For acquisition of high-quality OCT images, parameters such as alignment of the camera, focus, detector sensitivity and signal strength are important prerequisites. Automatic registration and matching of OCT images of the same retinal location is an essential tool for monitoring subtle changes over time. The same focus should be kept between different imaging sessions and tilting of the head should be avoided during image acquisition in order to minimize artifacts or inaccuracies [63]. Incorrect settings should be identified before a clinician interprets the results. In order to avoid misinterpretations, operators should be adequately trained and instructed to check the quality and completeness of the data directly after the recordings, as an immediate reacquisition might be possible with the subject still in front of the device [61].

Up to now, no common industry standard has been established for OCT imaging. In addition, device-dependent differences may also occur (e.g. in the appearance of retinal thickness), as OCT B-scans are usually displayed as stretched images in the vertical direction. Accordingly, for better comparability, the same patient should be examined with the same device platform over time. Even by simple software updates, the algorithms and definitions of the automatic segmentation lines may change and, therefore, the comparability of subsequent recordings and their evaluation may be limited [61].

#### **4.3.3 Interpretation**

For an adequate evaluation of modern OCT imaging, the collected data should be reviewed on the reviewer software instead of using printed scans or PDF files, as it offers the possibility to evaluate all collected B-scans individually. Evaluation of a single OCT-scan may not be sufficient for differential diagnosis or to determine disease activity and treatment effects (Fig. 4.13) [61].

Correct interpretation of OCT findings is a prerequisite for treatment decisions. Precise knowledge of retinal and macular diseases is mandatory, as focusing on the relevant findings may be

**Fig. 4.12** Pitfalls in OCT acquisition. Application of the 19-line volume scan (left panels) versus a star scan protocol (right panels) in an eye with vitreomacular traction and full-thickness macular hole. Note that the three central B-scans in the volume scan fail to detect the relevant pathologic findings. Source: Schmitz-Valckenberg S et al.: Pitfalls in retinal OCT imaging. *Ophthalmology @ Point of Care*. 2017. Reprinted with permission

**Fig. 4.13** Pitfalls in OCT interpretation I. Choroidal neovascularization (CNV) under antivascular endothelial growth factor therapy. Evaluation of only the central B-scan for activity of the CNV lesion (**a**) would fail adequate interpretation of the disease status, because inferior to the fovea, there is intraretinal fluid indicating disease activity (**b**). Source: Schmitz-Valckenberg S et al.: Pitfalls in retinal OCT imaging. *Ophthalmology @ Point of Care*. 2017. Reprinted with permission

challenging with an increasing number of B-scans. In addition, the exact assignment of OCT layers to anatomical structures (applied after the consensus of the International Nomenclature for Optical Coherence Tomography Panel) as well as pattern recognition plays an important role in image evaluation, as the differential diagnosis or treatment indication can be supported by recognition of characteristic OCT findings. For example, domeshaped elevations of the RPE in the presence of soft drusen indicate exudative AMD; whereas marked thickening of the choroid in OCT images without soft drusen would point to CSCR. Further examples of challenging OCT interpretations are demonstrated in Fig. 4.14.

Projection artefacts may derive from hyperreflective changes in the vitreous (e.g., floaters) or on the surface of the retina (e.g., epiretinal membranes) that may lead to suppression of structures in deeper retinal layers. In such scenarios, comparison with other imaging modalities or ophthalmoscopy is helpful. When applying automatic analysis algorithms, operators and clinicians should evaluate the segmentation of retinal boundaries in each B-scan and, if necessary, manually correct them [61].

The quantitative evaluation of OCT findings requires precise definition of individual parameters. To date, there is no industry standard or consensus, with different terms being used in

**Fig. 4.14** Pitfalls in OCT interpretation II. In contrast to intraretinal cystoid lesions secondary to CNV, macular telangiectasia type 2 revealed mimicking alterations within the inner retina, due to degenerative changes (**a**). Note that there is no thickening of the retina. Further misinterpretation concerning indication to anti-VEGF treatment might derive from

degenerative outer retinal tubulations (arrows) as visualized by an OCT B-scan and by an OCT en face image (**b**), epiretinal membrane (**c**), or choroidal folds of different origins (e.g. orbital tumor, (**d**)). Source: Schmitz-Valckenberg S et al.: Pitfalls in retinal OCT imaging. *Ophthalmology @ Point of Care*. 2017. Reprinted with permission

parallel. The correct geometric location and segmentation of relevant anatomic landmarks is crucial for meaningful and correct quantitative analysis. But the definition of landmarks such as the fovea may be challenging in the presence of pathologic changes. The OCT interpretation is usually based on the 1:1 pixel presentation mode, in which the image information in the lateral compared to the anteroposterior dimension is compressed depending on the device. It was shown that the quantification of areas or distances in the 1:1 pixel presentation mode is prone to overestimation of values in the anteroposterior dimension. Therefore, measurements should be performed in the 1:1 μm presentation mode [64, 65]. Inaccuracies of measurements within B-scans may further occur if the retinal layers are not orthogonal to the laser beam. To determine correct values, measurements should always be performed parallel to the beam path. Furthermore, the method of scaling must be considered when measured values are specified in the metric system.

Several structures are usually not distinguishable, because of the lack of reflectivity in the perpendicular laser beam. Changing the direction of the laser beam in relation to the retina, e.g. by tilting the head of the subjects, some of these structures, like the Henle fiber layer, may become visible [66]. However, other alterations, like retinal hemorrhages, stay concealed as the imaging light shows little or no interference in the area of bleeding (Fig. 4.15).

