Relationship between Glaucoma and the Three-Dimensional Optic Nerve Head Related Structure

青光眼与三维视神经头相关结构的关系

• 批准号: 10316448
• 负责人: Mengyu Wang
• 金额: $ 24.72万
• 依托单位: SCHEPENS EYE RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10316448
• 关键词: 3-Dimensional; Age; Anatomy; Biomechanics; Blindness; Bruch' s basal membrane structure; Cessation of life; Clinical; Clinical Data; Computer Simulation; Cross-Sectional Studies; Data; Data Science; Development; Diagnosis; Diagnostic; Ear; Elements; Eye; Fundus; Gaussian model; Geometry; Glaucoma; Hour; Image; Individual; Injury; Lead; Linear Models; Linear Regressions; Location; Machine Learning; Measurement; Mechanics; Medical Records; Methods; Modeling; Monitor; Morphologic artifacts; Multivariate Analysis; Nerve Fibers; Observational Study; Ophthalmology; Optic Disk; Optic Nerve; Optical Coherence Tomography; Participant; Pathogenesis; Patients; Pattern; Phase; Physiologic Intraocular Pressure; Play; Population Study; Process; Quality of life; Research; Research Training; Resolution; Retinal Ganglion Cells; Role; Scanning; Scheme; Scientist; Severities; Site; Solid; Source; Structure; Structure-Activity Relationship; Surface; Techniques; Testing; Thick; Translating; Variant; Vision; Visual Fields; Width; archetypal analysis; base; cell injury; clinical Diagnosis; clinical practice; clinically relevant; deep neural network; demographics; fundus imaging; image processing; improved; independent component analysis; insight; knowledge base; machine learning method; mathematical model; nerve damage; nonlinear regression; optic cup; retina blood vessel structure; retinal imaging; retinal nerve fiber layer; study population; training opportunity; unsupervised learning

Project Summary Glaucoma is the second leading cause of blindness globally, and is characterized by optic nerve damage that leads to the death of retinal ganglion cells with accompanying visual field (VF) loss. The optic nerve head (ONH) is the site of injury to the optic nerve fibers and plays a central role in glaucoma pathogenesis and diagnosis. Traditionally, glaucoma is diagnosed based on fundus inspection of the ONH, which provides information about the surface contour of the ONH. However, the optic nerve damage occurs in the deeper layers. With the development of optical coherence tomography (OCT) techniques for three-dimensional (3D) retinal imaging, parameters derived from the 3D ONH related structure (e.g., Bruch's membrane opening minimum rim width, peripapillary retinal nerve fiber layer thickness, disc tilt etc.) have been studied to better understand glaucoma pathogenesis, and are used to supplement clinical diagnosis. In addition, studies of the ONH biomechanics have also shown that the strain level at the ONH at any given intraocular pressure level depends on the 3D geometry of the ONH related structure. A high strain level is hypothesized to contribute to retinal ganglion cell injury. Previous research has suggested that the 3D ONH related structure is correlated to glaucoma pathogenesis and critically important to glaucoma diagnosis. However, to date, a systematic study using clinical data to determine the impact of the 3D ONH related structure on glaucoma has not been conducted. We propose to study the relationship between the 3D ONH related structure and glaucoma with a diverse set of combined techniques including image processing, computational mechanics and machine learning. The specific aims of this project are to: (1) Derive features from the 3D ONH related structure and study their implications on VF loss patterns (K99 Phase). (2) Investigate the impact of the strain field patterns at the ONH on glaucoma (K99 Phase). (3) Study the effect of the 3D ONH related features on OCT diagnostic parameters (R00 Phase). (4) Model central vision loss from the 3D ONH related structural features (R00 Phase). Collectively, these studies will provide new insights and perspectives into the structure-function relationships in glaucoma and establish ocular anatomy specific norms of retinal nerve fiber layer profiles, which will advance our current understanding of glaucoma pathogenesis and improve glaucoma diagnosis. Our research is of high clinical relevance and can be potentially translated into clinical practice for better glaucoma diagnosis, monitoring and treatment. Through the proposed research and training plans, the applicant will build a solid knowledge base in ophthalmology and further improve his expertise in mathematical modeling and data science. This project will provide critical training opportunities to further enhance the applicant's capabilities to become an independent computational vision scientist in ophthalmology.

项目摘要 青光眼是全球失明的第二大主要原因，其特征是视神经损伤 导致视网膜神经节细胞与伴随视野（VF）损失的死亡。视神经头（ONH） 是视神经纤维受伤的部位，在青光眼的发病机理和诊断中起着核心作用。 传统上，基于对ONH的眼底检查，对青光眼进行了诊断，该检查提供了有关 ONH的表面轮廓。但是，视神经损伤发生在较深的层中。随着光学相干断层扫描（OCT）技术的开发，用于三维（3D）视网膜成像，参数 源自3D ONH相关的结构（例如，Bruch的膜开口最小边缘宽度，围骨 已经研究了视网膜神经纤维层厚度，椎间盘倾斜等），以更好地了解青光眼的发病机理， 并用于补充临床诊断。此外，对ONH生物力学的研究也显示了 在任何给定的眼内压力水平下，ONH处的应变水平取决于ONH的3D几何形状 相关结构。假设高应变水平有助于视网膜神经节细胞损伤。先前的研究 已经表明，3D ONH相关的结构与青光眼的发病机理相关，至关重要 进行青光眼诊断。但是，迄今为止，一项系统的研究使用临床数据来确定 尚未进行3D ONH相关的青光眼结构。 我们建议研究3D ONH相关结构与青光眼之间的关系 组合技术，包括图像处理，计算力学和机器学习。该项目的具体目的是：（1）从3D ONH相关结构中得出特征并研究其含义 在VF损失模式（K99阶段）上。 （2）研究应变场模式在ONH上对青光眼的影响 （K99阶段）。 （3）研究3D ONH相关特征对OCT诊断参数（R00阶段）的影响。 （4）模型中央视力从3D ONH相关的结构特征（R00相）。总的来说，这些研究将为青光眼的结构 - 功能关系提供新的见解和观点，并建立 视网膜神经纤维层轮廓的眼部解剖学特定规范，这将提高我们当前的理解 青光眼发病机理并改善青光眼诊断。我们的研究具有很高的临床相关性，可以 有可能将其转化为临床实践，以获得更好的青光眼诊断，监测和治疗。 通过拟议的研究和培训计划，申请人将在眼科中建立坚实的知识库，并进一步提高他在数学建模和数据科学方面的专业知识。这个项目将提供 关键的培训机会，以进一步增强申请人成为眼科独立计算视觉科学家的能力。