Keywords

20.1 Introduction

Glaucoma is a chronic optic neuropathy characterised by progressive loss of retinal ganglion cells (RGCs) [1]. It is the leading cause of irreversible blindness in the world. A recent systematic review and meta-analysis estimated that the number of people aged between 40 and 80 years with glaucoma worldwide was 64.3 million, and that it would increase to 76.0 million in 2020 and 111.8 million in 2040 [2]. Another recent meta-analysis reported that the number of people blind due to glaucoma worldwide was 2.1 million and those visually impaired due to glaucoma was 4.2 million [3]. The meta-analysis also reported that between 1990 and 2010, the number of people blind due to glaucoma increased by 0.8 million or 62% and those visually impaired increased by 2.3 million or 83%, respectively [3]. As the loss of RGCs in glaucoma is irreversible, early detection of the disease is of utmost importance because appropriate treatment can prevent or slow disease progression and therefore, preserve vision.

Glaucoma is characterised by typical optic nerve head (ONH) and retinal nerve fiber layer (RNFL) changes with or without the presence of correlating visual field (VF) loss. Detection of VF loss by means of standard automated perimetry (SAP) has been one of the mainstays in the diagnosis and monitoring of glaucoma. However, the subjective nature of perimetry creates large variability in results, which make it difficult to reliably assess and monitor the disease. Structural changes in the ONH and RNFL often are visible before the appearance of detectable loss by SAP [4, 5]. The ability to detect structural loss at the level of the ONH and peripapillary RNFL is, therefore, fundamental in the diagnosis and management of glaucoma. Clinical examination of the ONH and RNFL is routinely done on a slit-lamp biomicroscope using a high powered convex lens. Although detecting glaucoma by this method is fairly simple in moderate and severe stages of the disease, identification of subtle ONH and RNFL changes in early glaucoma requires training and experience. Another method of detecting glaucoma is the evaluation of optic disc stereo-photographs. Both these methods, however, are subjective, and the agreement even among experts in detecting glaucomatous changes either clinically or on disc photographs is far from excellent [6, 7]. The other major drawback of these methods is that changes can only be qualitatively assessed but cannot be quantified. These limitations are overcome by optical coherence tomography (OCT) imaging of ONH and RNFL. OCT provides objective assessment of structural changes in glaucoma by reproducibly quantifying the changes in ONH and RNFL.

OCT technology has evolved from time-domain (TD-) to spectral-domain technology (SD-OCT) which is faster and provides greater resolution (axial resolution of 5 μm). As a result of improved resolution, it is now possible to evaluate each layer of the retina separately using SD-OCT. This has led to a renewed interest in evaluating the macular changes in glaucoma [8]. Macula contains approximately 50% of all RGCs. RGC layer in the macula contains seven layers of RGC bodies in contrast to the peripheral retina where the RGC layer is only one cell thick [9]. Therefore, evaluation of macula, especially the inner retinal layers (ganglion cell layer and the inner plexiform layer), along with ONH and RNFL, has gained importance in glaucoma.

Current OCT devices evaluate the ONH, peripapillary RNFL as well as the macula using cube scans for assessing the amount of structural damage in glaucoma. Below is a description of the way ONH, RNFL and macular analysis is performed by different OCT devices.

20.2 ONH Analysis of OCT

Glaucoma is a disease that primarily affects the RGCs and their axons, which travel through the ONH into the optic nerve. Loss of RGCs and their axons, therefore show typical features on the ONH, such as increased cup-to-disc ratio, thinning of the neuroretinal rim (NRR) and NRR notching. Clinical characteristics such as NRR thinning and increased optic disc cupping differentiate glaucoma from other optic neuropathies [10].

ONH analysis with OCT is performed by automated software. Different manufacturers of OCT devices have their own proprietary softwares for ONH analysis, which vary slightly from each other. A brief description of the automated ONH analysis performed in commonly used OCT devices is important to understand the differences between the reported measurements.

The software of one OCT device identifies the termination of Bruch’s membrane as the disc margin from the 3-dimensional data set. The software then generates a set of vectors from the disc margin to points on the vitreoretinal interface for each radial direction and calculates a trapezoidal, cross-sectional area associated with each vector. For each point on the disc margin, the vector that produces the minimum cross-sectional area is identified and each of these vectors is geometrically transformed into the plane of the optic disc to provide a 2-D representation of the 3-D data acquired by the imaging software. In this manner, the cup margin is defined by a set of optimal vectors and the neuro-retinal rim area is calculated as a piece-wise sum of the corresponding minimum trapezoidal, cross-sectional areas. One of the difficulties in clinical assessment of the ONH is when the viewing angle of the ophthalmologist is not the same as the ONH axis, for example in tilted discs, where the neuro-retinal rim appears foreshortened. The 2-D representation of the ONH provided is as though the viewing angle is parallel to the ONH axis which minimizes the problem of foreshortening (Fig. 20.1) [11].

Fig. 20.1
figure 1

Automated segmentation of the optic nerve head on an OCT device. Open black circles represent the termination of Bruch’s membrane and the red circles represent the demarcation between neuroretinal rim and cup

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The software of another OCT device detects the retinal pigment epithelium (RPE) tips automatically and then delineates the optic disc margin by joining the RPE tips. The optic cup is defined by fitting a plane 150 μm parallel to and above the plane that fit the coordinates of the RPE tips. ONH tissue above the cup line and within the perpendicular lines drawn from the RPE tips to the surface of the retina is considered as the neuroretinal rim (Fig. 20.2).

Fig. 20.2
figure 2

Automated segmentation of the optic nerve head with another OCT software. The lower red straight line represents the line connecting the retinal pigment epithelium (RPE) tips and another red line is drawn 150 μm above and parallel to this plane. The optic nerve head tissue above the upper red line (between the 3 yellow colored circles) is defined as the neuroretinal rim tissue (source: Optovue.com)

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The software of yet another OCT device identifies the termination of the Bruch’s membrane and delineates the Bruch’s membrane opening (BMO) which acts as a surrogate for the disc margin. It subsequently calculates a parameter called minimum rim width (MRW) which is the minimum distance between the BMO line and the surface of the rim tissue (Fig. 20.3).

