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. Author manuscript; available in PMC: 2022 Oct 1.
Published in final edited form as: Exp Eye Res. 2021 Aug 8;211:108724. doi: 10.1016/j.exer.2021.108724

Strain by Virtual Extensometers and Video-imaging Optical Coherence Tomography as a Repeatable Metric for IOP-Induced Optic Nerve Head Deformations

Jihee Kim 1, Stuart K Gardiner 2, Andrea Ramazzotti 3, Udayakumar Karuppanan 3, Luigi Bruno 4, Christopher A Girkin 3, J Crawford Downs 3, Massimo A Fazio 1,3,5
PMCID: PMC8511063  NIHMSID: NIHMS1734787  PMID: 34375590

Abstract

Purpose.

To determine if in vivo strain response of the Optic Nerve Head (ONH) to IOP elevation visualized using Optical Coherence Tomography (OCT) video imaging and quantified using novel virtual extensometers was able to be provided repeatable measurements of tissue specific deformations.

Methods.

The ONHs of 5 eyes from 5 non-human primates (NHPs) were imaged by Spectralis OCT. A vertical and a horizontal B-scan of the ONH were continuously recorded for 60 seconds at 9Hz (video imaging mode) during IOP elevation from 10 to 30 mmHg. Imaging was repeated over three imaging sessions. The 2D normal strain was computed by template-matching digital image correlation using virtual extensometers. ANOVA F-test (F) was used to compare inter-eye, inter-session, and inter-tissue variability for the prelaminar, Bruch’s membrane opening (BMO), lamina cribrosa (LC) and choroidal regions (against variance the error term). F-test of the ratio between inter-eye to inter-session variability was used to test for strain repeatability across imaging sessions (FIS).

Results.

Variability of strain across imaging session (F=0.7263, p=0.4855) and scan orientation was not significant (F=1.053, p=0.3066). Inter session variability of strain was significantly lower than inter-eye variability (FIS=22.63, p=0.0428) and inter-tissue variability (FIS=99.33 p=0.00998). After IOP elevation, strain was highest in the choroid (−18.11%, p<0.001), followed by prelaminar tissue (−11.0%, p<0.001), LC (−3.79%, p<0.001), and relative change in BMO diameter (−0.57%, p=0.704).

Conclusions.

Virtual extensometers applied to video-OCT were sensitive to the eye-specific and tissue-specific mechanical response of the ONH to IOP and were repeatable across imaging sessions.

INTRODUCTION

Glaucomatous optic neuropathy (GON) is the leading cause of irreversible blindness and affects more than 70 million people worldwide.1 Glaucoma is characterized by the enlargement of the optic cup associated with loss of the retinal ganglion cells (RGCs) and their axons at the optic nerve head (ONH),2, 3 and subsequent remodeling of both the neural and connective tissues.4 Elevated intraocular pressure (IOP) is a primary risk factor for the development and progression of glaucoma.5-9 However, many individuals with glaucoma present with IOP in the normal range while others with high IOP never develop the disease.10 As such, the mechanisms linking IOP to neural damage remain unknown.

To explain multifactorial and controversial characteristics of GON, a number of hypotheses have been proposed based on mechanical, vascular, neurodegenerative, genetic, and biomechanical paradigms, although these potential contributors to glaucoma pathophysiology are inseparably intertwined.11-13 The biomechanical hypothesis suggests that strain and biomechanical properties of the ONH modulate the individual susceptibility of the eye to changes in IOP which may detrimentally interact with blood perfusion and cellular activity in this region.14,6, 8, 9

To date, several research groups have developed engineering tools to measure mechanical tissue strain by means of fast, high-resolution, and non-invasive 3D imaging modalities.15-19 However, most studies have focused on ex vivo, in vitro or computational approaches to estimate the influence of IOP on ONH biomechanics. Recently, several studies have aimed to measure IOP-induced strain in vivo11,19-21,43,11, 20, 21 and relate it to glaucoma severity22, 23. However, the repeatability of in vivo measurements of IOP-induced ONH mechanical strain has never been assessed, either within or across imaging sessions; hence, little is known on how much variability in the strain metrics themselves, which limits the studies of strain as a factor in glaucoma.

