Stephan Holzer

Measuring retinal nerve fiber layer birefringence, retardation, and thickness using wide-field, high-speed polarization sensitive spectral domain OCT.

PURPOSE We presented a novel polarization sensitive optical coherence tomography (PS-OCT) system for measuring retinal nerve fiber layer (RNFL) birefringence, retardation, and thickness, and report on the repeatability of acquiring these quantities. METHODS A new PS-OCT system, measuring at 840 nm, was developed that supports scan angles of up to 40° × 40° with an A-scan rate of 70 kHz. To test the performance and reproducibility, we measured 10 eyes of 5 healthy human volunteers five times each. All volunteers were imaged further with scanning laser polarimetry (SLP). The obtained RNFL birefringence, retardation, and thickness maps were averaged, and standard deviation maps were calculated. For quantitative comparison between the new PS-OCT and SLP, a circumpapillary evaluation within 2 annular segments (superior and inferior to the optic disc) was performed. RESULTS High quality RNFL birefringence, retardation, and thickness maps were obtained. Within the superior and inferior segments, the mean retardation for individual eyes ranged from 20° to 28.9° and 17.2° to 28.2°, respectively. The quadrant precision over the 5 consecutive measurements for each subject, calculated for the average retardation obtained within the superior and inferior quadrants ranged from 0.16° to 0.69°. The mean birefringence ranged from 0.106°/μm to 0.141°/μm superior and 0.101°/μm to 0.135°/μm inferior, with a quadrant precision of 0.001°/μm to 0.007°/μm. The mean RNFL thickness varied from 114 to 150 μm superior, and 111 to 140.9 μm inferior (quadrant precision ranged from 3.6 to 11.9 μm). CONCLUSIONS The new PS-OCT system showed high image quality and reproducibility, and, therefore, might be a valuable tool for glaucoma diagnosis.

Correlation between retinal vessel density profile and circumpapillary RNFL thickness measured with Fourier-domain optical coherence tomography.

Aim To assess circumpapillary retinal vessel density (RVD) profiles and correlate them with retinal nerve fibre layer (RNFL) thickness measured by Fourier domain optical coherence tomography (FD-OCT). Methods RNFL thickness of 106 healthy volunteers was measured using Cirrus FD-OCT. A proprietary software was developed in MATLAB to assess the thickness and position of circumpapillary retinal vessels using the scanning laser ophthalmoscopy fundus image, centred on the optic disc. The individual retinal vessel positions and thickness values were integrated in a 256-sector RVD profile, and intrasubject and intersubject correlations were calculated. Results The mean value±SD for intrasubject correlation between RVD and RNFL was 0.5349±0.1639, with 101 of 106 subjects presenting significant correlation (p<0.05). 181 (out of 256) sectors presented a significant correlation between RVD and RNFL, with a mean value±SD of 0.2600±0.1140 (p<0.05). Conclusions Using our model of the circumpapillary retinal vessel distribution, 70% of the RNFL thickness is influenced by RVD. On average, 7% of the interindividual variance of the RNFL thickness may be explained by RVD. A normative database that takes into account the circumpapillary blood vessels might slightly improve the diagnostic power of RNFL measurement.

Influence of disc-fovea angle and retinal blood vessels on interindividual variability of circumpapillary retinal nerve fibre layer.

Background To assess whether intersubject variability of circumpapillary retinal nerve fibre layer (RNFL) thickness in healthy subjects acquired with spectral domain optical coherence tomography (SD-OCT) can be reduced by considering the disc-fovea angle (DFA), either alone or together with a compensation based on retinal blood vessel distribution (RVD). Methods 106 healthy volunteers underwent SD-OCT examination centred on the optic disc (OD) and on the macula. OD contours and foveal positions were automatically calculated. RVD at 3.4 mm diameter circle was manually assessed. We made two approaches to reduce interindividual variability in RNFL values using compensation processes; RVD compensation: RNFL thickness values were compensated according to RVD variation (RNFLRVD) and DFA compensation: we shifted the RNFL thickness measurements according to the DFA (RNFLDFA). Coefficient of variance (CoV) was calculated in 12 clock hour sectors for original RNFL (RNFLo), RNFLDFA, RNFLRVD and RNFL with both compensation methods (RNFLDFA-RVD). Results Compared with the mean CoV of RNFLO, mean CoV of RNFLDFA, RNFLRVD and RNFLDFA-RVD was changed by −0.71% (p>0.05), −9.51% (p<0.001) and −7.55% (p=0.001), respectively. When compared with RNFLDFA, RNFL DFA-RVD significantly reduced the mean CoV by −6.69% (p=0.001), while compared with RNFLRVD, RNFL DFA-RVD did not significantly increase the mean CoV (+2.20%), (p>0.05). Conclusions Although reaching an improvement in some sectors, rotation of RNFL measurements according to the DFA on average does not reduce intersubject variability of RNFL. However, adjusting for RVD reduced the variance significantly. The results reinforce our work in assessing RVD as an important anatomical factor responsible for intersubject variability in RNFL measurements.