Fig. 20.3
figure 3

The software of another OCT device identifies the termination of Bruch’s membrane (Bruch’s membrane opening, BMO) as the disc margin. The green lines represent the minimum rim width (MRW) which is the shortest distance between the BMO and the surface of the rim tissue at various locations

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It is important to note that the ONH measurements of different imaging technologies are dependent on the segmentation performed by the propriety software and are not necessarily interchangeable with each other.

20.3 Peripapillary RNFL Analysis of OCT

RNFL contains the axons of the RGCs and the loss of the axons in glaucoma manifests as RNFL thinning and wedge shaped RNFL defects around the ONH (peripapillary region). Like for the ONH, different manufacturers of OCT devices have their own proprietary automated softwares for segmenting the peripapillary RNFL and measuring its thickness. Softwares of most OCT devices measure the RNFL thickness along a circle 3.46/3.45 mm in diameter positioned evenly around the center of the optic disc (Figs. 20.4 and 20.5). Software of another OCT device uses Fovea-to-Disc alignment technology that automatically tracks and aligns circle scans of 3.45 mm diameter to overcome measurement errors due to changing head and/or eye position during scanning (Fig. 20.6). This alignment technology improves the reproducibility of RNFL measurements.

Fig. 20.4
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Left image shows the circle scan (in purple color) along which peripapillary retinal nerve fiber layer (RNFL) thickness is measured on an OCT device. Right image shows the segmentation of RNFL; red line marks the internal limiting membrane and purple line marks the lower border of the RNFL

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Fig. 20.5
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Left image shows the circle scan along which retinal nerve fiber layer (RNFL) thickness is measured on a different OCT device. Right image shows the segmentation of RNFL displayed by the upper two white lines on the B scan image (source: Optovue.com)

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Fig. 20.6
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Left image shows the circle scans (green) along which retinal nerve fiber layer (RNFL) thickness is measured on another OCT device. FoDi (fovea to disc) alignment technology (blue line) is used here to overcome the effect of eye rotation and head tilt on RNFL measurements and to match with alignment of the normative database. Right image shows the segmentation of RNFL; red line marks the internal limiting membrane and blue line marks the lower border of the RNFL

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Studies have shown that the RNFL thickness values vary between different SD-OCT devices and cannot be used interchangeably [12, 13].

20.4 Macular Analysis of OCT

Macula contains the greatest density of RGCs and therefore, loss of RGCs result in thinning of the inner retinal layers at the macula. Thinning of inner retinal layers at the macula, however, cannot be assessed either clinically using slit-lamp biomicroscope or on fundus photographs. This macular information provided by OCT therefore is unique.

Different manufacturers of OCT devices have different proprietary automated software for segmenting the inner retinal layers and measuring their thicknesses at the macula (which roughly is a region 8° in radius from the center of the fovea). Software of one OCT device measures the GCL and IPL thickness together as ganglion cell-inner plexiform layer (GCIPL) thickness (Fig. 20.7) while another measures the RNFL and GCIPL thickness together as ganglion cell complex (GCC , Fig. 20.8). Another device measures the thicknesses of each retinal layer separately (Fig. 20.9).

Fig. 20.7
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Macular imaging of an OCT device which evaluates ganglion cell layer and inner plexiform layer together as ganglion cell-inner plexiform layer (GCIPL) thickness. Purple and yellow lines demonstrate the segmentation of GCIPL

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Fig. 20.8
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Macular imaging of an OCT device which evaluates retinal nerve fiber layer, ganglion cell layer and inner plexiform layer together as ganglion cell complex (GCC) thickness. The two white lines on the top demonstrate the segmentation of GCC (source: Optovue.com)

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Fig. 20.9
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Macular imaging from an OCT device which segments each layer of the retina and measures the thickness of each layer of retina separately (source: Heidelberg Engineering)

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20.5 Interpreting the ONH, RNFL and Macular Measurements of OCT in Normal and Glaucoma Eyes

Once the ONH , RNFL and macular scans are acquired and segmentation performed, the software of the OCT calculates certain parameters. All these measurements are then compared with an age-matched reference database within the device to quantify glaucomatous damage and are interpreted either as “within normal limits (color-coded as green)” or “borderline (coded as yellow)” or “outside normal limits (coded as red)”. The parameter is coded in green if the subject’s measurement falls between the 5th and the 95th percentile values of the reference database (p > 5%). The parameter is flagged in yellow if the measurement falls between the 5th and the 1st percentile values (p < 5% and p > 1%) and “red” if the measurement falls below the lowest 1 percentile value (p < 1%) of the reference database.

Figure 20.10 shows the ONH and RNFL report of a normal and a glaucomatous eye and Fig. 20.11 shows the macular (GCIPL) analysis report of a normal (Fig. 20.11a) and a glaucoma (Fig. 20.11b) patient from a commercially available OCT device. Figure 20.12 shows the results of ONH, RNFL and macular imaging from another OCT device in a normal subject (Fig. 20.12a) and a glaucoma patient (Fig. 20.12b). Figures 20.13, 20.14 and 20.15 show the ONH, RNFL and macular analysis report respectively of a normal and a glaucomatous patient from yet another OCT device.

Fig. 20.10
figure 10figure 10

Optic nerve head (ONH) and retinal nerve fiber layer (RNFL) reports of a normal subject (a) and a glaucoma patient (b). Compared to a normal eye, glaucomatous eye shows cool colors (blues, greens) on the “RNFL thickness map” representing thinning of RNFL (seen in the superior quadrant of the right eye and inferior quadrant of the left eye, b), red and yellow regions on the “deviation map” and “quadrant and clock hour maps” corresponding to the regions of RNFL thinning, RNFL and neuroretinal rim thickness falling in the red or yellow shaded region at corresponding locations around the disc on “TSNIT profile map” and reduction in the “summary parameters” in the tables which are color-coded in yellow or red

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Fig. 20.11
figure 11figure 11

Macular scan of a normal subject (a) and a glaucoma patient (b) showing the analysis of ganglion cell-inner plexiform layer (GCIPL) thickness. Interpretation of the macular report is similar to that described for the retinal nerve fiber layer (RNFL) report in Fig. 20.10. Figure (b) shows normal GCIPL thickness in right eye but superior GCIPL thinning in left eye, which is shown on GCIPL thickness, deviation and sector maps