To fill this gap in knowledge, this study was designed to assess the repeatability of in vivo strain measurements in different regions of the ONH in response to acute IOP elevations. We quantified ONH mechanical strain using novel virtual extensometers as the deformation measurement method within an OCT B-scan through the ONH continuously acquired at 9Hz (video imaging mode). The purpose of this study was to determine if video OCT imaging would be sensitive to IOP induced strain, would provide repeatable results and can resolve differences in strain across the different tissues of the ONH.

METHODS

Each eye was imaged over three imaging sessions that were at least two weeks apart to investigate the in vivo structural response of the ONH and peripapillary tissue to elevated IOP across imaging sessions. For the sake of simplicity, from now on, we will refer to the peripapillary and proper ONH region as the ONH region. Five eyes from five adult male Rhesus macaque nonhuman primates (NHPs) were used for this study. All animals were treated in accordance with the ARVO guidelines for the use of animals in ophthalmic and vision research under procedures approved by the University of Alabama at Birmingham’s Institutional Animal Care and Use Committee.

Imaging of the ONH

Imaging of the ONH was performed by spectral-domain optical coherence tomography (SD-OCT). Specifically, tomographic B-scans of the ONH were taken with an SD-OCT (Spectralis HRA-OCT, Heidelberg Engineering; Heidelberg, Germany), equipped with custom software that allowed for continuously acquiring and exporting sequential B-scan images (video imaging mode) of a single section through the ONH (version SP-X1701, custom AQM). Vertical (superior-inferior) and horizontal (nasal-temporal) sections of the ONH were sequentially imaged in this fashion at an average of 5.8 Hz for 60s, hereafter referred to as a “video scan”. Each video scan consisted of 347 B-scans, and each B-scan consisted of 768 A-scans across a 30° field of view. Axial and lateral resolutions were 3.87μm and 6μm, respectively. To enhance visibility of the deep tissue, images were acquired in enhanced depth imaging mode (EDI)24 and active Automatic Real-time Tracking (ART) of 5 scans per final B-scan. The center of the vertical scans was positioned as close to the center of the ONH as possible while avoiding the central retina vasculature to reduce shadowing effects on the central lamina. Vertical orientation would be adjusted to be visually perpendicular to the fovea-Bruch’s Membrane Opening (BMO) axis. The horizontal scan was oriented 90° to the vertical scan, visually matching the orientation of the fovea-BMO axis orientation. The native follow-up imaging modality of the Spectralis was used to image the same sections of each ONH as IOP varied and across imaging sessions.

Acute IOP elevation

At the beginning of each imaging session, each monkey was anesthetized with an intramuscular injection of ketamine (3 mg/kg) and dexmedetomidine (50 mcg/kg), then intubated and placed under isoflurane gas anesthesia (1.5-3%) for maintenance. The imaged eye was cannulated with a 27-gauge needle placed through the cornea into the anterior chamber at the limbus connected via sterile infusion set and a three-way stopcock to two manometer bottles of sterile phosphate-buffered saline (PBS), with the calibrated height of one reservoir set at 10 mmHg and the other at 30 mmHg.25 To induce mechanical deformations in the ONH associated with IOP, the infusion bottle height was set so as to induce an IOP value of 10 mmHg in the imaged eye, as measured by calibrated bottle height. IOP was held steady for at least 2 minutes to allow for the eye to adjust to a baseline IOP of 10 mmHg. Once the eye stabilized, the OCT video acquisition was started. A few seconds after the video started, the stopcock was switched to the 30-mmHg-height reservoir and the video was acquired for 60 seconds, which was the maximum video length achievable at 5.8 Hz (ART of 5 B-scans) with the current acquisition software of the Spectralis. In our previous studies using this acute IOP increase protocol26-28, it was observed that an acute IOP change from 10 to 30 mmHg IOP would stabilize after only a few seconds after switching from the 10 to 30 mmHg IOP reservoir. Deformations on the live image of the B-scan were visually noticeable after a few seconds (video provided as an Appendix).

