Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography
A deep learning method restores capillary anatomy from a single OCTA volume, significantly improving image quality and addressing 3D vascular architecture for the first time.
[2606.05375] Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography
[Submitted on 3 Jun 2026]
Title:Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography
View a PDF of the paper titled Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography, by Yukun Guo and 5 other authors
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Abstract:Optical coherence tomographic angiography (OCTA) is a powerful technique for imaging retinal microvasculature. However, acquiring reliable quantification of retinal blood flow and areas of retinal nonperfusion is challenging because of imaging artifacts. Existing methods primarily focus on noise suppression, projection artifact removal, or signal enhancement to improve the image quality of OCTA in cross-sectional or two-dimensional (2D) en face projections, while neglecting the intrinsic three-dimensional vascular architecture. In this study, we propose a deep learning-based algorithm for restoring capillary anatomical vasculature from a single OCTA volume. The network consists of an EfficientNet-B5 encoder and a decoder incorporating concurrent spatial and channel squeeze-and-excitation modules, connected via skip connections to preserve spatial resolution. Three adjacent B-frames are used as input to predict the restored middle B-frame. We evaluated the performance of the model using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) against ground truth generated from averaging multiple scans. The results show that the proposed model significantly (both p
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