Lee Sieun, Heisler Morgan L, Popuri Karteek, Charon Nicolas, Charlier Benjamin, Trouvé Alain, Mackenzie Paul J, Sarunic Marinko V, Beg Mirza Faisal
Faculty of Applied Sciences, School of Engineering Science, Simon Fraser UniversityBurnaby, BC, Canada.
Center for Imaging Sciences, Johns Hopkins UniversityBaltimore, MD, United States.
Front Neurosci. 2017 Jul 12;11:381. doi: 10.3389/fnins.2017.00381. eCollection 2017.
Optical coherence tomography provides high-resolution 3D imaging of the posterior segment of the eye. However, quantitative morphological analysis, particularly relevant in retinal degenerative diseases such as glaucoma, has been confined to simple sectorization and averaging with limited spatial sensitivity for detection of clinical markers. In this paper, we present point-wise analysis and visualization of the retinal nerve fiber layer and choroid from cross-sectional data using functional shapes (fshape) registration. The fshape framework matches two retinas, or generates a mean of multiple retinas, by jointly optimizing the surface geometry and functional signals mapped on the surface. We generated group-wise mean retinal nerve fiber layer and choroidal surfaces with the respective layer thickness mapping and showed the difference by age (normal, younger vs. older) and by disease (age-matched older, normal vs. glaucomatous) in the two layers, along with a more conventional sector-based analysis for comparison. The fshape results visualized the detailed spatial patterns of the differences between the age-matched normal and glaucomatous retinal nerve fiber layers, with the glaucomatous layers most significantly thinner in the inferior region close to Bruch's membrane opening. Between the young and older normal cases, choroid was shown to be significantly thinner in the older subjects across all regions, but particularly in the nasal and inferior regions. The results demonstrate a comprehensive and detailed analysis with visualization of morphometric patterns by multiple factors.
光学相干断层扫描可提供眼部后段的高分辨率三维成像。然而,定量形态学分析,尤其是在青光眼等视网膜退行性疾病中具有重要意义的分析,一直局限于简单的扇形划分和平均,对临床标志物检测的空间敏感性有限。在本文中,我们使用功能形状(fshape)配准,对横断面数据中的视网膜神经纤维层和脉络膜进行逐点分析和可视化。fshape框架通过联合优化表面几何形状和映射在表面上的功能信号,来匹配两个视网膜,或生成多个视网膜的平均值。我们生成了具有各自层厚度映射的组平均视网膜神经纤维层和脉络膜表面,并展示了这两层在年龄(正常、年轻与年长)和疾病(年龄匹配的年长、正常与青光眼)方面的差异,同时还进行了更传统的基于扇形的分析以作比较。fshape结果可视化了年龄匹配的正常和青光眼视网膜神经纤维层之间差异的详细空间模式,青光眼层在靠近布鲁赫膜开口的下方区域最明显变薄。在年轻和年长正常病例之间,脉络膜在所有区域,尤其是鼻侧和下方区域,在年长受试者中明显变薄。结果展示了一种综合且详细的分析,并通过多种因素对形态测量模式进行了可视化。