Image pre-processing

Any in vivo OCT scan is affected by axial and lateral motions resulting from cardiac pulse, respiration, and eye motion from micro-saccades and loss of fixation.29 Motion in OCT images can compromise accurate quantification of the ONH deformations. To minimize strain artifact from motion, all B-scans of a given video scan were registered to each other by custom software using intensity-based image registration.30 Deep ONH tissue visibility was enhanced by light-attenuation compensation techniques.31-33

Mechanical deformations computation

To quantify regional variations in the ONH mechanical strain with acute IOP elevation, we used virtual extensometers and virtual strain gauges to track the relative displacement across B-scans of reference grid points within different tissues of the ONH (Figure. 1A). Strain computation was entirely performed using commercial software (Davis v.10, LaVision; Göttingen, Germany). Virtual extensometers and strain gauges are a native tool in Davis v.10 and higher.

Figure 1.

Figure 1.

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2D normal strain change over time (A) Strain maps for a sample of B-scans constituting the 60-second OCT video scan. IOP increased from 10 mmHg to 30 mmHg within 2 seconds from the video acquisition start. The first B-scan was used as a reference scan for the cumulative strain computation. (B) Strain change over time as measured by the virtual extensometers and strain gauges placed in the different tissues of the optic nerve head section (and peripapillary region) represented by the reference B-scan. (For graphical representation, a best-fit cubic polynomial function is superimposed onto the strain traces). Full-length videos provided as Supplementary Video S1 and S2.

Tracking local strain vs. time.

Deformation of the ONH tissues caused by increasing IOP is highly nonlinear34-38 and approximately follows a cubic polynomial trend as previously reported39 and as shown in Figure. 1B. In order to track local deformations, digital image correlation (DIC) algorithms maximize the cross-correlation value between a reference and a target subset of pixels of the reference and target B-scan images, respectively. Correlation kernel size in DIC is an important parameter considering that deformations are assumed to be homogeneous inside a given correlation subset, hence, small correlation kernels are required to resolve high gradients of deformation. Similarly, small correlation kernels yield lower correlation values, which affect strain tracking accuracy; so, optimal kernel size should be assessed iteratively depending on image noise and estimated strain gradient.40, 41 The value of the correlation function expresses matching quality between the reference and target image subsets and provide a quantitative metric for quality of the deformation tracking process.42

Large deformations and high strain gradients are common in the regions of the ONH with a large vascular component due to the high compressibility of vasculature tissue, as noticed previously in volumetric OCT scans20 and as visible in the prelaminar and choroidal regions shown in Figure 1A. In this study, tissue deformations were calculated by computing the differential deformations occurring between successive B-scan images and by adding them cumulatively; hence, the reported strain values correspond to the amount of deformations accumulated by the tissue during the duration of the video scan (60 seconds). By using the sum of differential increase of strain in between B-scans (Figure 2), the virtual extensometers and strain gauges can follow large tissue deformations without loss in the cross-correlation values of the correlation kernels; see a comparison between the sum of differential increase approach compared to computing strain from the difference between the first and last B-scan of the video (Figure 2).42 In Figure 2, for the same video scan, cross-correlation value maps show how local correlation values in the prelaminar and choroidal regions are much lower when strain is computed as the difference between the first and last B-scan (left column) compared to strain computed as the sum of the incremental increase in strain with each successive B-scan (right column); correlation maps are also shown for various sizes of the correlation kernel for both approaches.

Figure 2.

Figure 2.

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Cross-correlation value maps as they vary with correlation kernel size (by row) and computational approach for deformation tracking (by column). Left column: Cross-correlation value maps with strain computed as a difference between the first and last B-scan of the video sequence. Right column: cross-correlation maps with strain computed as a sum of the differential increase between sequential B-Scans. Variability of the correlation maps at different kernel sizes is shown by row. The comparison shows an example of how the sum of differential method allows for tracking large deformations without loss of correlation even for a small kernel size. Correlation values lower than 0.7 are shown as transparent.

