Faculty Bibliography

BACKGROUND:Spectral-domain (SD-) optical coherence tomography (OCT) can reliably measure axonal (peripapillary retinal nerve fiber layer [pRNFL]) and neuronal (macular ganglion cell + inner plexiform layer [GCIPL]) thinning in the retina. Measurements from 2 commonly used SD-OCT devices are often pooled together in multiple sclerosis (MS) studies and clinical trials despite software and segmentation algorithm differences; however, individual pRNFL and GCIPL thickness measurements are not interchangeable between devices. In some circumstances, such as in the absence of a consistent OCT segmentation algorithm across platforms, a conversion equation to transform measurements between devices may be useful to facilitate pooling of data. The availability of normative data for SD-OCT measurements is limited by the lack of a large representative world-wide sample across various ages and ethnicities. Larger international studies that evaluate the effects of age, sex, and race/ethnicity on SD-OCT measurements in healthy control participants are needed to provide normative values that reflect these demographic subgroups to provide comparisons to MS retinal degeneration. METHODS:Participants were part of an 11-site collaboration within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. SD-OCT was performed by a trained technician for healthy control subjects using Spectralis or Cirrus SD-OCT devices. Peripapillary pRNFL and GCIPL thicknesses were measured on one or both devices. Automated segmentation protocols, in conjunction with manual inspection and correction of lines delineating retinal layers, were used. A conversion equation was developed using structural equation modeling, accounting for clustering, with healthy control data from one site where participants were scanned on both devices on the same day. Normative values were evaluated, with the entire cohort, for pRNFL and GCIPL thicknesses for each decade of age, by sex, and across racial groups using generalized estimating equation (GEE) models, accounting for clustering and adjusting for within-patient, intereye correlations. Change-point analyses were performed to determine at what age pRNFL and GCIPL thicknesses exhibit accelerated rates of decline. RESULTS:The healthy control cohort (n = 546) was 54% male and had a wide distribution of ages, ranging from 18 to 87 years, with a mean (SD) age of 39.3 (14.6) years. Based on 346 control participants at a single site, the conversion equation for pRNFL was Cirrus = -5.0 + (1.0 × Spectralis global value). Based on 228 controls, the equation for GCIPL was Cirrus = -4.5 + (0.9 × Spectralis global value). Standard error was 0.02 for both equations. After the age of 40 years, there was a decline of -2.4 μm per decade in pRNFL thickness ( P < 0.001, GEE models adjusting for sex, race, and country) and -1.4 μm per decade in GCIPL thickness ( P < 0.001). There was a small difference in pRNFL thickness based on sex, with female participants having slightly higher thickness (2.6 μm, P = 0.003). There was no association between GCIPL thickness and sex. Likewise, there was no association between race/ethnicity and pRNFL or GCIPL thicknesses. CONCLUSIONS:A conversion factor may be required when using data that are derived between different SD-OCT platforms in clinical trials and observational studies; this is particularly true for smaller cross-sectional studies or when a consistent segmentation algorithm is not available. The above conversion equations can be used when pooling data from Spectralis and Cirrus SD-OCT devices for pRNFL and GCIPL thicknesses. A faster decline in retinal thickness may occur after the age of 40 years, even in the absence of significant differences across racial groups.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Recent studies have suggested that inter-eye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell+inner plexiform (GCIPL) thickness by spectral-domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. METHODS:Participants were from 11 sites within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with history of ON among PwMS. ROC curve analysis was performed on a training dataset (2/3 of cohort), then applied to a testing dataset (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT. RESULTS:Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs. controls. This composite score performed best, with AUC=0.89 (95% CI 0.85, 0.93), sensitivity=81% and specificity=80%. The composite score ROC curve performed better than any of the individual measures from the model (p<0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC=0.77, 95% CI 0.71,0.83, sensitivity=68%, specificity=77%). SVM analysis performed comparably to standard logistic regression models. CONCLUSIONS:A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with history of unilateral ON. SVM performed as well as standard statistical models for these classifications. CLASSIFICATION OF EVIDENCE/METHODS:The study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared to clinical criteria.

