Sparse-based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction

TL;DR

This paper introduces a novel super-resolution network that enhances low-resolution OCTA images to high-resolution, improving retinal vasculature analysis by unifying synthetic and realistic data through domain adaptation and a sparse edge-aware loss.

Contribution

The proposed SASR network employs a generative-adversarial strategy and a sparse edge-aware loss to improve OCTA image super-resolution, addressing domain differences between synthetic and real data.

Findings

Outperforms state-of-the-art super-resolution methods on OCTA datasets.

Enhances retinal vessel edge clarity and structure segmentation accuracy.

Validates effectiveness through improved quantitative and qualitative results.

Abstract

Retinal Optical Coherence Tomography Angiography (OCTA) with high-resolution is important for the quantification and analysis of retinal vasculature. However, the resolution of OCTA images is inversely proportional to the field of view at the same sampling frequency, which is not conducive to clinicians for analyzing larger vascular areas. In this paper, we propose a novel Sparse-based domain Adaptation Super-Resolution network (SASR) for the reconstruction of realistic 6x6 mm2/low-resolution (LR) OCTA images to high-resolution (HR) representations. To be more specific, we first perform a simple degradation of the 3x3 mm2/high-resolution (HR) image to obtain the synthetic LR image. An efficient registration method is then employed to register the synthetic LR with its corresponding 3x3 mm2 image region within the 6x6 mm2 image to obtain the cropped realistic LR image. We then propose a multi-level super-resolution model for the fully-supervised reconstruction of the synthetic data, guiding the reconstruction of the realistic LR images through a generative-adversarial strategy that allows the synthetic and realistic LR images to be unified in the feature domain. Finally, a novel sparse edge-aware loss is designed to dynamically optimize the vessel edge structure. Extensive experiments on two OCTA sets have shown that our method performs better than state-of-the-art super-resolution reconstruction methods. In addition, we have investigated the performance of the reconstruction results on retina structure segmentations, which further validate the effectiveness of our approach.