Virtual extensometers and strain gauges.

A virtual extensometer provides a one-dimensional measure of strain computed as the relative change in length between the extensometer’s terminal points divided by the extensometer length. The change in the positions of each terminal point is obtained by tracking the centroids of a 31x31 pixel-wide subset (correlation kernel) in each B-scan. We will refer to the measure of strain provided by the virtual extensometer as “normal strain” or, for the sake of simplicity, as “strain”.

A virtual strain gauge provides a two-dimensional measure of the strain tensor; minimum normal strain – the largest negative eigenvalue of the strain tensor – was used as a metric for mechanical deformations for the strain gauge. Pixel size of each strain gauge varied depending on sampling location; the size of each stain gauge is provided for each sampling location. We will refer to the measure of normal strain provided by the strain gauges as “strain”.

Morphology and density of the peripapillary ONH considerably vary regionally and across tissues.20, 43-45 Therefore, the different regions of the ONH experience different amounts of strain when IOP varies.46 To account for tissue-dependent strain variability, virtual extensometers and strain gauges were placed in four tissues of the ONH: prelaminar tissue, Bruch’s membrane opening (BMO), laterally in the lamina cribrosa (LC), and choroid. Graphical visualization and locations of the virtual extensometers and strain gauges are shown in Figure. 3.

Figure 3.

Figure 3.

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Locations of strain measuring virtual extensometers and strain gauges placed on a vertical B-scan. (A) Representative vertical B-scan of one eye. The location of the scan is shown by the green line overlaid on the en face view of the fundus (bottom left). (B) Manually segmented tissues of the ONH: blue, prelaminar tissues (PLT); pink, lamina cribrosa (LC); purple, retina; gold, choroid; (C) Delineation of virtual extensometers and strain gauges. Yellow line shows the location of the extensometer measuring the change in relative distance between BMO points. Blue line indicates the location of an extensometer that measured thinning of the prelaminar tissue at its shallowest point in the B-scan. Green and brown boxes show the location of the strain gauges in the left and right choroidal regions; red box for the strain gauge placed in the central laminar cribrosa. In each strain gauge, two arrows inside the rectangle represented the internal coordinate system of the gauge.

Disk margin strain.

Relative change of the disk margin’s diameter (normal strain) was measured by an extensometer whose terminal points connected the opposing BMO points visible in the horizontal and vertical B-scans (Figure. 3C, yellow-colored line).

Prelaminar tissue strain.

Normal strain of the prelaminar tissue was measured as the relative change in length of an extensometer whose terminal points connect the center of the visible anterior lamina cribrosa surface (ALCS) and the shallowest point visible in the B-scan of the internal limiting membrane (ILM; Figure. 3C, blue line). The center of ALCS was defined as the point on the ALCS intersecting a parallel line drawn from the midpoint of BMO in a given scan.

LC strain.

Normal strain in the lamina was measured by a strain gauge placed in the portion of the LC proximal to the BMO center. The strain gauge was of rectangular shape, 100 pixels wide and 20 pixels high (387 x 77 μm). (Figure. 3C, red-colored box); height of the strain gauge was chosen to approximately match the visible thickness of the hyper-reflective band that is commonly referred to as laminar thickness in OCT images; width was chosen such that most of the central region of the lamina (with the minimal amount of overhanging prelaminar tissue) was covered by the strain gauge. In a qualitative sensitivity analysis, no appreciable dependence of the strain values on the strain gauge size was observed.

Choroidal strain.

