Envisu r2210

Anterior segment morphology and thickness of the retinal layers were evaluated in anesthetized mice, with an SD-OCT system (Envisu R2210; Bioptigen, Morrisville, NC, USA).^{22 (link)} Spectral-domain OCT was performed by using 100 consecutive B-scan lines composed of 1000 A-scans, in a 3.0 × 3.0-mm (anterior segment) or 1.4 × 1.4-mm field (retina). Anterior chamber depth, CCT, total retinal thickness, and thickness of the ganglion cell complex were analyzed by using InVivoVue Diver 2.2 software (Bioptigen).

The corneal images were captured by a spectral-domain OCT (SD-OCT) machine (Envisu R2210, Bioptigen, Buffalo Grove, IL). The size of the captured images is 1024 × 1000 pixels. The used machine has a corrected depth of 1.58 mm using approximate refractive index for the whole cornea of 1.38, which corresponds to an axial digital resolution (i.e., pixel height) of 1.5 μm. However, the axial optical resolution of the machine is approximately 3 μm. The machine has a transversal width of 6 mm, which corresponds to a transversal digital resolution (i.e., pixel width) of 6 μm. A radial scan pattern was used for all the patients in the study.

OCT was recorded using a Bioptigen Envisu R2210 with a 10-mm telecentric lens directly prior to sacrifice. OCT datasets comprise 100 transverse sections spanning 6 mm of the eye (the central wound). Each section has a width of 6 mm (1,000 pixels) and a depth of 1.491 mm (1,024 pixels). Regions of each image containing cornea are identified using the MATLAB function “imbinarize” with the adaptive thresholding method, and all other pixels of the image are reduced to zero intensity. Corneal thickness was measured at pixel resolution in these thresholded images as the distance across the nonzero region, and thickness was averaged across the entire cornea. With regard to OCT variance, aberrations in cornea (i.e., scarring) increase nonuniformity of pixel intensities in localized areas of the cornea. To quantify this nonuniformity, we first segmented the cornea in each image file into 100 equal parts. For each segment the statistical variance (i.e., SD2) of pixel intensities was calculated. This yields 100 variances for each transverse section. Transverse sections from a dataset are averaged yielding a two-dimensional “variance by position” plot.