Purpose : The ability to detect progression in eyes with advanced glaucoma is challenging because of known limitations of commonly used structural and functional parameters reaching their minimal measurable limit (floor effect) or increased measurement variability. We examined the ability of inner nuclear layer (INL) thickness measurements to demonstrate change longitudinally in eyes with early and advanced severity glaucoma. Methods : Subjects with glaucoma and >=4 visits were included in the study. Subjects in the ?Early/Moderate? group (EG) had average circumpapillary retinal nerve fiber layer (cRNFL) thicknesses >=60mum and subjects in the ?Advanced? group (AG) had average cRNFL thicknesses <=60mum. All subjects had comprehensive ophthalmic examination, Humphrey visual field (Zeiss, Dublin, CA) testing, and spectral-domain OCT (Cirrus HD-OCT; Zeiss) optic nerve head (ONH) and macula scans. Segmentation of the INL was performed using the Iowa Reference Algorithms (Retinal Image Analysis Lab, Iowa Institute for Biomedical Imaging, Iowa City, IA) and segmentation errors were manually corrected by a trained grader. Overall INL thickness along with the superior and inferior hemifields were used for analysis. Rates of progression were estimated from longitudinal OCT and visual field (VF) data using mixed effects models adjusting for baseline age, follow-up duration, and signal strength at each visit. Results : 23 eyes (23 subjects), 12 with EG and 11 with AG, were included in the study. At baseline, a statistically significant difference between groups was detected in MD, cRNFL, and GCIPL thicknesses (Table 1). In EG eyes, the rate of change was significantly different than a zero slope for cRNFL thickness, C:D ratio, and GCIPL thickness (Table 2). Inferior INL thickness was the only INL parameter showing significant rate of change. However, in the advanced group, all parameters (including both global and sectoral INL thicknesses) showed significant rate of change except for the cRNFL. Conclusions : Longitudinal measurements of INL thickness may be useful for following disease progression in subjects with advanced-stage glaucoma where cRNFL thickness is no longer useful

Purpose : Deep learning of optical coherence tomography (OCT) may help discriminate glaucomatous eyes from healthy controls. However, the underlying decision making processes remain unclear. Recently, through computing class activation maps, our feature agnostic artificial intelligence of OCT images using a 3D convolutional neural network identified the optic nerve head (ONH) and its surrounding regions as structures significantly associated with glaucoma classification (PMID: 31260494). To pursue their contributions further, here we analyzed the optic nerve morphology from OCT and magnetic resonance imaging (MRI) in a subset of glaucoma and healthy subjects. Methods : Nine early glaucoma, 12 advanced glaucoma, and 4 healthy control subjects underwent spectral-domain OCT at 30x30x2 mum³ and 3-Tesla anatomical MRI at 1x1x1mm³ . Maximum intensity projection was applied to en-face OCT scans at the ONH (Fig. 1). The areas of the ONH [inner regions of interest, (ROI)] and surrounding regions (outer ROIs) visible in OCT were measured using global thresholding in ImageJ. One-way ANOVAs with post-hoc Tukey's tests were performed on the inner and outer ROIs between the 3 groups. Also, a Pearson correlation analysis was performed between the ROI areas in OCT and optic nerve volume extracted from MRI between the eye and optic chiasm. Results : For OCT of the ONH, significant group effect was observed for the areas in the inner ROIs (ANOVA: F= 7.823, p=0.00133). Post-hoc analyses revealed a significant difference between healthy controls and advanced glaucoma (p=0.0082) and between early and advanced glaucoma (p=0.0057) but no significance between healthy controls and early glaucoma (p=0.80) (Fig. 2A). No significant group effect was observed in the outer ROIs (ANOVA: F=0.004, p=0.996) (Fig. 2B). There was a negative correlation between the inner ROI area in OCT and optic nerve volume in MRI (R=-0.47, p=0.0011) (Fig. 2C). Conclusions : The ONH tissues visible on OCT appeared to contribute more than their surrounding regions to distinguishing between glaucomatous eyes and healthy eyes. The negative correlation between ONH area in OCT and optic nerve volume in MRI suggested the need to further understand the interactions between ONH and deeper brain structures in glaucoma. (Figure Presented)

