In this study, we developed voxel-wise projection-resolved optical coherence tomographic angiography (PR-OCTA) using artificial intelligence (AI). Two different artificial intelligence models were developed, including a pure convolutional neural network (CNN) model and a CNN and recurrent neural network (RNN) hybrid model. Compared with the state-of-art rules-based model, the AI models were able to preserve more in-situ blood flow and suppress projection artifacts and background noise.
We propose a three-dimensional (3-D) registration method to correct motion artifacts and construct the volume structure for angiographic and structural optical coherence tomography (OCT). This algorithm is particularly suitable for the nonorthogonal wide-field OCT scan acquired by a ultrahigh-speed swept-source system (>200 kHz A-scan rate). First, the transverse motion artifacts are corrected by the between-frame registration based on en face OCT angiography (OCTA). After A-scan transverse translation between B-frames, the axial motions are corrected based on the rebuilt boundary of inner limiting membrane. Finally, a within-frame registration is performed for local optimization based on cross-sectional OCTA. We evaluated this algorithm on retinal volumes of six normal subjects. The results showed significantly improved retinal smoothness in 3-D-registered structural OCT and image contrast on en face OCTA.
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