Normal strain in the choroid was measured by two strain gauges were placed in the choroidal region between the Bruch’s membrane and the anterior sclera surface. Notice that the choroid in NHPs is thinner than in humans.47 The strain gauges were placed at 750 μm from the BMO center; this region of the choroid was shown to have a more uniform thickness than the region closer to the neural canal.48 The size of the strain gauge was set as a rectangle of 120 x 10 pixels (465 x 40μm; Figure. 3C, green-colored box, and brown-colored box); height was chosen to approximately match the visible thickness of the choroid. No appreciable dependence of the strain values on the strain gauge width was observed when the width of the strain gauge was broad enough to also include areas of the choroid not affected by shadowing from the overhanging vessels.

DIC performance evaluation.

An extensive quantitative assessment on the performance of the DIC method used in the study is reported the supplementary material. In S1 we computed as evaluation of the DIC displacement error by comparing computed vs. artificially deformed images (theoretical displacement field). In S2, we evaluated the performance of the incremental strain computation method used in this study compared to a non-incremental (first image as reference) strain computation method by computing the displacement error against artificially deformed images, when the magnitude of the artificial deformations increases over time (to emulate the large cumulative deformations as occurring in the choroidal tissue of the ONH). In S3 we propose a comparative evaluation of the tissue-specific strain from the actual video-OCT data of this study.

Statistical Analysis

Inter-eye, inter-session, and regional variations of mechanical strain were analyzed by analysis of variance (ANOVA) of a linear regression model in R 3.5.3 (R Foundation, Vienna, Austria) with normal strain as the dependent variable and eye-ID (NHP1, NHP2, etc.), tissue (prelaminar tissue, choroid, LC, and BMO), imaging session (1, 2, and 3) and scan orientation (vertical or horizontal) as categorical independent variables.

Error variance.

A standard F-test was used to determine whether the variability in strain between eyes and/or between tissues and scan orientation was significantly higher than the variability of the regression model residual variance (error variance).

Inter-session variance.

A standard F-test would not provide a comparative metric of the variability of each parameter against their variability across imaging sessions (inter-session variance, i.e., their repeatability with follow-up imaging). To fill this gap, we computed an F-test as the ratio of the between-sample variance of the parameter of interest to the intersession variance (FIS) of the same parameter (inter-session variance). As formulated, FIS provides a concise metric for intersession repeatability of a given strain metric.

The F-test computed against the variance of the regression model residuals (standard ANOVA) and the F-test computed against the intersession strain variance will be abbreviated as F and FIS, respectively.

A p-value of 0.05 or smaller was considered significant.

RESULTS

Changes in strain over time following a rapid elevation in IOP from 10 to 30 mmHg, as measured in both horizontal and vertical OCT B-scans through the ONH and peripapillary region of one NHP, are shown in Figure 1. The strain trend in both scans shows how the magnitude of normal strain rapidly increased with acquisition time (and IOP increase); negative values of normal strain indicate a strong predominance of compressive strain in the ONH tissues with increasing IOP. Strain distributions for each eye, tissue, imaging session, and imaging session stratified by tissue are shown in Figure 4.

Figure 4.

Figure 4.

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The strain distribution computed by virtual extensometers is influenced by three regression parameters: eye, image session, and tissue. When the IOP changes from 10 to 30 mmHg, (A) shows the change in the individual eye's global tissue strain changes. (B) shows the strain change in different tissues of five eyes. (C) shows the distribution of the global tissue strain in five eyes as the measurement time changes. (D) shows the tissue-specific strain variation across imaging sessions in the five eyes.

The strain variation depending on the regression parameters of eye, tissue, imaging session and scan orientation were analyzed by ANOVA (Table 1). Strain metrics significantly varied across eyes (p<0.001) but did not significantly vary with scan orientation (p=0.3066) or imaging session (p=0.4855) as regression parameters Distribution of global strain (non-specific to scan orientation) across the different tissues of the ONH is reported in Table 2 (Global strain). The ANOVA of strain variation with scan orientation stratified by tissue is reported in Table 2. The ANOVA of strain variation across imaging sessions (inter-session variance) and stratified by scan orientation is shown in Table 3.

Table 1.

ANOVA reporting the variation in strain across eyes, tissues, imaging sessions and scan orientations, as computed by virtual extensometers. F-statistics and p-values computed against the variance of the residual term. That error variance is from the regression parameters.

