Three-Dimensional Retinal Microvasculature Restoration in OCT Angiography

2026-06-03

AI Summary

This study addresses the challenge of artifacts in optical coherence tomography angiography (OCTA) that severely compromise accurate quantification of retinal blood flow and non-perfused areas. Existing approaches are often limited to two-dimensional processing or fail to fully exploit three-dimensional vascular architecture. To overcome these limitations, this work proposes an end-to-end deep learning algorithm that, for the first time in OCTA, integrates 3D vascular structure by reconstructing a central B-scan from three adjacent input B-scans, thereby preserving spatial resolution while substantially enhancing microvascular fidelity. The model employs an EfficientNet-B5 encoder, a decoder augmented with spatial-channel parallel squeeze-and-excitation modules, and skip connections, trained under supervision using ground truth derived from multi-scan averaging. Experimental results demonstrate significant improvements: PSNR increased to 26.16 ± 1.26 (from 22.23 ± 0.78), SSIM reached 0.91 ± 0.02 (from 0.72 ± 0.03), and 2D and 3D Dice coefficients improved by at least 3.8% and 51.2%, respectively (both p < 0.001).

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 < 0.001) improved image quality compared with the original single OCTA volume, with a PSNR of 26.16 +/- 1.26 vs. 22.23 +/- 0.78 and an SSIM of 0.91 +/- 0.02 vs. 0.72 +/- 0.03. The proposed model also significantly (p < 0.001) improved microvascular fidelity, measured by the Dice coefficient overlap between the model output and ground truth, in both 2D and 3D by at least 3.8% and 51.2%, respectively, across several different vascular slabs.

Problem

• OCT angiography
• retinal microvasculature
• 3D vascular restoration
• imaging artifacts
• microvascular quantification

Innovation

• 3D microvasculature restoration
• OCT angiography
• deep learning
• squeeze-and-excitation
• vascular fidelity