Abstract
Purpose.:
To map choroidal (ChT) and retinal thickness (RT) in patients with diabetes type 1 with and without maculopathy and retinopathy in order to compare them with healthy subjects using high speed 3-dimensional (3D) 1060 nm optical coherence tomography (OCT).
Methods.:
Thirty-three eyes from 33 diabetes type 1 subjects (23–57 years, 15 male) divided into groups of without pathology (NDR) and with pathology (DR; including microaneurysms, exudates, clinically significant macular-oedema and proliferative retinopathy) were compared with 20 healthy axial eye length and age-matched subjects (24–57 years, 9 male), imaged by high speed (60.000 A-scans/s) 3D 1060 nm OCT performed over 36° × 36° field of view. Ocular health status, disease duration, body mass index, haemoglobin-A1c, and blood pressure (bp) measurements were recorded. Subfoveal ChT, and 2D topographic maps between retinal pigment epithelium and the choroidal/scleral–interface, were automatically generated and statistically analyzed.
Results.:
Subfoveal ChT (mean ± SD, μm) for healthy eyes was 388 ± 109; significantly thicker than all diabetic groups, 291 ± 64 for NDR, and 303 ± 82 for DR (ANOVA P < 0.004, Tukey P = 0.01 for NDR and DR). Thinning did not relate to recorded factors (multi-regression analysis, P > 0.05). Compared with healthy eyes and the NDR, the averaged DR ChT-map demonstrated temporal thinning that extended superiorly and temporal-inferiorly (unpaired t-test, P < 0.05). Foveal RT and RT-maps showed no statistically significant difference between groups (mean SD, μm, healthy 212 ± 17, NDR 217 ± 15, DR 216 ± 27, ANOVA P > 0.05).
Conclusions.:
ChT is decreased in diabetes type 1, independent of the absence of pathology and of diabetic disease duration. In eyes with pathology, 3D 1060 nm OCT averaged maps showed an extension of the thinning area matching retinal lesions and suggesting its involvement on onset or progression of disease.
Monocular visual acuity was determined with ETDRS Original Series Charts (Precision Vision, LaSalle, IL). Five AL measurements were averaged from each eye using optical biometry (IOL Master Zeiss, Jena, Germany). All subjects had blood pressure (BP) measured after at least 20 minutes resting in a sitting position. All diabetic subjects had their glycosylated hemoglobin A1c (an estimate of the average blood glucose over the past 1–3 months) recorded if the measurement was not older than 3 months or else it was tested on the day of the recruitment. Fasting blood glucose was not obtained, as this would have altered homeostatic mechanisms, and not allowed a representative comparison to be made between normal and diabetic eyes. Information about diabetes duration, weight, and height were recorded.
High speed, 60.000 A-scans/s 3D OCT–imaging at 1060 nm was performed with less than 2.5 mW at the cornea, well below the maximum power limit for 10 second exposure.
13,14 3D OCT volumes were acquired at 1060 nm with 15- to 20-μm transverse resolution, approximately 7-μm axial resolution and 512 voxels per depth-scan (A-scan). Raster scans across a 36 × 36° field were centered on the fovea and resulted in up to 120 frames/second. Automatic retinal and choroidal segmentation, automatic measurement of ChT from preprocessed images with ImageJ software (available at
http://rsb.info.nih.gov/ij/index.html; National Institutes of Health, Bethesda, MD)
8 and the generation and statistical analysis of thickness maps is described elsewhere.
11,15 Briefly, axial retinal thickness was defined as the distance between internal limiting membrane and the center of the peaks originating from the retinal pigment epithelium/Bruch's membrane/choriocapillaris (RBC) complex and axial ChT as the distance between RBC complex and the choroidal-scleral interface (
Fig. 1 demonstrates segmentation lines at each of the interfaces). For the investigation of the thickness variation throughout the entire field of view, thickness maps were generated based on automatic segmentation.
11 This method uses training data from manual segmentations in healthy and diseased eyes of previous publications to build a statistical model. Its advantage is that it can actively learn and determine the segmentation line in a low signal, noisy environment such as in OCT tomograms in the region of the choroid without having to rely on boundary edge information. The resulting pixel distance was converted into optical distance using the depth sampling calibration for the 1060 nm OCT system and further to the anatomical distance. This resulted in thickness maps for individual eyes. Reported variation for automatic segmentation in eyes with pathology is 13%,
11 comparable to values seen by retinal automated segmentation.
16 Interobserver variability, measured by re-imaging seven subjects (two with pathology) and comparing automatic segmentation of subfoveal ChT, ranged between 0% and 10% with a median of 1% difference between the first and the second image.
For the investigation of a correlation between the location of retinal change and choroidal thickness alterations, individual ChT-maps were viewed at the location of recorded microaneurysms, exudates and focal edema, or proliferation for obvious changes in thickness.
For the statistical analysis of mean and variation, all the individual ChT-maps and RT-maps were aligned to each other in respect to the macula and optic nerve position with Matlab software (The MathWorks, Inc., Natick, MA). Unreliable portions of the images with five or less than five measurements at one location were excluded. Before statistical analysis the maps were median filtered by a 30 × 30 kernel to suppress the influence of local fluctuations of the individual maps (e.g., vessels) or positioning errors. To judge the clinical significance of possible thickness alteration in this study, subfoveal ChT was measured after automatic segmentation and smoothing for compound maps and controlled for manually by an experienced observer. The average difference was below 1% for each group of subjects with an SD of 9% (9% for NDR, 7% for DR, and 10% for healthy, P > 0.05, ANOVA).
To create a compound map of average thickness, mean and SD was obtained for these three groups of eyes with color-coded thickness maps. The coefficient of variation was used to map contour lines of 45%, 30%, and 15% for the variation within each group. Difference maps were generated to investigate the change in ChT and RT by subtracting each diabetic category from the healthy eyes group. A further statistical analysis of the difference between the healthy and each diabetic group was generated by conducting t-tests over the field of view. To show areas of statistically significant difference between the compound maps, contour lines for P values smaller than 0.05 were drawn on the difference maps. The statistics software IBM SPSS Statistics for Windows, Version 20.0 (IBM Corp., Armonk, NY) was used for conducting ANOVA testing and a multiregression analysis of the contribution of groups and their characterizing factors to ChT. Therefore, central ChT measurement was located beneath the foveola.