Parameter DoF SS MS F-value p-value
Eye 4 0.06427 0.01607 16.44 <0.001
Tissue 3 0.21156 0.07052 72.15 <0.001
Imaging Session 2 0.00142 0.00071 0.7263 0.4855
Scan Orientation 1 0.00103 0.00103 1.053 0.3066
Residual 139 0.13586 0.00098
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DoF: Degree of Freedom, SS: Sum of Squares, MS: Mean Square

Table 2.

Distribution of the global regional strain and with scan orientation across the different tissues of the optic nerve head and their ANOVA, as computed by virtual extensometers. (*** p<0.001)

Error Variance
Tissue Global Strain (%) Variation with Scan Orientation
(Mean ± SD) MS MS F p-value
BMO diameter −0.57 ± 1.05 0. 0006273 0. 0006273 37.67 <0.001*
Choroid −18.11 ± 12.73 0.004095 0.004095 4.598 0.03669*
LC −3.79 ± 2.72 0.0009855 0.0009855 1.976 0.1738
Prelamina −11.00 ± 4.24 0.01119 0.01119 9.819 0.004831*
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SD: Standard Deviation

Table 3.

ANOVA of the variability in strain across, stratified by tissue and scan orientation. F-statistics and p-values are computed against the inter-session variance (FIS), as computed by virtual extensometers.

Inter-session Variance
Scan Orientation
Tissue Horizontal scan (H.) Vertical scan (V.)
MS F IS p-value MS F IS p-value
BMO diameter 0.0000203 0.3843 0.1889 0.0000100 1.620 0.4161
Choroid 0.0068937 13.10 0.0722 0.0079777 25.49 0.0381*
LC 0.0014892 3.703 0.2238 0.0032436 81.53 0.0122*
Prelaminar 0.0046818 14.44 0.0658 0.0061833 36.94 0.0265*
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MS: Mean Square

Variability of strain across tissues.

Global regional strain for each tissue is reported in Table 2. Strain significantly varied with scan orientation (i.e., strain varied with sampling location) for BMO (F=37.67, p<0.001), choroid (F=4.598, p=0. 03669), prelaminar tissue (F=9.819, p=0.004831), but not for the lamina (F=1.976, p=0.1738).

Inter-session and inter-eye strain variability.

Strain variability across imaging sessions stratified by tissue and scan orientation is shown in Table 3. BMO diameter strain variability across eyes (inter-eye variability) was not larger than its variability across imaging session (inter-session variance) in the horizontal scan (H: FIS=0.3843, p=0.1889); inter-eye variability was larger than inter-session variability in the vertical scan (V: FIS=1.620), but it was not significant (V: p=0.4161).

Inter-eye variability of strain in the choroid was larger than its variability across imaging sessions in the horizontal scan and approaching significance (H: FIS=13.10, p=0.0722); much larger and significant F-ratio was observed in the vertical scan (V: FIS=25.49, p=0.0381). Notice that strain in the choroid significantly varied with scan orientation (p=0.03669), as shown in Table 2.

For the LC, intersession strain variability was not significant (H: FIS=3.703, p=0.2238) in the horizontal scan, but was significant (V: FIS=81.53, p=0.0122) in the vertical scan. Note that strain variability with scan orientation in the LC was not significant (p=0.1738), as shown in Table 2.

For the prelaminar neural tissue, inter-session variability was significant in the vertical scan (V: FIS=36.94 p=0.0265) and approached significance in the horizontal scan (H: FIS=14.44, p=0.0658). Strain variation with scan orientation in the prelaminar neural tissues was significant (p=0.004831), as shown in Table 2.

DISCUSSION

This study is the first step in identifying repeatable metrics of the mechanical response of the ONH and peripapillary tissues to acute IOP elevations as measured in vivo by OCT. To the best of our knowledge, this is the first study to evaluate repeatability of in vivo strain by OCT. This study suggests that video-mode imaging by Spectralis SD-OCT, combined with mechanical strain computed by virtual extensometers and virtual strain gauges, can provide a repeatable metric of strain that is sensitive to acute changes in IOP and can resolve differences in strain with various anatomical regions of the ONH.