Purpose : Multiple sublayers of retina can be visualized with visible light (vis-) OCT.However, image quality can be compromised due to patient movement, cataracts, small pupil size, and light scattering causing haziness and variability in signal to noise ratio in individual A-scans and in entire B-scans.The purpose of this study was to examine the effect of conventional and deep neural network dehazing techniques on the visibility and quantitative assessment of retinal sub-layers on vis-OCT images. Methods : 9 healthy and 5 glaucoma subjects were scanned 3 times during one session.Scanning was done on the superior nasal side of para-foveal region,1.5 mm from the fovea with a 3D speckle reduction raster scanning protocol(3x3x1.6 mm with 8192x16x1024 samplings) using a prototype vis-OCT system.16 A-scan lines were averaged to reduce speckle noise.Gray-scale image dehazing guided by depth information and pretrained Dehazenet deep model following deep convolutional neural network with residual learning(DnCNN) were applied on original B-scans.Quality improvement were evaluated using quality index(QI) and contrast to noise ratio(CNR) on dehazed B-scans.For each subject, the dehazed B-scan of Dehazenet and DnCNN from a fixed location adjacent to the fovea were selected.The distances between each of 3 bright inner plexiform layers(IPL) and retinal pigment epithelium(RPE) sublayers were segmented manually for thickness measurements using a 8 A-scan averaged profile(Fig.).Coefficient of variations (CVs) were calculated to assess the measurement repeatability of the sublayers on original and dehazed B-scans. Results : Healthy and glaucoma subjects were age 45.67+/-11.7and 59.60+/-13.4(p=0.07,t-test),visual field mean deviation(MD)-1.55 to1.20 dB,and from -26.42 to -7.70dB(p= 0.003,Wilcoxon),global mean circumpapillary retinal nerve fiber layer(RNFL)thickness 96.33+/-12.20 and 59.80+/-9.09mm(p<0.001,Wilcoxon),respectively.Dehazed B-scans obtained by deep models have statistically significant better QI and CNR(Table1).Overall intra-subject CVs showed significantly improved reproducibility on all measured sub-layers of dehazed B-scans compared to original scans for all subjects(Tables 2,3). Conclusions : Vis-OCT image quality can be improved using deep neural network dehazing model resulting in higher reproducible thickness measurements of retinal sublayers within subjects in dehazed B-scans

Purpose : Automated segmentation of in-vivo lamina cribrosa (LC) has been challenging, owing to the complex 3D structure and decreased visibility in the lamina depth. Frangi's vesselness filter, which was originally developed for angiogram segmentation, have been successfully demonstrated in segmenting the ex-vivo LC from micro-CT and second harmonic generation microscopy images. In this project we are proposing a new approach of segmenting the in vivo LC from OCT scans, incorporating the Frangi's vesselness principle to facilitate in vivo LC image analysis in much greater detail compared to our previously described 3D analysis method. Methods : In-vivo spectral-domain OCT scans (Leica, Chicago, IL) were acquired from healthy non-human primates. Scans of varying degree of image quality were selected for the analysis and underwent automated brightness and local contrast enhancement. 3D Frangi's vesselness filter was applied using a fixed setting for scans of all qualities. Our previously described segmentation algorithm was then used to quantify the LC microstructure. The measurements generated from the Frangi analysis and from our own conventional method were compared with a standard reference (manually segmented LC by an expert). Paired t tests were performed to compare if the differences between standard reference and conventional method are greater than the differences between standard reference and Frangi analysis. The visibility of analyzable lamina and dice coefficient were also compared to the conventional method using the same test. Results : In vivo scans acquired from 5 rhesus macaques (3 males, 1 female, aged 4.3-10.7 yrs) were used for the analysis. No significant difference was detected for LC microstructure parameters between Frangi's approach and conventional method with respect to the standard reference, except for significantly higher pore count in Frangi's method (p=0.003; Table). Furthermore, visibility (Figure) was significantly higher for the Frangi method compared to the conventional approach (p<0.001) with no difference detected for the semantic segmentation, as reflected by the dice coefficient. Conclusions : The use of Frangi analysis substantially increase the analyzable lamina while providing similar quantification of the LC microstructure compared to our previous 3D analysis method. This improves the potential for automated and thorough volumetric analysis of in vivo OCT LC image