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Fig. 20.12
figure 12

Optic nerve head (ONH), retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) analysis report of a normal subject (a) and a glaucoma patient (b) from an OCT device. The interpretation of the report is similar to that described in Fig. 20.10. Figure (b) shows RNFL and GCC thinning in the inferior quadrant of right eye. Left eye in (b) shows inferior quadrant RNFL thinning without any GCC abnormality

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Fig. 20.13
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Optic nerve head (ONH) report of a normal subject (a) and a glaucoma patient (b) from another OCT device showing the minimum rim width (MRW) at different sectors. Compared to the normal MRW measurements in Figure (a), Figure (b) shows MRW being thinner in the superior, temporal and inferior regions of left eye

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Fig. 20.14
figure 14figure 14

Retinal nerve fiber layer (RNFL) report of a normal subject (a) and a glaucoma patient (b) from another OCT device. Compared to the RNFL report of the normal subject, glaucoma patient shows inferotemporal RNFL thinning in both eyes

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Fig. 20.15
figure 15figure 15

Macular analysis report of a normal (a) and a glaucomatous eye (b) from another OCT device. Each layer of retina is analysed separately in this device, and ganglion cell layer (GCL) and inner plexiform layer (IPL) analysis is shown here. For each layer, the thickness is assessed at 64 cubes across the entire scan region and overlaid on the thickness map. The comparison between the superior and inferior hemispheres is also provided. Compared to the normal eye, the glaucomatous eye shows thinning of both the GCL and IPL in the inferior hemisphere

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Most of these reports show (1) a thickness map: in which RNFL/macular thickness measurements are represented in pseudocolors (hot colors such as red and orange represent thicker, and cool colors such as green and blue represent thinner measurements), (2) deviation and sector/clock-hour maps: in which the RNFL/macular thickness is compared with the reference database and coded either as green, yellow or red as described previously.

RNFL/macular thickness map is reported to be better than the deviation and the clock hour map to detect structural abnormalities in early glaucoma [14, 15]. Deviation and clock hour maps rely on the reference database to classify a measurement as normal, suspect or outside normal limits. The reference database has strict inclusion criteria and because of the wide range of “normal”, significant reduction of the structural measurements is required before a measurement is labelled as outside normal limits of the reference database.

20.6 Role of OCT in Diagnosing Glaucoma

Multiple studies have evaluated the ability of OCT in diagnosing glaucoma. A Cochrane review of these studies found the ONH and RNFL measurements to have excellent abilities to diagnose glaucoma [16]. In clinical practice, OCT imaging has been found to be especially useful in eyes with suspicious optic discs and normal VF. Figure 20.16 shows an eye with a glaucomatous optic disc (showing neuroretinal rim thinning and the presence of a RNFL defect inferiorly) and normal VF. OCT confirms the presence of RNFL thinning and quantifies it, enabling monitoring over time for progression. A study by Kuang et al. found that OCT was able to detect RNFL thinning 8 years before the development of VF defects [17]. Figure 20.17 shows an eye with a suspicious optic disc and a probable nasal defect on VF. OCT confirms the presence of RNFL thinning inferiorly which aids in the diagnosis and monitoring of this patient.

Fig. 20.16
figure 16figure 16

An example of a right eye showing neuroretinal rim thinning and the presence of a retinal nerve fiber layer (RNFL) defect inferiorly on the optic disc photograph (a) but normal visual field (b). OCT confirms the presence of RNFL thinning on RNFL thickness map, deviation map and clock-hour map (c). Reproduced with permission from [102]

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Fig. 20.17
figure 17

An example of an eye showing suspicious optic disc (a), a possible inferotemporal retinal nerve fiber layer (RNFL) defect on red-free photograph (b) and a probable nasal defect on visual fields (c). OCT confirms the presence of inferotemporal RNFL thinning on the thickness map and deviation map (d). It can be noted that the clock-hour and quadrant maps do not show any RNFL abnormality; these maps are known to have a poorer diagnostic ability as compared to the thickness and deviation maps. Reproduced with permission from [102]

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Structural changes in glaucoma are detectable on the ONH, RNFL and macular scans of OCT; however, it is still not clear which of these parameters detects glaucoma early. A systematic review reported RNFL measurements to be better than the inner retinal measurements at macula for diagnosing glaucoma [18]. However, it is useful to evaluate glaucomatous damage both on the ONH/RNFL and macular OCT scans to better understand the severity and pattern of structural loss.

20.7 OCT in Monitoring Glaucoma Progression

One of the most challenging aspects in the management of glaucoma is to diagnose progression early and accurately. Like for VF, software to detect progression on OCT also employ trend and event-based analyses. A trend-based analysis estimates the rate of change (slope) of the parameter over time. An event-based analysis determines progression if the amount of change crosses a particular pre-set value (threshold).

Figure 20.18a shows the ONH/RNFL progression report of one OCT device which uses both the trend and the event based progression analyses. The report includes three analyses:

  1. 1.

    a chronological display of RNFL thickness maps and RNFL thickness change maps

  2. 2.

    average cup to disc ratio (CDR), superior, inferior and average RNFL thickness graphs representing rate of change, and

  3. 3.

    RNFL thickness profiles comparing the current exam to the baseline exams.

Fig. 20.18
figure 18

Optic nerve head (ONH)/retinal nerve fiber layer (RNFL) progression analysis of an OCT device (a) showing disease progression on both event and trend-based methods. Two good quality baseline exams are required for all analyses and two good quality follow-up exams are required to confirm “likely change” in the ONH and RNFL parameters. The progression report includes “RNFL thickness change map”, an event-based analysis, which marks out the RNFL regions which have changed by an amount greater than the test-retest variability, “RNFL thickness graphs”, trend-based analyses, which evaluate the rate of change of superior, inferior and average RNFL thickness measurements over time and “RNFL thickness profile analysis”, also an event-based analysis, which detects the regions on the RNFL profile graph that have decreased beyond the expected test-retest variability. Macular (GCIPL thickness) analysis (b) showing disease progression on both event and trend-based methods. The report of GCIPL progression analysis, similar to the ONH/RNFL report, includes “GCIPL thickness change map”, an event-based analysis, and “GCIPL thickness graphs”, a trend-based analysis

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Two good quality baseline exams are required for these analyses and two good quality follow-up exams are required to confirm likely change in the ONH and RNFL parameters. Statistically significant changes are summarized with color-codes for possible or likely RNFL thickness loss (or possible RNFL thickness increase).