Individual-specific biomechanical properties of the ONH have been hypothesized to play an important role in individual-specific susceptibility to IOP in glaucoma.4, 8, 9, 13, 49-53 Considering the focal nature of glaucomatous damage, mechanical strain metrics sensitive to local tissue-specific mechanical deformations in the peripapillary ONH could serve as eye-specific biomarkers of glaucoma risk and/or damage. Several cross-sectional studies have shown associations between both mechanical strain and IOP45, and mechanical strain and glaucoma severity, with greater change in LC depth associated with worse mean deviation (MD) and visual field index (VFI).54 However, to assess individual-specific ONH deformations to IOP, deformation metrics must be sensitive to the inter-subject variability in ONH mechanical response in the face of intersession variability to allow for longitudinal follow-up imaging. In other words, strain measure precision (repeatability) could be of more importance than measurement accuracy, considering that is still open to investigation understanding which aspects of the strain components and magnitude affect/cause neural damage or collagen tissue remodeling55.

In this study, we conducted an extensive analysis of the in vivo strain variability in the ONH tissues with IOP and imaging sessions, which provided several notable results.

First, following an acute IOP elevation from 10 to 30 mmHg, choroid and prelaminar tissue showed important and significant normal compressive strains (−18.11% and −11.00%); compressive strain was also significant in the LC (−3.79%) and BMO diameter became shorter with IOP elevation (Table 2, Figure. 4C).

Second, the tissue-specific metrics of strain were repeatable across imaging sessions (Table 3, Figure. 4B), except for BMO. BMO strain was very low on average (−0.57%, Table 2) so its variation between eyes was not larger than random variations in strain across imaging sessions (Table 3). This outcome is indicative that BMO diameter within the OCT B-scan as a metric of disk size is repeatable even for large changes in IOP (up to 20 mmHg).

Third, the strain quantification method was sensitive to the eye-specific mechanical response to an IOP increase from 10 to 30mmHg (Figure. 4A).

Overall, notable results are that compressive strain in the prelaminar neural tissue was three times higher than the strain in the LC. It is important to notice that region-dependent high compressive strain in the ONH has been observed before by Pavlatos et al.56 The large difference in strain between the prelaminar and laminar tissues is suggestive of large shear forces at the transition between these tissues, which could underlie some portion of the axon transport blockade that is known to occur in this region of the ONH with elevated IOP and glaucoma57. Axon transport blockade has been recently shown to be measurable in a mouse glaucoma model in vivo, so studies showing a direct association between mechanical strain and axonal blockage are possible.58 The high strains observed in the choroid indicate a large degree of choroidal compression with acute IOP increase. Considering that the choroid is the primary supplier of blood and nutrients to the outer retina, significant compression of its plexuses, if chronic, may affect retinal function over time. Moreover, changes in choroidal thickness have been proposed as a possible cause of prelaminar neural tissue deformation59.

The results of this study should be viewed with the following limitations in mind. First, since the ONH is not perfectly symmetrical in geometry, material properties or mechanical response, and any out-of-plane motion of the tissue cannot be tracked by the 2D video-mode imaging as performed in this study, out-of-plane deformations of the tissue may occur undetected during IOP elevation. Hence, it is possible that a significant amount of strain is either not represented or artefactually caused by out-of-plane tissue motion. While the magnitude of this potential error is currently under investigation by comparing 2D video metrics against digital volume correlation,20, 60, 61 strain measures reported herein has been proven to be highly repeatable while sensitive to eye-specific variations in the ONH strain caused by IOP. Considering that it is unclear which aspects of ONH mechanical deformation are associated with tissue remodeling or neurodegeneration in glaucoma, one could argue that accuracy of strain metrics is of less primary importance compared to measurement repeatability and sensitivity to individual-specific biomechanics as investigated in this study. Also, the video imaging is capable of following large deformations thanks to its high and adjustable temporal resolution (variable frame rate) and high spatial resolution (small correlation kernel size) without loss of deformation tracking correlation values, as shown in Figure 2.