Purpose : The lamina cribrosa (LC) is hypothesized to be the site of initial axonal damage in glaucoma with the peripapillary retinal nerve fiber layer (RNFL) thickness is widely used as a standard metric for quantifying this damage. The purpose of this study was to determine in vivo changes in the microstructure of the LC in eyes of non-human primates (NHP) with naturally occurring RNFL thinning. Methods : Spectral-domain OCT scans (Leica, Chicago, IL) of the optic nerve head (ONH) were acquired in vivo from a colony of 50 adult rhesus monkeys, suspected of having high prevalence of naturally occurring glaucoma. The circumpapillary global and quadrant RNFL thickness was analyzed using a custom automated segmentation software. From the set of 100 eyes, the 10 eyes with the thinnest global RNFL values were selected as the study group, while 10 eyes with RNFL values around the 50 percentile were used as the control group. A previously described automated segmentation algorithm was used for LC microstructure analysis. The LC microstructure was analyzed globally and in the th following volumetric sectors: quadrants, central and peripheral lamina, and 3 depth slabs (anterior, middle, posterior; Figure). Beam thickness/pore diameter ratio (BPR) and connective tissue volume fraction (CTVF: beam volume/total volume) were calculated globally and in sectors. Results : 20 eyes (15 animals) were analyzed (Table 1). While no significant difference was detected between groups for age, weight or disc size, the study group had significantly thinner RNFL than the control group (p<0.01). The study group had significantly larger BPR and CTVF compared with the control group (Table 2). Significant sectoral differences between study and control group RNFL thickness were noted for BPR and CTVF in the nasal and temporal quadrants, central LC, and in LC depth. Across eyes, the global RNFL thickness was moderately negatively correlated only with the global CTVF (lower RNFL thickness associated with higher CTVF; r² =0.63, p=0.045). Conclusions : Eyes with thinner circumpapillary RNFL had thicker LC BPR and CTVF globally and in various sectors when compared to eyes with normal RNFL thickness. Whether these LC changes are the cause of RNFL damage or the result of remodeling of the LC requires further investigation. (Figure Presented)

Purpose:The lamina cribrosa (LC) is a leading target for initial glaucomatous damage. We investigated the in vivo microstructural deformation within the LC volume in response to acute IOP modulation while maintaining fixed intracranial pressure (ICP). Methods:In vivo optic nerve head (ONH) spectral-domain optical coherence tomography (OCT) scans (Leica, Chicago, IL, USA) were obtained from eight eyes of healthy adult rhesus macaques (7 animals; ages = 7.9-14.4 years) in different IOP settings and fixed ICP (8-12 mm Hg). IOP and ICP were controlled by cannulation of the anterior chamber and the lateral ventricle of the brain, respectively, connected to a gravity-controlled reservoir. ONH images were acquired at baseline IOP, 30 mm Hg (H1-IOP), and 40 to 50 mm Hg (H2-IOP). Scans were registered in 3D, and LC microstructure measurements were obtained from shared regions and depths. Results:Only half of the eyes exhibited LC beam-to-pore ratio (BPR) and microstructure deformations. The maximal BPR change location within the LC volume varied between eyes. BPR deformer eyes had a significantly higher baseline connective tissue volume fraction (CTVF) and lower pore aspect ratio (P = 0.03 and P = 0.04, respectively) compared to BPR non-deformer. In all eyes, the magnitude of BPR changes in the anterior surface was significantly different (either larger or smaller) from the maximal change within the LC (H1-IOP: P = 0.02 and H2-IOP: P = 0.004). Conclusions:The LC deforms unevenly throughout its depth in response to IOP modulation at fixed ICP. Therefore, analysis of merely the anterior LC surface microstructure will not fully capture the microstructure deformations within the LC. BPR deformer eyes have higher CTVF than BPR non-deformer eyes.

