OCT and OCTA image processing for retinal assessment of people with MS

用于多发性硬化症患者视网膜评估的 OCT 和 OCTA 图像处理

• 批准号: 10357873
• 负责人: Jerry L Prince
• 金额: $ 44.1万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10357873
• 关键词: Address; Affect; Algorithmic Analysis; Algorithms; Anatomy; Angiography; Bayesian Modeling; Behavior; Biological; Biological Markers; Central Nervous System Diseases; Clinic; Clinical; Communities; Computational algorithm; Computer software; Computers; Data; Development; Diagnosis; Disease; Disease Management; Disease Progression; Endothelium; Equilibrium; Esthesia; Evaluation; Event; Eye; Eye diseases; Failure; Foundations; Four-dimensional; Future; Grant; Graph; Image; Imaging Device; Individual; Language; Learning; Literature; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Methodology; Methods; Modeling; Monitor; Morphologic artifacts; Multiple Sclerosis; Muscular Atrophy; Neurodegenerative Disorders; Optical Coherence Tomography; Persons; Play; Process; Relapse; Research; Retina; Role; Sampling; Scanning; Severities; Source; Spinal Cord Lesions; Technology; Testing; Text; Thick; Thinness; Time; Training; Variant; Vision; Visit; advanced disease; automated algorithm; base; burden of illness; cerebral atrophy; cohort; convolutional neural network; cost; deep learning; density; diagnostic tool; disability; efficacy evaluation; experience; ganglion cell; gray matter; image processing; imaging Segmentation; imaging biomarker; imaging modality; improved; insight; learning progression; macula; macular edema; magnetic resonance imaging biomarker; nervous system disorder; novel; novel diagnostics; open source; portability; random forest; recursive neural network; research clinical testing; retina blood vessel structure; retinal imaging; segmentation algorithm; three dimensional structure; tool; treatment choice; white matter

Project Summary/Abstract (Max 30 lines of text) Although magnetic resonance imaging (MRI) is necessary for diagnosing multiple sclerosis (MS), it has been challenging to acquire consistent MRI measurements of MS disease burden. Spectral domain optical coherence tomography (OCT) of the retina has emerged as a complementary source of imaging biomarkers, wherein retinal thickness measurements have been shown to correlate well with MS disease burden. As well, OCT angiography (OCTA)—a new imaging modality acquired with the same OCT scanner—yields multiple new biomarkers among which macular vessel density has been shown to correlate with MS disability. There is strong evidence that OCT and OCTA may provide much needed imaging biomarkers for MS, but there are remaining technical challenges to overcome. Many algorithms for computation of retinal layer thicknesses from OCT images have been developed, but measurement of longitudinal changes in individual MS subjects remains highly challenging, especially in MS where yearly changes are small relative to intrinsic measurement variations. We propose a novel iterative registration and deep learning segmentation algorithm for longitudinal OCT retinal image segmentation. Development of automatic algorithms for analysis of OCTA images is in an early stage and there are opportunities for significant improvements. We will develop a deep network for OCTA vessel segmentation and biomarker computation that both suppresses artifacts that are common in OCTA and provides consistent results across different scanners. As both OCT and OCTA become more widely used in the characterization and management of MS, it is becoming increasingly important to jointly characterize these biomarkers and relate them to disease status, which is currently characterized largely by clinical evaluations. We will address the central question of whether OCT and OCTA can be used to predict disease progression by developing a new disease progression score for MS based on multiple OCT and OCTA measurements as well as clinical and MRI biomarkers, acquired in both single and multiple imaging visits. The proposed research will: 1) Develop a fast, topologically-correct longitudinal segmentation method for the macula; 2) Develop a method for artifact-suppressed and consistent computation of OCTA features in the macula; 3) Develop a disease progression score to jointly characterize longitudinal retinal OCT and OCTA measurements in MS; and 4) Carry out longitudinal studies of healthy controls and people with MS using OCT and OCTA measurements. We will assess whether average features within the macula or features averaged over smaller segments yield better estimates of progression. We will also assess whether OCT alone or OCT together with OCTA provide better estimates of progression. Image processing and disease progression algorithms will be made freely available to the research community. The proposed research will greatly advance the use of OCT and OCTA in characterizing longitudinal changes in the retina, potentially leading to standard eye measurements for monitoring and managing MS and other neurological and eye diseases.

项目摘要/摘要（最多 30 行文本） 尽管磁共振成像 (MRI) 对于诊断多发性硬化症 (MS) 是必要的，但它已被广泛应用。 获得一致的多发性硬化症疾病负担的 MRI 测量结果具有挑战性。谱域光学 视网膜相干断层扫描（OCT）已成为成像生物标志物的补充来源， 其中视网膜厚度测量结果已被证明与多发性硬化症疾病负担密切相关。还有， OCT 血管造影 (OCTA)——一种使用同一台 OCT 扫描仪获取的新成像方式——产生多种 新的生物标志物，其中黄斑血管密度已被证明与多发性硬化症残疾相关。有 强有力的证据表明 OCT 和 OCTA 可能为 MS 提供急需的成像生物标志物，但也有 尚待克服的技术挑战。许多计算视网膜层厚度的算法 OCT 图像的数据已经开发出来，但测量个体 MS 受试者的纵向变化 仍然极具挑战性，特别是在 MS 中，相对于内在测量，每年的变化很小 变化。我们提出了一种新颖的纵向迭代配准和深度学习分割算法 OCT 视网膜图像分割。用于分析 OCTA 图像的自动算法的开发正在进行中 早期阶段，有机会进行重大改进。我们将为 OCTA 开发深度网络 血管分割和生物标志物计算都可以抑制 OCTA 中常见的伪影， 在不同的扫描仪上提供一致的结果。随着 OCT 和 OCTA 的应用越来越广泛 随着 MS 的表征和管理的不断深入，联合表征这些特征变得越来越重要 生物标志物并将其与疾病状态联系起来，目前疾病状态主要通过临床评估来表征。 我们将解决 OCT 和 OCTA 是否可用于预测疾病进展的核心问题： 基于多次 OCT 和 OCTA 测量，为 MS 制定新的疾病进展评分 作为临床和 MRI 生物标志物，在单次和多次成像访问中获得。拟议的研究将： 1）开发一种快速、拓扑正确的黄斑纵向分割方法； 2）制定方法 用于黄斑 OCTA 特征的伪影抑制和一致计算； 3）患上疾病 进展评分联合表征 MS 中的纵向视网膜 OCT 和 OCTA 测量；和 4) 使用 OCT 和 OCTA 测量对健康对照和 MS 患者进行纵向研究。 我们将评估黄斑内的平均特征或较小部分的平均特征是否会产生 更好地估计进展。我们还将评估单独使用 OCT 还是 OCT 与 OCTA 一起提供 更好地估计进展。图像处理和疾病进展算法将自由制作 可供研究界使用。拟议的研究将极大地推进 OCT 和 OCTA 的使用 表征视网膜的纵向变化，可能导致标准的眼睛测量 监测和管理多发性硬化症以及其他神经系统和眼部疾病。