescein for vascular contrast enhancement, and can be highly suitable for longitudinal monitoring of patients with vasculature-related pathologies in the retina. In our second application, we present a GPU-accelerated CS-OCT processing pipeline. The principle behind CS-OCT is to take advantage of the redundancy in biological structures, such as the retina, for reconstructing volumes acquired at a sampling density below the Nyquist criterion; the purpose is to justify decreasing volume acquisition time by significantly subsampling the datasets. In our final application, we present a WSAO-OCT system as a novel technique for cellular resolution imaging of the human photoreceptor layer. This technique leverages on the ultrahigh speed processing rates in our GPU-based processing software in order to produce a real-time intensity-based merit function for en face image quality optimization.
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Thesis advisor: Sarunic, Marinko V.
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