RNFL thickness map and thickness change map is an event-based analysis, which compares the follow-up exams with the baseline exams and marks out the RNFL regions which have changed by an amount greater than the test-retest variability. When a particular region exceeded the test-retest variability for the first time, it is flagged as a “possible loss” and coded in yellow. When the same region shows a change beyond the test-retest variability in a subsequent exam, it is flagged as a “likely loss” and coded in red. When a particular region shows an increase in RNFL thickness beyond the test-retest variability, it is flagged as a “possible increase” and coded in lavender. In the absence of concurrent retinal pathologies, “possible increase” generally occurs if the baseline exams include poor quality scans with an improvement in scan quality subsequently. RNFL thickness change map is preferred to detect focal changes.

RNFL thickness profile analysis is also an event-based analysis, which compares the RNFL profile of the current examination with that of the baseline exams. It detects the regions on the RNFL profile graph that have decreased beyond the expected test-retest variability. The regions showing the change are flagged as “possible loss”, “likely loss” or “possible increase” as mentioned above. RNFL thickness profile analysis, like the RNFL thickness and change map, is preferred to detect focal changes.

Average cup to disc ratio (CDR) and RNFL thickness graphs use a trend-based analysis to evaluate the rate of change of average CDR, superior, inferior and average RNFL thickness measurements over time. Rate of change in these trend-based analyses is estimated using linear regression. The individual data points (from each examination) are highlighted (color-coded) to indicate when the parameter value changed by an amount greater than the test-retest variability. The result shows up as “possible loss” when the rate of loss is statistically significant for only a single visit and is indicated by a yellow symbol. The result shows up as “likely loss” when the rate of loss is statistically significant for two visits in a row and is indicated by a red symbol. Average thickness graphs are preferred to detect diffuse changes unlike the previous two maps. Figure 20.18b shows the macular progression analysis report which is interpreted similar to the RNFL progression report detailed above.

Figure 20.19 shows the progression analysis report from another OCT device which employs a trend-based method to evaluate the rate of change of RNFL and macular (GCC) thickness over time. The rate of change is high-lighted (as represented in the figure) according to the p value of the slope. In the example, both RNFL and GCC thickness are decreasing at a magnitude which is significant.

Fig. 20.19
figure 19

Retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) thickness progression analysis of an OCT device showing significant deterioration of average RNFL and GCC thickness on the trend-based analysis

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Figure 20.20 shows the trend-based analysis of the global RNFL thickness (Fig. 20.20a) and the global MRW (Fig. 20.20b) of an OCT device. The analysis shows the magnitude of slope associated with the change in the parameters with time and also the p value associated with the slope.

Fig. 20.20
figure 20

Trend-based progression analysis of global retinal nerve fiber layer (RNFL) thickness (a) and minimum rim width (MRW, b) of an OCT device (source: Dr. Sebastian Rausch, Heidelberg Engineering)

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As demonstrated above, OCT analyses ONH, RNFL and macular parameters for progression; however, it is of interest to evaluate which of these parameters is useful in detecting progression early and at all stages of disease severity. Kim et al. investigated the temporal relationship between GCIPL and corresponding peripapillary RNFL thinning on the OCT deviation maps in 151 patients with early-stage POAG [19]. The scans were performed at baseline and 3 years later. They found that in eyes with early glaucoma, GCIPL change is frequently detected before corresponding peripapillary RNFL change. They concluded that peripapillary RNFL analysis alone likely would overlook macular damage and macular imaging should be included in the imaging algorithm for the serial observation of patients with glaucoma [19]. In another study, Hwang et al. found progression to be more frequent in RNFL than GCIPL thickness [20]. Of the 106 eyes that showed either RNFL and/or GCIPL progression in a cohort of 527 glaucoma eyes, 54 eyes (50.9%) showed progression earlier on RNFL thickness, 13 eyes (12.3%) showed progression earlier on GCIPL thickness, and 39 eyes (36.8%) showed progression both on RNFL and GCIPL thickness simultaneously [20]. Another study by Hou et al. investigated the temporal relationship among progressive macular GCIPL thinning, progressive RNFL thinning, and VF progression in 136 patients (231 eyes) with POAG followed up for ≥5 years [21]. GPA detected progressive GCIPL thinning in 57 eyes (24.7%) and progressive RNFL thinning in 66 eyes (28.6%) with at a specificity of 95.5% and 91.0%, respectively. In their study, eyes with progressive GCIPL thinning had a higher risk for progressive RNFL thinning (hazard ratio, HR = 5.27; 95% CI: 2.89–9.62), and eyes with progressive RNFL thinning also had a higher risk for progressive GCIPL thinning (HR = 2.99; 95% CI: 1.48–6.02). Eyes with progressive GCIPL and RNFL thinning were at a higher risk of VF progression. However, eyes with VF progression were not at risk of progressive RNFL or GCIPL thinning [21]. They concluded that progressive GCIPL thinning and progressive RNFL thinning are mutually predictive, and integrating GCIPL and RNFL measurements is relevant to facilitate early detection of disease deterioration in glaucoma patients [21]. Another recent study demonstrated that the macular and ONH parameters of OCT have the ability to detect progression even in advanced cases of glaucoma where the RNFL thickness had reached a “floor” (explained below) [22]. All the OCT parameters are complementary to each other and analysing all of them increases the sensitivity to detect progression. Therefore, it is preferable to obtain both the RNFL/ONH and macular scans at all follow-up examinations, and to consider the severity of glaucoma before deciding the OCT parameter to be used for detecting structural progression. However, it is to be noted that the use of multiple OCT parameters increases the number of eyes that are falsely labelled as progressing.

The magnitude of decrease in OCT measurements that is clinically significant is also debatable. Studies have reported a change of 4 μm for GCIPL, 5 μm for average RNFL and 7 μm for inferior and superior sector RNFL thickness to be clinically significant [23, 24], and this can be used as a thumb rule to aid decision making.

The relevance of the glaucoma progression on OCT is frequently questioned because it is believed that VF progression is more important from the patients’ perspective. However, studies have shown that OCT progression precedes VF progression [25, 26]. Also, faster rate of RNFL loss has been shown to be associated with faster decline in quality of life [27]. OCT therefore offers the possibility of detecting patients at high risk of VF progression, following them up more closely and a possibility of augmenting treatment early.

