Image enhancement of wide-field retinal optical coherence tomography angiography by super-resolution angiogram reconstruction generative adversarial network
Image enhancement of wide-field retinal optical coherence tomography angiography by super-resolution angiogram reconstruction generative adversarial network
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DOI：
10.1016/j.bspc.2022.103957
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
X. Yuan;Yanping Huang;L. An;J. Qin;Gongpu Lan;Haixia Qiu;Bo Yu;H. Jia;S. Ren;Haishu Tan;Jingjiang Xu
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文献类型：
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作者：
X. Yuan;Yanping Huang;L. An;J. Qin;Gongpu Lan;Haixia Qiu;Bo Yu;H. Jia;S. Ren;Haishu Tan;Jingjiang Xu
宽视野视网膜光学相干断层血管成像（OCTA）在临床应用中，由于横向采样不足，图像分辨率较低。在这项研究中，我们开发了一种基于深度学习的方法，称为超分辨率血管造影重建生成对抗网络（SAR-GAN），以提高OCTA图像质量。利用自制的光谱域OCTA系统采集不同扫描方案下的视网膜血管造影数据。高分辨率3 × 3 mm 2 OCTA图像和低分辨率（LR）6 × 6 mm 2 OCTA图像用于训练网络。我们提出了一种改进的损失函数SAR-GAN的感知增强超分辨率图像的重建。利用训练好的网络对3 × 3 mm 2、6 × 6 mm 2和9 × 9 mm 2的LR OCTA图像进行了处理。定性和定量的比较表明，SAR-GAN提供了更好的视觉效果，并显着提高了图像质量的噪声强度，对比度噪声比和血管连通性。此外，与其他传统和基于深度学习的方法相比，它对具有小或大FOV的视网膜OCTA显示出上级图像增强。SAR-GAN在改善宽视野OCTA的临床评估方面具有很大的潜力。
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 theen faceOCTA 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 × 3 mm2OCTA images and low-resolution (LR) 6 × 6 mm2OCTA 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 × 3 mm2, 6 × 6 mm2and as large as 9 × 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 intensity, 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.