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Optical coherence tomography angiography (OCTA) has become an essential tool in clinics for structural and functional microvasculature imaging. However, a primary setback for OCTA is its imaging speed. The current protocols require high sampling density from raster scanning and multiple cross-sectional B-scan acquisitions to form a single image frame, limiting the acquisition speed. Although advanced ultrafast imaging systems have been proposed, extensive hardware adjustments are cost-prohibitive and pose limitations for practical implementations. Herein, we present an integrated deep learning (DL) method to simultaneously tackle the sampling density and the B-scan repetition process, thus improving the imaging speed while preserving quality. We designed an end-to-end deep neural network (DNN) framework with a two-staged adversarial training scheme to reconstruct fully sampled, high quality (8 repeated B-scans) angiograms from their corresponding undersampled, low quality (2 repeated B-scans) counterparts by successively enhancing the pixel resolution and the image quality. We evaluate our proposed framework using an in-vivo mouse brain vasculature dataset and demonstrate that our method can enhance the OCTA acquisition speed while achieving superior reconstruction performance than conventional methods. Our DL-based framework can accelerate the OCTA imaging speed from 16 to 256× while preserving the image quality and thus provides a convenient software-only solution to aid preclinical and clinical studies.
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