It is important to note that the OCT progression analysis gives us statistically significant change, but not clinically significant change. Therefore, the results of the OCT progression analysis should not be interpreted independently but in the context of the entire clinical picture. It is important to consider the results of the clinical evaluation (intraocular pressure, disease stage, occurrence of disc hemorrhage, increase in beta peripapillary atrophic region, etc.) and the VF along with the OCT findings while determining glaucoma progression.

20.8 Lamina Cribrosa Imaging

The lamina cribrosa (LC) is a multi-layered sieve-like structure in the sclera where retinal ganglion cell axons exit from the eye. The LC has been known to play a critical role in the pathogenesis of glaucoma [28]. Blockage of axonal transport within the LC has been proposed as a salient pathogenic mechanism for glaucoma. It is assumed that the LC provides mechanical support to the nerve fibers traversing the deeper ocular structures. Deformation of LC presumably from elevated IOP likely hinders axoplasmic flow and thus disrupts transport of trophic factors critical to retinal ganglion cells, thereby causing neuronal death that is characteristic of glaucoma. Furthermore, the LC represents a biomechanical discontinuity in the spherical casing of the eye, so that the globe is more vulnerable to stress loading that may play a role in glaucoma. Understanding such forces that affect the structure of the LC, through both focal and general morphologic changes, would clarify the mechanisms of glaucoma. A detailed study of the LC, however, requires accurate and clear visualization of this structure. Clinical examination of the LC structures cannot be performed routinely with current diagnostic methods.

Advances in OCT technology have enabled in vivo visualization of the LC now [29,30,31]. The enhanced depth imaging (EDI) technique for example, has provided better image contrast for the deep portion of the ONH [32, 33]. OCT light-attenuation software such as adaptive compensation has further improved LC visibility by effective blood-vessel shadow removal and better differentiation of tissue boundaries [34, 35]. Swept-source OCT (SS-OCT), which incorporates a longer-wavelength (1050 nm) light source than SD-OCT, reduces light dispersion from blood vessels, thereby allowing for high-resolution imaging of the LC [36].

LC findings on OCT are commonly categorized in terms of position (LC depth and LC insertion depth), curvature or shape, and microarchitectural change (LC defect) [37].

LC depth (LCD, Fig. 20.21) is defined as the maximum or mean vertical distance from the anterior LC surface to the reference plane of Bruch’s membrane opening (BMO). Multiple studies have demonstrated that the LCD is greater in eyes with glaucoma compared to healthy eyes [38,39,40]. LCD was also noted to reduce when the IOP in glaucomatous eyes was lowered after the introduction of medical therapy [41], or after glaucoma filtration surgery [42, 43]. In prospective studies, greater LCD was also associated with greater visual field progression [44] and faster rates of retinal nerve fibre layer (RNFL) thinning [45]. However, one of the limitation of calculating LCD is that it requires the use of a reference plane and BMO, which is used as the reference plane commonly, includes choroidal thickness in its measurement. Choroidal thickness varies with age and other non-glaucomatous pathologies and therefore can affect the LCD measurement [46, 47].

Fig. 20.21
figure 21

The anterior laminar insertion depth (ALID) is defined as the perpendicular distance between the Bruch’s membrane openings (BMO, red dots) and the anterior laminar insertion (ends of the orange line). The lamina cribrosa depth (LCD) is measured as the vertical distance between the BMO reference plane (red dotted line) and the maximally depressed point of the anterior laminar surface (orange line) (Image courtesy: Minh H. Suh, Department of Ophthalmology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea)

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Similar to LCD, lamina cribrosa insertion (LCI, Fig. 20.21), the region wherein the vascular perfusion from the capillaries of the short posterior ciliary artery drains to the LC [48], is reported to migrate posteriorly in glaucoma [49]. Determining LCI depth also requires the use of a reference plane.

To overcome the challenges of LCD and LCI depth determination, LC curvature, which does not require the use of a reference plane, has been proposed as an alternative structural parameter that is useful in glaucoma. LC curvature has been evaluated by measuring the difference between the mean LCD and the anterior LCI depth, in both the horizontal and vertical meridians [50]. LC curvature is reported to have a significantly better diagnostic performance than LCD [40].

All the above LC parameters on OCT are based only on the anterior surface of LC and the posterior surface of LC is difficult to image on OCT [51].

Focal LC defect (FLD) is representative of focal LC-microstructural change. On OCT, it is seen as an anterior laminar surface irregularity manifesting as a laminar hole or disinsertion that violates the normal smooth curvilinear contours of the LC (Fig. 20.22) [52]. FLD presents clinically as an acquired pit of optic nerve, rim thinning or notching, and has shown a spatial correlation with VF defect and localized RNFL defects [53, 54]. FLDs are also associated with presence of disc hemorrhages [55, 56]. Eyes with FLDs showed a faster rate of glaucoma progression [57,58,59,60].

Fig. 20.22
figure 22

Focal lamina cribrosa defect (FLD) observed in the inferotemporal region of the optic nerve head (Image courtesy: Minh H. Suh, Department of Ophthalmology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea)

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20.9 Limitations of OCT

OCT imaging requires clear media for good quality scans. Segmentation algorithms of OCT softwares fail in poor quality scans leading to erroneous measurements. Image quality is known to significantly affect the ability of OCT to detect glaucomatous defects [61, 62].

Using OCT in highly myopic eyes is challenging as a result of poor image quality and comparisons drawn using a dissimilar reference database.

OCT measurements are known to level off in more advanced stages of glaucoma. RNFL thickness, for example, levels off when the corresponding functional loss on automated perimetry reaches a severity of −10 to −15 dB [63]. Further deterioration in the glaucoma severity causes little change in the RNFL thickness, resulting in a floor effect (Fig. 20.23). It has to be noted that the floor may be at different levels for different OCT parameters and GCIPL and ONH measurements may still be able to detect progression in eyes where the RNFL thickness has reached a floor [22].

Fig. 20.23
figure 23

An example of a glaucomatous eye showing obvious progression on visual fields (a) but showing no progression on retinal nerve fiber layer (RNFL) analysis of OCT (b) due to the ‘floor effect’. Reproduced with permission from [102]

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Certain clinical findings that are crucial in decision making, like neuroretinal rim pallor and disc hemorrhages, are not picked up by the OCT.

