Diagnosis and Monitoring of Glaucoma with Optical Coherence Tomography Angiography

项目摘要 光学相干层析血管成像(OCTA)是一种非侵入性的三维成像方法 可视化和量化整个视网膜的微血管形成。这项拟议的研究评估了这种药物的临床实用性。 OCTA测量与标准视神经头(ONH)、视网膜结构测量的比较 使用当前临床成像标准光谱域测量神经纤维层(RNFL)和黄斑 光学相干断层扫描(OCT)预测和检测青光眼可疑患者和 青光眼患者。我们和其他人的研究表明，视网膜浅血管密度 (由血管组成的测量面积的比例)ONH区和黄斑在开放时密度较低 角型青光眼(OAG)的患眼明显多于健康眼。此外，随着诊断准确率的提高，诊断准确率也随之提高 疾病的严重性。我们实验室的研究表明，血管密度的诊断准确性是 与OCT测量RNFL厚度相似，且视网膜区域的血管密度降低 与局限性视野(VF)缺陷相关。这些横断面结果强烈表明，OCTA 测量结果反映了与OAG的病理生理学相关的组织损伤。在一项纵向研究中， OAG患者黄斑血管密度的平均变化速度明显快于 青光眼可疑或眼睛健康。最后，最近的结果表明，浅层机器学习分析 与OCTA测量相比，OCTA测量的组合可以改善健康和青光眼的分类 标准仪器测量以及OCTA面部图像的深度学习分析。目前的研究 提供了一个独特的机会来扩展已从480收集的纵向OCTA数据 对不同种族的人进行长达10年的观察，以调查健康、 青光眼可疑和OAG眼，并将其与其他成像方式进行比较。本研究的目的有：1) 为了加深我们对健康和健康人群OCTA和OCT测量随时间变化的了解 更准确地识别与疾病相关的真实变化和2)提高我们对 应用基于统计和深度学习的多因素分析研究青光眼进展的风险 模式OCTA和OCT测量。建议的研究将加强我们对年龄和 青光眼相关血管密度和视网膜组织厚度的变化使我们能够预测和检测 更准确地改变，从而可能减慢进展速度，从而降低 与视力相关的生活质量下降，包括失明。

Project Summary Optical coherence tomography angiography (OCTA) is a non-invasive, 3-dimensional imaging method to visualize and quantify microvasculature throughout the retina. The proposed study evaluates the clinical utility of OCTA measurements compared to standard structural measurements of the optic nerve head (ONH), retinal nerve fiber layer (RNFL) and macula measured using the current clinical imaging standard, spectral domain optical coherence tomography (OCT) to predict and detect progression of disease in glaucoma suspects and glaucoma patients. Our research, and that of others, has shown that superficial retinal vessel density (proportion of measured area composed of blood vessels) in the ONH region and macula is less dense in open angle glaucoma (OAG) eyes than in healthy eyes. Moreover, diagnostic accuracy is improved with increasing disease severity. Research from our laboratory suggests that the diagnostic accuracy of vessel density is similar to that of OCT-measured RNFL thickness, and that vessel density is reduced in retinal regions associated with localized visual field (VF) defects. These cross-sectional results strongly suggest that OCTA measurements reflect damage to tissues relevant to the pathophysiology of OAG. In a longitudinal study, the mean rate of change in macula vessel density was shown to be significantly faster in OAG eyes than in glaucoma suspect or healthy eyes. Finally, recent results indicate that shallow machine learning analyses of combinations of OCTA measurements can improve classification of healthy and glaucoma eyes compared to standard instrument measurements as can deep learning analyses of OCTA enface images. The current study provides a unique opportunity to extend the longitudinal OCTA data that has already been collected from 480 eyes racially diverse individuals for up to 10 years to investigate vessel density change over time in healthy, glaucoma suspect and OAG eyes, and to compare it to other imaging modalities. The aims of this study are 1) to improve our understandingof the relative change over time of OCTA and OCT measurements in healthy and diseased eyes to identify true disease-related change more accurately and 2) to improve our understanding of the risk of developing glaucomatous progression using statistical and deep learning-based analyses of multi- modal OCTA and OCT measurements. The proposed studies will enhance our understanding of age and glaucoma-related change in vessel density and retinal tissue thickness allowing us to predict and detect change more accurately, thus possibly slowing the rate of progression and consequently reducing the risk of reduction of vision related quality of life, including blindness.