3D OCT Angiography for quantitative characterization of diabetic retinopathy

摘要 在糖尿病视网膜病变（DR）的临床诊断中，荧光素血管造影（FA）是目前唯一的方法 常规用于缺血的检测和治疗，但它是一种侵入性方法，对 视觉症状出现前的早期微血管变化。为了及早发现和管理 DR中的缺血，光学相干断层扫描血管造影（OCTA）是一种新的方法， 2015年FDA批准。与FA相比，OCTA允许神经感觉视网膜的非侵入性成像 以及视网膜血管系统，分辨率约为10 μ m。最近的一些研究已经成功地使用OCTA， 提取与DR严重程度密切相关的定量2D指标。卡沙尼博士的团队开创了 用于毛细血管形态和密度定量评估的几个2D OCTA指标。而 这些2D OCTA指标是非常有价值的，它们来自3D OCTA数据的正面投影， 因此不可避免地模糊了原始3D脉管系统网络中的几何和拓扑信息。 为了克服这一基本限制，Shi博士和Kashani将在R21项目中合作开发 用于自动分析OCTA数据的真正3D指标，并将其应用于大型DR的早期诊断 规模眼睛研究。在脑成像研究方面，南加州大学神经成像实验室（LONI）的施博士团队 开发了各种先进的计算工具，用于3D形状分析，基于普遍适用的 内在几何学的原理在这个项目中，我们将翻译和适应这些工具的3D视网膜 使用OCTA数据的脉管系统建模和分析。本项目有三个具体目标：（1）应用 在脑成像中开发的尖端计算算法，用于开发新的3D OCTA指标， 视网膜毛细血管密度和形态的基于体积和表面的定量。(2)界定你们的关系 使用我们先前发表的队列，2D和3D-OCTA指标与DR临床严重程度的可靠性 健康和糖尿病受试者。(3)验证2D和3D-OCTA指标与DR严重程度的关系 在NEI资助的非裔美国人眼病研究（AFEDS）中， 并识别与目前无法检测到的（亚临床）视网膜病变相关的OCTA指标。鉴于富人 由施博士在LONI的小组开发的计算工具和已经收集的大规模OCTA数据 （n=396）根据AFEDS研究和Kashani博士发表的研究，该项目的风险较低，但 由此产生的3D OCTA指标和相关的软件工具将是非常有价值的研究和潜在的 临床社区我们将免费提供本项目开发的所有软件工具和源代码 可通过NITRC（http：//www.example.com）和LONI网站（http：//www.loni.usc.edu/Software）获得。www.nitrc.org

Abstract In the clinical diagnosis of diabetic retinopathy (DR), fluorescein angiography (FA) is currently the only method used routinely for the detection and treatment of ischemia, but it is an invasive method and not sensitive to early microvascular changes before the onset of visual symptoms. For the early detection and management of ischemia in DR, Optical Coherence Tomography Angiography (OCTA) is a novel method that has obtained FDA approval in 2015. Compared with FA, OCTA allows non-invasive imaging of both the neurosensory retina as well as the retinal vasculature at ~10m resolution. Some recent studies have successfully used OCTA to extract quantitative 2D metrics that are well correlated with DR severity. Dr. Kashani’s group has pioneered several of the 2D OCTA metrics for the quantitative assessment of capillary morphology and density. While highly valuable, these 2D OCTA metrics were derived from the en face projection of the 3D OCTA data, and therefore inevitably obscure the geometric and topological information in the original 3D vasculature networks. To overcome this fundamental limitation, Drs. Shi and Kashani will collaborate in this R21 project to develop truly 3D metrics for the automated analysis of OCTA data and apply them for the early diagnosis of DR in large scale eye studies. For brain mapping research, Dr. Shi’s group at Laboratory of Neuro Imaging (LONI) of USC has developed various advanced computational tools for 3D shape analysis based on generally applicable principles from intrinsic geometry. In this project, we will translate and adapt these tools for 3D retinal vasculature modeling and analysis using OCTA data. There are three specific aims in this project: (1) Apply cutting-edge computational algorithms developed in brain imaging to develop novel 3D OCTA metrics for volume- and surface-based quantitation of retinal capillary density and morphology. (2) Define the relationship and reliability of 2D- and 3D-OCTA metrics with clinical severity of DR using our previously published cohorts of healthy and diabetic subjects. (3) Validate the relationship of 2D- and 3D-OCTA metrics with DR severity among a well-characterized population from the NEI funded African American Eye Diseases Study (AFEDS) and identify OCTA metrics associated with currently undetectable (sub-clinical) retinopathy. Given the rich computational tools developed by Dr. Shi’s group at LONI and the already collected, large-scale OCTA data (n=396) from the AFEDS study and Dr. Kashani’s published studies, the risk of this project is low, but the resulting 3D OCTA metrics and associated software tools will be highly valuable to the research and potentially clinical community. We will make all the software tools and source codes developed in this project freely available through the NITRC (http://www.nitrc.org) and LONI website (http://www.loni.usc.edu/Software).