20.10 OCT Angiography

OCT has also been used to non-invasively delineate the vasculature of the retina and ONH. Doppler OCT was one of the earliest techniques developed for vascular imaging. It assessed blood flow by comparing phase differences between adjacent A-scans [64]. Although Doppler OCT was appropriate for large vessels around the disc, it was not sensitive enough to measure accurately the low velocities in small vessels that make up the ONH and retinal microcirculation. The search for a simple, non-invasive, reproducible method of evaluating the ocular blood flow has recently lead to the development of a technique called OCT angiography (OCTA).

OCTA is capable of imaging large vessels as well as microvasculature of the retina and ONH by performing multiple OCT scans of the same region. The variation in OCT signal at each location is then studied. Moving particles, such as red blood cells, result in a high variance of the OCT signal between repeated scans and this is used to identify blood vessels. Several algorithms have been developed to interpret the OCT signals and to delineate the blood vessels. Split spectrum amplitude decorrelation angiography (SSADA ; Angiovue, RTVue-XR SD-OCT, Optovue Inc., Fremont, CA) is one such algorithm that uses the variation in the intensity of the OCT signal to identify blood vessels. The fluctuating value of OCT intensities is considered as the decorrelation (D). Thus, pixels in the B-scan frame where blood is flowing have fluctuating intensities and yield high D values (approaching 1). Pixels in the B-scan frames that contain static tissue yield small D values (approaching 0) [65]. The principles of SSADA have been explained in detail by Jia et al. [66]. The optical microangiography (OMAG), another algorithm that performs OCTA (Angioplex, Cirrus HD-OCT, Carl Zeiss Meditec Inc., Dublin, CA), uses the variation in intensity as well as the phase difference of the OCT signals for vessel delineation. As the time required to obtain the scan with OCTA is close to 3 s, involuntary saccades and changes in fixation during data acquisition can lead to motion artifacts that may confound the interpretation of the final OCT angiogram. Active eye tracking is therefore an essential feature of the OCTA devices.

Retina is segmented into different slabs, such as choriocapillaris, outer and deep retina and superficial retina, and vessels in each of these slabs is presented in 2-dimensional format. Figure 20.24 shows the macular scan of one OCTA device. The macular OCTA scan is performed using a volumetric scan covering a 3 × 3 mm area. More recently 6 × 6 mm scans of the macula are also available. The superficial retinal slab extends from 3 μm below the ILM to 15 μm below the inner plexiform layer (IPL). Deep retinal slab extends from 15 μm below IPL to 70 μm below the IPL. Outer retinal slab extends from 70 μm below the IPL to 30 μm below the retinal pigment epithelium (RPE) and choroid capillary slab extends from 30 μm below the RPE to 60 μm below the RPE.

Fig. 20.24
figure 24

Angiography slabs of the macular scan obtained using spectral domain optical coherence tomography showing the choriocapillaris (a), deep retinal (b), outer retinal (c) and superficial retinal (d) slabs

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The optic disc OCTA scan is performed using volumetric scans covering an area of 4.5 × 4.5 mm. The optic disc region, similar to the macular region, is divided into several slabs for further analysis. Figure 20.25 shows the optic disc scan of one OCTA device. The most superficial “vitreous” slab is usually used for assessing neovascularization of the disc and is not used in glaucoma. The “nerve head” layer extends from the internal limiting membrane (ILM) to 150 μm posterior and is used for assessing the vasculature within the optic disc. The “radial peripapillary capillary (RPC)” layer extends from the ILM to the posterior boundary of the RNFL and is used for assessing the vascular supply of the RNFL layer of the peripapillary region. And the “choroidal slab” is used to assess the deep retinal and choroidal vasculature.

Fig. 20.25
figure 25

Angiography slabs of the optic nerve head scan obtained using spectral domain optical coherence tomography showing the choroid (a), nerve head (b), radial peripapillary capillary, RPC (c) and vitreous (d) slabs

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OCTA quantifies the ocular circulation using two parameters: flow index and vessel density. Flow index is defined as the average decorrelation values in the measured area, and vessel density, which is the most widely used OCTA parameter, is defined as the percentage area occupied by vessels in the measured area [65]. The threshold decorrelation value used to separate blood vessel and static tissue is set at 0.125, which is two standard deviations above the mean decorrelation value in the foveal avascular zone, a region devoid of vessels. Quantification of vessel density can be performed in the nerve head and the RPC slab of the optic disc scan, but not the choroidal slab. After acquiring the optic disc scan, the software automatically fits an ellipse to the optic disc margin. The region within this margin is referred to as the “inside disc” region. The peripapillary region is defined as a 0.75 mm-wide elliptical annulus extending from the optic disc boundary. This region is further divided in 6 sectors based on the Garway-Heath map.

Similarly, vessel densities can also be determined for the superficial vascular plexus of the macula scan. The macular region is divided in the small central foveal area and a 1.5 mm wide parafoveal, circular annulus. This parafoveal region is divided in 2 hemispheres of 180° each (superior and inferior). Additionally, it is also divided into 4 sectors of 90° each (nasal, inferior, superior, and temporal sectors). The current OCTA machines do not contain a normative database for comparison of the patient’s vessel densities. Currently these comparisons are made in research studies using control eyes.

20.11 OCTA Report of a Normal Eye

Figure 20.26 shows the peripapillary OCTA report of a normal eye (showing normal optic disc and visual field). The OCTA report shows (Fig. 20.26a) angiography image showing large vessels and a dense network of capillaries in the RPC segment (Fig 20.26b) En face image (Fig. 20.26d, e) B scan showing the segmentation lines (for the RPC segment) along with the blood vessels detected by the SSADA algorithm (Fig. 20.26f) Heat map showing dense network of vessels in the peripapillary region; represented by warm colors such as red and yellow (Fig. 20.26c) Table showing the vessel density in the entire scan region and various sectors.

