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Updated: May 1, 2026

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats
Published on: January 10, 2019
Quantitative evaluation of factors influencing the repeatability of SD-OCT thickness measurements in the rat
Diana C Lozano1, Michael D Twa
1University of Houston College of Optometry, Houston, Texas, USA.
Purpose:
To quantify repeatability and reproducibility of thickness measurements and the effects of realignment and image quality on measurements of retinal thickness from optical coherence tomographic (OCT) imaging in the rat eye.
Methods:
Retinal imaging was performed in 16 brown norway rats (N = 16 EYES; X = 372 G). Precision metrics: 95% limits of agreement (LoA), intraclass correlation coefficient (ICC), and the coefficient of variation (CV), were calculated using manual and combined manual + automated realignment procedures for nerve fiber and retinal ganglion cell layer (NFL/GCL), NFL/GCL and inner plexiform layer (NFL/GCL + IPL), and total retina thicknesses (excluding blood vessels). The influence of image quality on NFL thickness measurement was assessed by comparing high- and low-quality image data (real and simulated) from the rat as well as clinical data.
Results:
Mean NFL/GCL thickness was 26 3 M, NFL/GCL + IPL thickness was 70 3 M, AND total retinal thickness was 192 7 M. Thickness difference between imaging sessions for NFL/GCL was 1 M (95% LOA: -4 to 3 μm; ICC = 0.82; CV = 4.7%), for NFL/GCL + IPL was 0 μm (95% LoA: -4 to 4 μm; ICC = 0.88; CV = 1.4%), and total retinal thickness was 1 μm (95% LoA: -3 to 4 μm; ICC = 0.97; CV = 0.7%). Thickness differences were similar between realignment procedures (NFL/GCL: P = 0.43; NFL/GCL + IPL: P = 0.33; total retina: P = 0.62). Although NFL thickness measurements increased slightly in low-quality rat images (4 μm; P = 0.04), this was not true with clinical images (1.4 μm; P = 0.36).
Conclusions:
Precision of retinal layer thickness estimation from OCT imaging is excellent when manual and automated realignment procedures are combined, but may still be influenced by image quality and segmentation methods.

