Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242, USA.
Invest Ophthalmol Vis Sci. 2013 Jul 18;54(7):4808-12. doi: 10.1167/iovs.13-12211.
To report an automated method for adjustment of the retinal angle in spectral-domain optical coherence tomography (SD-OCT) and compare its intervisit reproducibility of the peripapillary retinal nerve fiber layer (RNFL) thicknesses of glaucomatous eyes to that obtained by the Cirrus algorithm.
Fifty-six glaucoma and glaucoma suspect subjects were repeatedly imaged, and optic nerve head (ONH)-centered OCT image volumes (200×200×1024 voxels, 6×6×2 mm3, Cirrus HD-OCT machine) were acquired within a 4-month period from one eye of the 56 patients. Retinal angle correction in B-scans was accomplished by adjusting the angle using the voxel aspect ratio of the SD-OCT followed by straightening of rotated A-scans. The RNFL layer was automatically segmented using the Iowa Reference Algorithm. Reproducibility of the peripapillary RNFL thicknesses was determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), repeatability coefficient (RC), and 95% tolerance limit (TL) for the Iowa Reference Algorithm without and with the retinal angle correction and for the Cirrus algorithm (Cirrus version 5.1.0.96).
The angle corrected Iowa Reference Algorithm (ICC: 0.990, 95% confidence interval [CI]: 0.983-0.994) for peripapillary RNFL thicknesses showed significantly better reproducibility than the nonangle corrected algorithm (ICC: 0.964, 95% CI: 0.940-0.979) and the Cirrus algorithm (ICC: 0.960, 95% CI: 0.933-0.976) based on the 95% CIs for the ICCs.
Angle correction leads to more consistent peripapillary RNFL thicknesses. This may lead to improved management of patients with glaucoma.
报告一种用于调整频域光学相干断层扫描(SD-OCT)中视网膜角度的自动化方法,并比较其在青光眼患者的视盘周围视网膜神经纤维层(RNFL)厚度的随访可重复性与 Cirrus 算法的结果。
对 56 例青光眼和疑似青光眼患者的双眼进行重复成像,在 4 个月的时间内,使用 Cirrus HD-OCT 仪器采集每只眼视神经头(ONH)中心的 OCT 图像体积(200×200×1024 体素,6×6×2mm3)。通过调整 SD-OCT 体素纵横比来校正 B 扫描中的视网膜角度,然后对旋转的 A 扫描进行校正。使用爱荷华参考算法自动分割 RNFL 层。通过内类相关系数(ICC)、变异系数(CV)、重复性系数(RC)和爱荷华参考算法(无和有视网膜角度校正)以及 Cirrus 算法(Cirrus 版本 5.1.0.96)的 95%容忍限(TL)来确定视盘周围 RNFL 厚度的可重复性。
校正角度后的爱荷华参考算法(ICC:0.990,95%置信区间[CI]:0.983-0.994)用于视盘周围 RNFL 厚度的可重复性明显优于未校正角度的算法(ICC:0.964,95% CI:0.940-0.979)和 Cirrus 算法(ICC:0.960,95% CI:0.933-0.976),这是基于 ICC 的 95%CI。
角度校正可导致更一致的视盘周围 RNFL 厚度。这可能会改善青光眼患者的管理。