Fig. 20.26
figure 26

Peripapillary OCTA report of an eye showing normal optic disc (upper left), normal retinal nerve fiber layer on enface image (b) and normal visual fields (lower left). The OCTA report shows dense radial peripapillary capillaries on the angiography image (a) and heat map (f) with vessel densities in different sectors quantified in the table (c). Segmentation of the angiography slab on B scan is shown in (d) and (e). Flow signals on the B scan is shown in (d)

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Figure 20.27 shows a macular OCTA report of a normal eye showing the superficial retinal vessels. Macular scan is performed using a 3 × 3 mm scan. The interpretation of the report is similar to that of the peripapillary report described earlier.

Fig. 20.27
figure 27

Macular OCTA report showing the superficial retinal vessels of a normal eye. The OCTA report shows dense capillaries on the angiography image (a) and heat map (f) with vessel densities in different sectors quantified in the table (c). Enface image of the macula is shown in (b). Segmentation of the angiography slab on B scan is shown in (d) and (e). Flow signals on the B scan is shown in (d)

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20.12 OCTA Report of a Glaucomatous Eye

Figure 20.28 shows a glaucomatous eye (mild severity of disease) with a superior neuroretinal rim notch and superotemporal RNFL defect (indicated by arrows on the disc photograph) with a correlating inferior hemifield defect on the visual fields. OCTA shows reduced vessel density in the superotemporal region on the angiography and the heat map, along with a decrease in the vessel density in the corresponding sector.

Fig. 20.28
figure 28

Peripapillary OCTA report of a glaucomatous eyes with mild disease. Optic disc photograph (a) shows superior neuroretinal rim notch and superotemporal RNFL defect (indicated by arrows on the disc photograph) with a correlating inferior hemifield defect on the visual fields (b). OCTA report shows reduced vessel density in the superotemporal region as indicated by the arrows on the angiography (c) and heat map (h), along with a decrease in the vessel density in the corresponding region on the table (e, red arrow). Enface map (d) also shows the RNFL defect. fegmentation of the angiography slab on B scan is shown in (f) and (g). Flow signals on the B scan is shown in (f)

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Figure 20.29 shows an eye with advanced glaucoma. OCTA shows gross loss of capillaries on the angiography and heat map, along with a decrease in the vessel densities in all peripapillary sectors in the Table.

Fig. 20.29
figure 29

Peripapillary OCTA report of a glaucomatous eyes with advanced disease, which shows advanced disc damage (a), bi-arcuate visual field loss (d) and diffuse RNFL thinning (e). OCTA report shows gross loss of capillaries on the angiography (b) and heat map (f), along with a decrease in the vessel densities in all peripapillary OCTA sectors in the table (c)

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Figure 20.30 shows a glaucomatous eye with superior hemifield defect and a corresponding inferior ganglion cell complex (GCC) thinning. OCTA shows reduced superficial retinal vessel density in the inferior macular region noted on the heat map. Figure 20.31 shows the heat map of macular OCTA scan of the same eye performed with 6 × 6 mm scan. Compared to the 3 × 3 mm scan, the vessel dropouts are more obvious on the 6 × 6 mm scan.

Fig. 20.30
figure 30

Macular OCTA report of a glaucomatous eye which shows a superior hemifield defect (a) and a corresponding inferior ganglion cell complex thinning (b). OCTA shows reduced superficial macular vessel density in the inferior region on the angiography map (c), better noted on the heat map (h) and quantified in the table (e). Enface image of the macula is shown in (d). Segmentation of the angiography slab on B scan is shown in (f) and (g). Flow signals on the B scan is shown in (f)

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Fig. 20.31
figure 31

Macular OCTA report of the eye shown in Fig. 20.30 imaged using a 3 × 3 mm (a) and a 6 × 6 mm scan (b). Compared to the 3 × 3 mm scan, the vessel dropouts are more obvious on the 6 × 6 mm scan

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20.13 A Quantitative Analysis of OCTA Changes in Different Subtypes of Glaucoma

  1. (i)

    POAG: All the initial studies with OCTA were performed in eyes with primary open angle glaucoma (POAG) and showed reduced flow index and vessel density inside the ONH and in the peripapillary region of eyes with POAG compared to controls [65, 67,68,69]. Multiple studies subsequently showed that the OCTA vessel densities measured in the macular regions were also reduced in eyes with glaucoma compared to control eyes [70, 71]. Peripapillary vessel density reduction was found to be significantly greater than that inside the ONH and the macular region in glaucomatous eyes [70]. Vascular theory of glaucoma is considered to be more applicable in eyes developing glaucomatous damage at low/normal IOP. A few studies, therefore, compared the OCTA measurements in POAG eyes showing damage at low/normal IOP and high IOP. However, no differences in OCTA measurements were seen between these two POAG groups [70, 72].

  2. (ii)

    Angle closure glaucoma: OCTA measurements in primary angle closure glaucoma (PACG) were found to be similar to that in POAG when the severity of disease was matched for [73]. Like POAG (described below), OCT neuronal (NRR, RNFL and GCC) measurements had a better diagnostic ability compared to OCTA vessel density measurements (inside ONH, in peripapillary and macular regions) in PACG [74]. Moghimi et al. demonestrated that in acute primary angle closure glaucoma, vessel density decreased over 6 weeks after an the attack compared with the contralateral unaffected eyes. In contrast, there was an initial increase in RNFL thickness that was followed by a subsequent decrease [75].

  3. (iii)

    Pseudoexfoliation glaucoma: A few studies have evaluated the superficial retinal vasculature in the peripapillary region of PXG eyes and have reported that the reduction of vessel densities was greater in PXG compared to POAG eyes of similar disease severity [76, 77].

20.14 OCTA Changes in Perimetrically Intact Regions of Glaucomatous Eyes

It is important to determine the temporal relationship of vessel density reduction on OCTA with respect to RNFL thinning and visual field defects. This would help us develop strategies to detect the disease in the earliest stages. A longitudinal study is required to address this question. However, OCTA is a relatively new technology and there are, currently, no longitudinal studies addressing this question. As an alternate approach, studies have been performed in eyes with established perimetric glaucoma, whose visual field defects are limited to one hemifield; and the vascular changes in regions corresponding to the intact hemifield have been examined. These studies have found reduced peripapillary vessel density and RNFL thickness in the hemiretina corresponding to the perimetrically intact hemifield compared to that of healthy eyes [77,78,81]. One of these studies also found that the temporal sector of the perimetrically intact hemifield (corresponding to the region of papillomacular bundles) showed reduced vessel density in the presence of normal RNFL thickness [79]. This suggested that there may be regional variations in the alterations of RNFL thickness and vessel density measurements, and OCTA changes may precede RNFL changes in some sectors. In a longitudinal study with 13 months follow up by Shoji et al., eyes with POAG had significantly faster loss of macula vessel density than either glaucoma-suspect or healthy eyes. Serial OCTA measurements also detected glaucomatous change in macula vessel density in eyes without evidence of change in GCC thickness [82].