Compensation for retinal vessel density reduces the variation of circumpapillary RNFL in healthy subjects.

This work intends to assess circumpapillary retinal vessel density (RVD) at a 3.46 mm diameter circle and correlate it with circumpapillary retinal nerve fiber layer (RNFL) thickness measured with Fourier-Domain Optical Coherence Tomography. Furthermore, it aims to evaluate the reduction of intersubject variability of RNFL when considering RVD as a source of information for RNFL distribution. For that, 106 healthy subjects underwent circumpapillary RNFL measurement. Using the scanning laser ophthalmoscope fundus image, thickness and position of retinal vessels were assessed and integrated in a 256-sector RVD profile. The relationship between local RVD value and local RNFL thickness was modeled by linear regression. RNFL was then compensated for RVD variation by regression formulas. A strong statistically significant intrasubject correlation was found for all subjects between RVD and RNFL profiles (mean R = 0.769). In the intersubject regression analysis, 247 of 256 RNFL sectors showed a statistically significant positive correlation with RVD (mean R = 0.423). RVD compensation of RNFL resulted in a relative reduction of up to 20% of the intersubject variance. In conclusion, RVD in a 3.46mm circle has a clinically relevant influence on the RNFL distribution. RVD may be used to develop more individualized normative values for RNFL measurement, which might improve early diagnosis of glaucoma.

Retinal Blood Vessel Distribution Correlates With the Peripapillary Retinal Nerve Fiber Layer Thickness Profile as Measured With GDx VCC and ECC.

Purpose:Aim of the present study was to evaluate whether there is a correlation between retinal blood vessel density (RVD) and the peripapillary retinal nerve fiber layer (RNFL) thickness profile. Methods:RNFL thickness of 106 healthy subjects was measured using scanning laser polarimetry, GDx variable corneal compensation (VCC), and GDx enhanced corneal compensation (ECC). A proprietary software was developed in MATLAB to measure the peripapillary retinal vessels using scanning laser ophthalmoscopy fundus images, centered on the optic disc measured by Cirrus spectral domain optical coherence tomography. The individual retinal vessel positions and thickness values were integrated in a 64-sector RVD profile and intrasubject and intersubject correlations were calculated. Results:The mean R value±SD for intrasubject correlation between RVD and RNFL thickness measured with GDx VCC and GDx ECC was 0.714±0.157 and 0.629±0.140, with 105 of 106 subjects presenting significant correlations. In the intersubject linear regression analysis for GDx VCC, 33 of 64 (52%) sectors presented a significant Pearson correlation coefficient between RNFL thickness and RVD values, with a mean R value of 0.187±0.135 (P<0.05). Conclusions:Peripapillary RNFL thickness profiles correlate with the RVD over 50% of the sectors and might explain up to 26% of the interindividual variance of the peripapillary RNFL thickness values as measured with GDx VCC. To our opinion, taking into account RVD might reduce interindividual variation in peripapillary RNFL thickness profiles measured with scanning laser polarimetry.

Multivariate Model of the Intersubject Variability of the Retinal Nerve Fiber Layer Thickness in Healthy Subjects.

PURPOSE We present and validate a multivariate model that partially compensates for retinal nerve fiber layer (RNFL) intersubject variability. METHODS A total of 202 healthy volunteers randomly attributed to a training (TS) and a validation (VS) sample underwent complete ophthalmic examination, including Fourier-domain optical coherence tomography (FD-OCT). We acquired FD-OCT data centered at the optic disc (OD) and the macula. Two-dimensional (2D) projection images were computed and registered, to determine the distance between fovea and OD centers (FD) and their respective angle (FA). Retinal vessels were automatically segmented in the projection images and used to calculate the circumpapillary retinal vessel density (RVD) profile. Using the TS, a multivariate model was calculated for each of 256 sectors of the RNFL, including OD ratio, orientation and area, RVD, FD, FA, age, and refractive error. Model selection was based on Akaike Information Criteria. The compensation effect was determined for 12 clock hour sectors, comparing the coefficients of variation (CoV) of measured and model-compensated RNFL thicknesses. The model then was applied to the VS, and CoV was calculated. RESULTS The R value for the multivariate model was, on average 0.57 (max = 0.68). Compensation reduced the CoV on average by 18%, both for the TS and VS (up to 23% and 29%), respectively. CONCLUSIONS We have developed and validated a comprehensive multivariate model that may be used to create a narrower range of normative RNFL data, which could improve diagnostic separation between early glaucoma and healthy subjects. This, however, remains to be demonstrated in future studies.

