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弥漫性视网膜神经纤维层缺陷在厚度图中的识别和量化。

Diffuse retinal nerve fiber layer defects identification and quantification in thickness maps.

机构信息

Department of Ophthalmology, Hanyang University College of Medicine, Seoul, Korea.

出版信息

Invest Ophthalmol Vis Sci. 2014 Apr 17;55(5):3208-18. doi: 10.1167/iovs.13-13181.

DOI:10.1167/iovs.13-13181
PMID:24744205
Abstract

PURPOSE

To report retinal nerve fiber layer (RNFL) defect identification and quantification in RNFL thickness maps according to the structural RNFL loss, and to evaluate diffuse RNFL defects.

METHODS

A total of 170 patients with glaucoma and 186 normal subjects were consecutively enrolled. We defined RNFL defects in an RNFL thickness map by the degree of RNFL loss. The reference level for RNFL defect determination was set as a 20% to 70% degree of RNFL loss with a 1% interval. To identify RNFL defects, each individual RNFL thickness map was compared to the normative database map by using MATLAB software, and the region below the reference level was detected. The area, volume, location, and angular width of each RNFL defect were measured. Diffuse RNFL defects were defined as having an angular width > 30°.

RESULTS

The optimal reference level for glaucomatous RNFL defects identification was 42% loss of RNFL. Retinal nerve fiber layer defects were identified in all (100%) of the 170 glaucoma patients and false-positive RNFL defects were detected in 16 (8.16%) cases among the 186 normal subjects. In all, 64.1% of glaucoma patients had diffuse RNFL defects, and 47.7% of diffuse RNFL defects were associated with mild glaucoma patients. The volume of diffuse RNFL defects was significantly associated with the severity of glaucomatous damage (P = 0.009). Diffuse RNFL defects were located closer to the center of the optic disc than localized RNFL defects (P < 0.001).

CONCLUSIONS

Retinal nerve fiber layer thickness map analysis is an effective method for analyzing RNFL defects. Quantitative measurements (area, volume, location, and width) were useful to understanding diffuse RNFL defects.

摘要

目的

根据结构神经纤维层(RNFL)丢失程度,报告 RNFL 厚度图中的 RNFL 缺陷识别和定量,并评估弥漫性 RNFL 缺陷。

方法

连续纳入 170 例青光眼患者和 186 例正常对照者。我们通过 RNFL 丢失程度定义 RNFL 厚度图中的 RNFL 缺陷。将 RNFL 缺陷确定的参考水平设定为 RNFL 丢失程度为 20%至 70%,间隔为 1%。为了识别 RNFL 缺陷,使用 MATLAB 软件将每个个体的 RNFL 厚度图与参考数据库图进行比较,并检测低于参考水平的区域。测量每个 RNFL 缺陷的面积、体积、位置和角度宽度。将弥漫性 RNFL 缺陷定义为角度宽度>30°。

结果

青光眼 RNFL 缺陷识别的最佳参考水平为 42%的 RNFL 丢失。170 例青光眼患者中均发现了 RNFL 缺陷,而在 186 例正常对照者中,有 16 例(8.16%)出现假阳性 RNFL 缺陷。所有青光眼患者中有 64.1%存在弥漫性 RNFL 缺陷,其中 47.7%的弥漫性 RNFL 缺陷与轻度青光眼患者有关。弥漫性 RNFL 缺陷的体积与青光眼损伤的严重程度显著相关(P=0.009)。弥漫性 RNFL 缺陷的位置比局灶性 RNFL 缺陷更接近视盘中心(P<0.001)。

结论

RNFL 厚度图分析是一种分析 RNFL 缺陷的有效方法。定量测量(面积、体积、位置和宽度)有助于理解弥漫性 RNFL 缺陷。

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