20.15 Comparing OCTA with OCT Measurements in Diagnosing Glaucoma

A number of studies have compared the diagnostic abilities of OCTA measurements (vessel densities of the inside disc, peripapillary and macular regions) with corresponding OCT measurements (ONH neuroretinal rim area, RNFL and macular thickness) in glaucoma.

A few studies comparing the diagnostic abilities (area under the receiver operating characteristic curves [AUC] and sensitivities at high specificities) of peripapillary vessel densities and RNFL thickness in POAG have found them to similar [68, 69, 73]. Depending on the severity of glaucoma patients included in these studies, the AUCs of both peripapillary vessel density and RNFL thickness have ranged between 0.85 to 0.95. A few other studies have reported a better diagnostic ability of RNFL thickness compared to peripapillary vessel density in POAG [71, 83]. In spite of the AUCs being similar, one study showed that the sensitivity to detect glaucoma in early stages of severity was better with RNFL thickness compared to peripapillary vessel density measurements [71]. Diagnostic abilities of vessel density measurements inside ONH were found to be significantly lesser than that of the OCT measured NRR area [71]. Similar to the peripapillary measurements, diagnostic ability of superficial retinal vessel density at macula was found to be similar to that of macular GCC thickness by one study [84] while the same was found to be inferior to GCC thickness by another study [71]. However, macular vessel densities in all these studies were evaluated on 3 × 3 mm scans, and a subsequent study showed that evaluating the macular vessel densities on 6 × 6 mm scans would be able to better detect glaucomatous changes [85]. It is still not clear if OCTA measured vessel density changes occur before or after OCT measured neuronal (NRR, RNFL and GCC) changes in glaucoma. Longitudinal studies in the future should be able to clarify this.

20.16 OCTA of the Peripapillary Choroid

Peripapillary choroidal circulation is of particular interest in glaucoma as it may be a surrogate marker for the perfusion of the deep ONH structures. Recently, choroidal microvasculature dropout (CMvD , Fig. 20.32), defined as the complete loss of choriocapillaris in localized regions of parapapillary atrophy (PPA), has been observed using OCTA in POAG eyes [86, 87]. CMvD has been shown to be a true perfusion defect using indocyanine green angiography [88]. Studies have also reported a topographic association between the location of CMvD and structural defects (RNFL thinning and lamina cribrosa defects) as well as functional defects (visual field loss) in POAG eyes [86, 86,87,91]. CMvD is a relatively novel finding in glaucoma and the clinical implications of it are not fully known. It has been argued that CMvD is likely to precede glaucomatous ONH damage [92]. A recent study reported an association between CMvD and progressive RNFL thinning in POAG eyes with DH [93]. Longitudinal studies are required to determine the clinical implication of CMvD in glaucoma.

Fig. 20.32
figure 32

Choroidal OCTA slab of a glaucomatous eye showing the presence of choroidal microvasculature dropout (CMvD) in the inferior region (a). Arrow points to the CMvD. Yellow line marks out the boundary of the CMvD (b)

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20.17 Factors Affecting OCTA Measurements

Unlike the neuronal elements, vasculature is affected by multiple factors other than glaucoma. A study evaluated the effect of subject-related (age, gender, systemic hypertension and diabetes), eye-related (refractive error, optic disc size) and technology-related (signal strength index, SSI of the scans) determinants on the peripapillary and macular vessel densities in normal eyes [94]. It found that peripapillary vessel densities were higher in females. Peripapillary vessel densities were lower, while the macular vessel density was higher, in subjects with hypertension. Most of the vessel densities were lower in subjects with diabetes. In addition to these factors, SSI of the OCTA scans showed a significant positive association with the vessel densities of all regions. Vessel densities were higher in scans with higher SSI values [94]. These results should be considered while interpreting the vessel densities in glaucoma.

20.18 Limitations and Recent Advances in OCTA

Motion artifacts are common with OCTA imaging due to the prolonged time required to acquire the scans; in spite of methods available to account for the artifacts (Fig. 20.33). This is true even in research settings and multiple studies have also reported high number of poor quality images with OCTA [92,93,94,98]. Two significant improvements incorporated recently to overcome the issue of poor quality scans are (1) real time eye tracking technology, for controlling the motion artifacts more effectively [99] and, (2) high-density (HD) scanning mode, for improving the resolution of the scans. A recent study has reported that the number of poor quality scans significantly decreased with the incorporation of these improvements [100].

Fig. 20.33
figure 33

Macular OCTA scan showing two types of artifacts; motion artifacts, recognized as vertical bands temporally on the en face map (b) and duplication of vessels, recognized inferiorly and nasally on the angiography map (a). These artifacts cause a decrease in vessel density in the temporal sector and increase in vessel density in the nasal and inferior sectors (noted on the heat map [f] and table [c]). Segmentation of the angiography slab on B scan is shown in (d) and (e). Flow signals on the B scan is shown in (d)

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Media opacities, especially vitreous opacities, can significantly affect the quality of OCTA scans and the quantification of vessel densities (Fig. 20.34).

Fig. 20.34
figure 34

Vitreous opacity (red arrow on en face map [b]) casting a shadow, as seen on the angiography map (a) and causing a falsely reduced vessel density on the heat map (c)

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OCTA technology is able to evaluate the superficial retinal vessels well but not the deeper retinal and choroidal vasculature. This is because the signals from the superficial retinal vessels project on to the deeper layers causing artifacts known as the projection artifacts [66]. Detection of CMvD, for example, is affected by the presence of projection artifacts. Newer methods of projection artifact correction have been tried and the newer generations of OCTA (projection resolved OCTA) are likely to evaluate the deeper retinal and choroidal vasculature better [101].

20.19 Summary

The introduction of OCT, has caused a paradigm shift in glaucoma diagnosis and monitoring. The information derived from OCT complements the information derived from the clinical examination and VF assessment in detecting glaucoma or its progression.