Distinguishing Keratoconic Eyes and Healthy Eyes Using Ultrahigh-Resolution Optical Coherence Tomography–Based Corneal Epithelium Thickness Mapping

PURPOSE To find differences in epithelial thickness (ET) maps of eyes with keratoconus (KC) and healthy eyes. DESIGN Institutional cross-sectional study. METHODS In this study 40 keratoconic eyes and 76 healthy eyes were scanned using a custom-built ultrahigh-resolution optical coherence tomography system. Automated segmentation ET maps with 17 subsectors were calculated (central, temporal inferior, temporal superior, nasal inferior, and nasal superior area). The thinnest point of the epithelium (minET), the thickest point of the epithelium (maxET), and the thinnest point diagonally opposing the thickest point (ETmax/op) were additional parameters. Ratios were calculated as follows: minET/diagonally opposing point (R1), maxET/diagonally opposing point (R2), inferior temporal area/superior nasal area (RTI/NS), and inferior/superior hemisphere (RI/S). Furthermore, collected parameters were analyzed regarding their diagnostic accuracy (area under the curve; AUC). RESULTS Statistically significant differences were as follows: central ET, 46.25 ± 2.56/50.91 ± 1.66; minET, 38.50 ± 2.10/46.79 ± 1.27; ETmax/op, 47.14 ± 2.45/49.60 ± 1.57; temporal inferior area: 43.93 ± 2.95/51.04 ± 1.51 (all mean ± standard deviation, μm); R1, 0.76 ± 0.09/0.93 ± 0.04; R2, 1.08 ± 0.04/1.21 ± 0.16; RTI/NS, 0.85 ± 0.08/1.02 ± 0.04; RI/S: 0.92 ± 0.07/0.99 ± 0.02. AUC values were R1: 0.979 (confidence interval [CI]: 0.957-1.000), RTI/NS: 0.977 (CI: 0.951-1.000), and minET: 0.928 (CI: 0.880-0.977). CONCLUSIONS Epithelial thickness maps could clearly visualize different ET patterns. Parameters with the highest potential of diagnostic discrimination between eyes with KC and healthy eyes were, in descending order, R1, RTI/NS, and minET. Consequently, epithelial thickness irregularity and asymmetry seem to be the most promising diagnostic factor in terms of discriminating between keratoconic eyes and healthy eyes.

Can Cut-Off-Values for Tumor Size or Patient Age in Breast Ultrasound Reduce Unnecessary Biopsies or is it all About Bi-rads?– A Retrospective Analysis of 763 Biopsied T1-Sized Lesions

AIMS AND OBJECTIVES To assess whether it is possible to establish a size cut-off-value for sonographically visible breast lesions in a screening situation, under which it is justifiable to obviate a biopsy and to evaluate the grayscale characteristics of the identified lesions. MATERIALS AND METHODS Images of sonographically visible and biopsied breast lesions of 684 patients were retrospectively reviewed and assessed for the following parameters: size, shape, margin, lesion boundary, vascularity, patients age, side of breast, histological result, and initial BI-RADS category. Statistical analyses (t-test for independent variables, ROC analyses, binary logistic regression models, cross-tabulations, positive/negative predictive values) were performed using IBM SPSS (Version 21.0). RESULTS Of all 763 biopsied lesions, 223 (29.2%) showed a malignant histologic result, while 540 (70.8%) were benign. Although we did find a statistically significant correlation of malignancy and lesion size (p=0.031), it was not possible to define a cut-off value, under which it would be justifiable to obviate a biopsy in terms of sensitivity and specificity (AUC: 0.558) at any age. Lesions showing the characteristics of a round or oval shape, a sharp delineation and no echogenic rim (n=112) were benign with an NPV of 99.1%. CONCLUSION It is not possible to define a cut-off value for size or age, under which a biopsy of a sonographically visible breast lesion can be obviated in the screening situation. The combination of the 3 grayscale characteristics, shape (round or oval), margin (circumscribed) and no echogenic-rim sign, showed an NPV of 99.1%. Therefore, it seems appropriate to classify such lesions as BI-RADS 2.