Purpose/UNASSIGNED:The lamina cribrosa (LC) has an important role in the pathophysiology of ocular diseases. The purpose of this study is to characterize in vivo, noninvasively, and in 3D the structure of the LC in healthy non-human primates (NHPs). Methods/UNASSIGNED:Spectral-domain optical coherence tomography (OCT; Leica, Chicago, IL) scans of the optic nerve head (ONH) were obtained from healthy adult rhesus macaques monkeys. Using a previously reported semi-automated segmentation algorithm, microstructure measurements were assessed in central and peripheral regions of an equal area, in quadrants and depth-wise. Linear mixed-effects models were used to compare parameters among regions, adjusting for visibility, age, analyzable depth, graded scan quality, disc area, and the correlation between eyes. Spearmen's rank correlation coefficients were calculated for assessing the association between the lamina's parameters. Results/UNASSIGNED:Sixteen eyes of 10 animals (7 males and 3 females; 9 OD, 7 OS) were analyzed with a mean age of 10.5 ± 2.1 years. The mean analyzable depth was 175 ± 37 µm, with average LC visibility of 25.4 ± 13.0% and average disc area of 2.67 ± 0.45mm2. Within this volume, an average of 74.9 ± 39.0 pores per eye were analyzed. The central region showed statistically significantly thicker beams than the periphery. The quadrant-based analysis showed significant differences between the superior and inferior quadrants. The anterior LC had smaller beams and pores than both middle and posterior lamina. Conclusions/UNASSIGNED:Our study provides in vivo microstructure details of NHP's LC to be used as the foundation for future studies. We demonstrated mostly small but statistically significant regional variations in LC microstructure that should be considered when comparing LC measurements.

Purpose/UNASSIGNED:Growing evidence suggests that dendrite retraction or degeneration in a subpopulation of the retinal ganglion cells (RGCs) may precede detectable soma abnormalities and RGC death in glaucoma. Visualization of the lamellar structure of the inner plexiform layer (IPL) could advance clinical management and fundamental understanding of glaucoma. We investigated whether visible-light optical coherence tomography (vis-OCT) could detect the difference in the IPL sublayer thicknesses between small cohorts of healthy and glaucomatous subjects. Method/UNASSIGNED:We imaged nine healthy and five glaucomatous subjects with vis-OCT. Four of the healthy subjects were scanned three times each in two separate visits, and five healthy and five glaucoma subjects were scanned three times during a single visit. IPL sublayers were manually segmented using averaged A-line profiles. Results/UNASSIGNED:The mean ages of glaucoma and healthy subjects are 59.6 ± 13.4 and 45.4 ± 14.4 years (P = 0.02.) The visual field mean deviations (MDs) are -26.4 to -7.7 dB in glaucoma patients and -1.6 to 1.1 dB in healthy subjects (P = 0.002). Median coefficients of variation (CVs) of intrasession repeatability for the entire IPL and three sublayers are 3.1%, 5.6%, 6.9%, and 5.6% in healthy subjects and 1.8%, 6.0%, 7.7%, and 6.2% in glaucoma patients, respectively. The mean IPL thicknesses are 36.2 ± 1.5 µm in glaucomatous and 40.1 ± 1.7 µm in healthy eyes (P = 0.003). Conclusions/UNASSIGNED:IPL sublayer analysis revealed that the middle sublayer could be responsible for the majority of IPL thinning in glaucoma. Vis-OCT quantified IPL sublayers with good repeatability in both glaucoma and healthy subjects.