Image enhancement of wide-field retinal optical coherence tomography angiography by super-resolution angiogram reconstruction generative adversarial network

作者：Yuan, Xing; Huang, Yanping; An, Lin; Qin, Jia; Lan, Gongpu; Qiu, Haixia; Yu, Bo; Jia, Haibo; Ren, Shangjie; Tan, Haishu; Xu, Jingjiang^*

来源：Biomedical Signal Processing and Control, 2022, 78: 103957.

DOI：10.1016/j.bspc.2022.103957

• Optical coherence tomography
• Optical coherence tomography angiography
• Image Processing
• Image Enhancement
• Medical and biological imaging

Wide-field retinal optical coherence tomography angiography (OCTA) usually suffers from low image resolution in clinical practice because of insufficient lateral sampling. In this study, we develop a deep-learning-based method named super-resolution angiogram reconstruction generative adversarial network (SAR-GAN) to enhance the en face OCTA image quality. A sophisticated home-made spectral-domain OCTA system is employed to capture the data of retinal angiograms with different scanning protocols. High-resolution 3 x 3 mm2 OCTA images and low-resolution (LR) 6 x 6 mm2 OCTA images are utilised in training the network. We propose an improved loss function for SAR-GAN for the reconstruction of perceptually enhanced super-resolution images. The well-trained network is utilized to processing the LR OCTA images with a field of view (FOV) of 3 x 3 mm2, 6 x 6 mm(2) and as large as 9 x 9 mm2. The qualitative and quantitative comparisons show that SAR-GAN provides perceptually better visualization and significantly enhances the image quality in terms of noise in-tensity, contrast-to-noise ratio and vessel connectivity. Moreover, it demonstrates superior image enhancement for retinal OCTA with small or large FOVs, compared with other traditional and deep-learning based methods. The SAR-GAN has great potential to improve the clinical assessment by wide-field OCTA.

• 单位
  1; 哈尔滨医科大学; 佛山科学技术学院; 天津大学