Second, In order to minimize the influence of vessel shadowing on the strain computation, B-scan images were preprocessed with adaptive light-attenuation compensation techniques31-33; however, pulsatile motion of the central retinal vessel likely introduced artificial deformations in the strain metrics reported herein. Motion artifacts are likely to inflate the random component of the strain variability and are therefore accounted for in the random error term (residual) of the regression models used in the ANOVAs. The high compressibility of the choroid and prelaminar neural tissue is likely driven by the much higher compressibility of vasculature present in these regions compared to primarily collagenous tissues like the LC. Estimation of vessel locations by OCT angiography in video scans is currently under development and will be utilized in future studies to account for vascular motion and compressibility, which should increase measurement repeatability and provide more accurate deformation metrics.

Third, NHP eyes were used for this study, which may not perfectly translate to human patients, as morphological differences are likely to affect the reported strain metrics and their repeatability. That said, of all animal models, the ONH anatomy and laminar structure in NHPs most closely resemble that in human eyes, and the central retinal vascular trunk is of similar size and shape. Comparative studies of the ONH mechanical response to IOP of the NHP human ONHs are underway.

Fourth, video scans were acquired along two preferential sections, one along the superior-inferior (vertical) and another along the nasal-temporal (horizontal) axes in separate sessions acquired one after the other. A more recent version of the Spectralis OCT acquisition module supports acquisition of four equally spaced radial scans simultaneously, allowing for the reported strain metrics to be acquired in eight regions around the ONH in one video-scan session. At the time imaging for this study was performed, the multi-axial acquisition mode was not yet available, so we had to limit the acquisition to two scans only in order to limit the time the NHPs were under anesthesia. Acquiring four simultaneous B-scans in one session could provide higher repeatability and higher inter-individual variability of the strain metrics.

Fifth, positioning of the B-scans was performed manually, which may have increased inter-eye variability of the strain metrics. Spectralis acquisition software now allows automatic centering and positioning of the B-scans by means of a proprietary algorithm (named Anatomic Positioning System, or APS) that looks at the position of the BMO centroid and fovea. APS positioning was not available at data collection for this study.

Lastly, the NHP ONHs were imaged at least two weeks apart under tightly-controlled experimental conditions with precise IOP control that are likely to increase measurement repeatability. For the purposes of this study, controlled imaging conditions are desirable when environmental factors influencing mechanical strain should be minimized, however, the approach may be more limited when deployed in a clinical setting.

In conclusion, this study demonstrated a repeatable tissue strain measurement approach based on in vivo imaging to quantify the ONH mechanical response to acute IOP elevation. Video mode OCT imaging demonstrated sensitivity to IOP induced deformation and regional variation in strain across mechanistically relevant tissue compartments of the ONH. Quantification of the repeatability of tissue deformations does not only enhance the understanding of the susceptibility of ocular tissue to IOP but also provides a foundation for investigating structural and mechanical biomarkers that predict and detect neural damage and strain-driven tissue remodeling in glaucoma.

Supplementary Material

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Highlights.

  • This study presents the first assessment of IOP-induced ONH tissue deformation metrics repeatability.

  • The study presents a novel approach to measure soft tissue mechanical deformations based on virtual extensometers and strain gauges.

  • Mechanical strain as measured by virtual extensometers is a repeatable metric of the ONH response to acute IOP elevations.

  • Choroid and prelaminar tissue showed threefold the magnitude of compressive strain measured at the lamina cribrosa.

Financial support:

NIH Grants R01-EY026574 (MAF, CAG); R01-EY026035 (JCD); R01-EY028284 (CAG, MAF)

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of Interest: No conflicts of interest or financial relationships pertinent to this study exist for any of the authors.

Hardware support was provided by Heidelberg Engineering, Inc. to MAF and CAG.

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