Gao Min, Guo Yukun, Hormel Tristan T, Sun Jiande, Hwang Thomas S, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA.
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
Biomed Opt Express. 2020 Jun 8;11(7):3585-3600. doi: 10.1364/BOE.394301. eCollection 2020 Jul 1.
Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3×3- or 6×6-mm. Compared to 3×3-mm angiograms with proper sampling density, 6×6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6×6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3×3-mm and 6×6-mm angiograms from the same eyes. The reconstructed 6×6-mm angiograms have significantly lower noise intensity, stronger contrast and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6×6-mm OCTA.
商用设备上典型的光学相干断层扫描血管造影（OCTA）采集区域为3×3毫米或6×6毫米。与具有适当采样密度的3×3毫米血管造影相比，6×6毫米血管造影的扫描质量显著较低，信噪比降低，且由于欠采样导致阴影伪影更严重。在此，我们提出一种基于深度学习的高分辨率血管造影重建网络（HARNet），以生成增强的6×6毫米浅表血管复合体（SVC）血管造影。该网络使用来自同一只眼睛的3×3毫米和6×6毫米血管造影数据进行训练。重建后的6×6毫米血管造影的噪声强度显著更低，对比度更强，血管连通性比原始图像更好。该算法在原始血管造影呈现的噪声水平下未产生假血流信号。我们算法产生的图像增强可能会改善6×6毫米OCTA的生物标志物测量和定性临床评估。
