1. Denoising Swept Source Optical Coherence Tomography Volumetric Scans using a Deep Learning Model

    Denoising Swept Source Optical Coherence Tomography Volumetric Scans using a Deep Learning Model

    Purpose: To evaluate the use of a deep learning (DL) noise-reduction model on swept source optical coherence tomography (SS-OCT) volumetric scans. Methods: Three groups of images including single-line highly averaged foveal scans (Averaged images), foveal B-scans from volumetric scans using no averaging (Unaveraged images) and DL denoised versions of the latter (Denoised images) were obtained. We evaluated the potential increase in signal to noise ratio by evaluating the contrast to noise ratio (CNR) of the resultant images and measured the multi scale structural similarity index (MS-SSIM) to determine if the Unaveraged and Denoised images held true in structure to the ...

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