New Techniques for Measuring Volumetric Structural Changes in Glaucoma

摘要 此K99/R00应用程序支持计算数学和计算机方面的其他研究培训 使Madhuudhanan Balasubramanian博士--申请者--能够成为独立的 计算眼科的多学科研究员。具体地说，在这个K99培训阶段 Grant，Balasubramanian博士将在加州大学圣地亚哥分校在琳达·赞威尔博士的指导下进行培训， 眼科老牌青光眼临床研究员，以及一支由 导师，包括数学系的迈克尔·霍尔斯特博士和 计算数学，计算科学、数学和工程以及 来自计算机科学与工程的大卫·克里格曼博士。培训将通过正式的 课程作业、动手实验室培训、有指导的研究、咨询委员会的进度审查、访问 与研究人员合作，定期参加研讨会和讲习班。随后的R00 独立研究阶段涉及应用Balasubramanian博士新获得的计算 从光学图像识别青光眼随时间的变化这一困难任务的技术 神经头和视网膜神经纤维层。 进行性视神经病变的记录是目前可用的最佳黄金标准 青光眼诊断。共焦扫描激光检眼镜(CSLO)与光谱域光学 相干层析成像(SD-OCT)是两种可用于监控光学系统的光学成像设备 神经头健康在青光眼诊断和治疗中的作用。目前，有几个统计和计算 CSLO检查提供了检测局限性青光眼变化的技术。SD-OCT是一个 基于光学干涉原理的新一代眼科成像仪。与之形成鲜明对比的是 CSLO技术，SDOCT可以从内界膜(ILM)通过 Bruch的膜，能够以非常高的分辨率捕捉到视神经头的三维结构。 这些高分辨率、高维的体积扫描将数据复杂性提升到了一个新的水平， 之前的青光眼进展分析，因此，强大的(高性能)计算技术 为了充分利用高精度的视网膜测量来诊断青光眼。中心焦点 该应用程序在K99指导阶段的应用程序将在1)开发计算性和 SDOCT检测不同视网膜层结构青光眼变化的统计技术 扫描，以及2)开发青光眼治疗的新途径，其中视网膜层处于紧张状态 将被非侵入性地估计为青光眼进展的特征。在R00独立阶段， 具体目标侧重于开发1)检测体积的统计和计算技术 使用3D SD-OCT体积扫描和2)计算框架来计算青光眼随时间的变化 从标准SD-OCT扫描中估计全场3-D体积应变。

ABSTRACT This K99/R00 application supports additional research training in computational mathematics and computer vision which will enable Dr. Madhusudhanan Balasubramanian-the applicant, to become an independent multidisciplinary investigator in computational ophthalmology. Specifically, in the K99 training phase of this grant, Dr. Balasubramanian will train at UC San Diego under the direction of Linda Zangwill PhD, an established glaucoma clinical researcher in the Department of Ophthalmology, as well as a team of co- mentors, including, Dr. Michael Holst from the Department of Mathematics and co-director for the Center for Computational Mathematics, and co-director of the Comptutational Science, Mathematics and Engineering and Dr. David Kriegman from Computer Science and Engineering. Training will be conducted via formal coursework, hands-on lab training, mentored research, progress review by an advisory committee, visiting collaborating researchers and regular attendance at seminars and workshops. The subsequent R00 independent research phase involves applying Dr. Balasubramanian's newly acquired computational techniques to the difficult task of identifying glaucomatous change over time from optical images of the optic nerve head and retinal nerve fiber layer. A documented presence of progressive optic neuropathy is the best gold standard currently available for glaucoma diagnosis. Confocal Scanning Laser Ophthalmoscope (CSLO) and Spectral Domain Optical Coherence Tomography (SD-OCT) are two of the optical imaging instruments available for monitoring the optic nerve head health in glaucoma diagnosis and management. Currently, several statistical and computational techniques are available for detecting localized glaucomatous changes from the CSLO exams. SD-OCT is a new generation ophthalmic imaging instrument based on the principle of optical interferometry. In contrast to the CSLO technology, SDOCT can resolve retinal layers from the internal limiting membrane (ILM) through the Bruch's membrane and can capture the 3-D architecture of the optic nerve head at a very high resolution. These high-resolution, high-dimensional volume scans introduce a new level of data complexity not seen in glaucoma progression analysis before and therefore, powerful (high-performance) computational techniques are required to fully utilize the high precision retinal measurements for glaucoma diagnosis. The central focus of this application in the K99 mentored phase of the application will be in 1) developing computational and statistical techniques for detecting structural glaucomatous changes in various retinal layers from the SDOCT scans, and 2) developing a new avenue of research in glaucoma management where in strain in retinal layers will be estimated non-invasively to characterize glaucomatous progression. In the R00 independent phase, the specific aims focus on developing 1) statistical and computational techniques for detecting volumetric glaucomatous change over time using 3-D SD-OCT volume scans and 2) a computational framework to estimate full-field 3-D volumetric strain from the standard SD-OCT scans.