In conclusion, while OCT alone should not be the only basis for diagnostic and treatment recommendations, its application in daily clinical practice and for research purposes has become invaluable. It should be regarded as an additional diagnostic procedure in a multimodal imaging assessment including fundoscopy, fluorescence angiography, in association with a careful anamnesis and assessment of patient's complaints. The latter is known to differ frequently from the severity of OCT findings. In these cases, it is more than mandatory to perform complimentary imaging and diagnostic procedures [61].

**Fig. 4.15** Pitfalls in OCT interpretation III. Hemorrhages (arrow on fundus photograph) are frequently not visualized by near-infrared reflection and optical coherence tomography imaging due to the wavelength used. Source: Schmitz-Valckenberg S et al.: Pitfalls in retinal OCT imaging. *Ophthalmology @ Point of Care*. 2017. Reprinted with permission

#### **4.4 Summary and Outlook**

Many posterior segment ocular diseases involve both the retina and the choroid as the RPE, Bruch's membrane and the choroid represent a coadjutant functional complex [67]. This may be particularly important in retinal disorders such as AMD, the most common cause of legal blindness in industrialized countries, characterized by abnormal extracellular material deposition either below or above the retinal pigment epithelial layer [68, 69]. Even single gene retinal dystrophies like *ABCA4*-related retinopathy, that primarily affects the RPE by excessive accumulation of lipofuscin, or pseudoxanthoma elasticum (PXE), which leads to a calcification of the Bruch's membrane, have been described to reveal choroidal alterations [54, 55, 70, 71]. The combination of shorter and longer wavelength light sources within one gadget might combine the advantages attributed to SD-OCT (i.e., better resolution for the visualization of retinal layers) and SS-OCT (i.e., visualization of the choroid). This might allow for optimum visualization of intraretinal as well as subretinal structures without temporal or spatial separation. In 2017, the first OCT device using different wavelengths of laser light sources was built at the Technical University of Biel and University of Basel, Switzerland. First clinical data and value with the device remain to be demonstrated as well as the possible commercial feasibility.

Since the beginning, continuous improvements have been made to scan rates as well as axial and lateral resolution. Commercial OCT systems achieve scan rates up to 250,000 Hz and an axial resolution of under 7 μm [1, 6]. Faster imaging improves patient comfort and reduces acquisition time, increasing the likelihood of better scan quality. It also enables volumetric as well as 3-dimensional analysis of various pathological features, including choroidal neovascularization and intraretinal fluid. The latter might help in monitoring disease progression and treatment effects [72]. Furthermore, higher quality by improved resolution will further enhance automated segmentation and analysis, a field of rising importance in the view of growing applications of artificial intelligence and machine learning in ophthalmology [73–75].

During the last decades, OCT technology has revolutionized the retina subspecialty field. OCT imaging now plays a pivotal role in understanding, diagnosing, and monitoring natural history and treatment effects in AMD, diabetic retinopathy, retinal vascular diseases, CSCR, high myopia and many other retinal and choroidal conditions. High resolution and high-quality multimodal assessment in combination with continuous innovations of the OCT imaging modality are aiming to further improve the clinical assessment of retinal and choroidal diseases.

#### **References**

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growth factor agents. Surv Ophthalmol. 2015;60:204– 15. https://doi.org/10.1016/j.survophthal.2014.10.002.

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# **Ophthalmic Diagnostic Imaging: Glaucoma**

Robert N. Weinreb, Christopher Bowd, Sasan Moghimi, Ali Tafreshi, Sebastian Rausch, and Linda M. Zangwill

#### **5.1 Introduction**

The detection and monitoring of glaucoma customarily involves several processes that include diagnostic modalities such as subjective evaluation of the optic nerve head (ONH), visual field testing, and intraocular pressure measurements. These traditional methods of assessing glaucoma have several key limitations that dictate the need for supplementary approaches. The diagnostic assessment of the ONH in glaucoma by ophthalmoscopic examination or serial stereoscopic photographs is highly dependent on observer skills, inducing high inter-observer and intra-observer variation that affects its utility [1–3]. Visual field analysis through automated perimetry is a widely used technique that is considered an established clinical endpoint and is arguably the goldstandard for evaluation of glaucoma and for monitoring of disease progression. Although it is sensitive and specific at detecting glaucomatous functional loss, automated perimetry has several significant limitations [4–7]. The test requires the subjective input of the tested individual, making it prone to high short- and long-term fluctuation. The high fluctuation of the test, induced by its subjective nature, necessitates multiple tests to improve the reliability of the technique, delaying the recognition of glaucomatous damage [8]. Several studies have shown that detectable glaucomatous field abnormalities may be preceded by structural changes of the ONH and nerve fiber layer [9–18] Furthermore, intraocular pressure (IOP) is the major identified risk factor for the development of glaucomatous damage and is the only modifiable risk factor to date. Although lowering IOP serves to impede the progression of retinal ganglion cell degenerative change [19– 21], the high inter-individual variability and the diurnal variation in the intraocular pressure have limited the use of this parameter for the detection of the disease. Moreover, intraocular pressure values do not indicate whether damage has occurred, or to what extent. In addition, elevated IOP (i.e. ocular hypertension) does not necessarily result in glaucomatous damage [14].

While detection of glaucomatous structural damage to the eye during the earliest stage and precise assessment of this change are critical aspects of managing the disease, both feats are challenging. Glaucomatous damage is largely irreversible and, therefore, eyes with structural damage must be identified as early and as

R. N. Weinreb · C. Bowd · S. Moghimi · L. M. Zangwill Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, CA, USA

A. Tafreshi (\*) · S. Rausch Heidelberg Engineering GmbH, Heidelberg, Germany

accurately as possible because they are at risk for continued injury. It has been suggested that the earlier glaucoma is detected and treated, the greater the likelihood that medical or surgical intervention will delay or prevent the progression of glaucomatous neuropathy and subsequent functional impairment [22–24]. Furthermore, because glaucoma progresses slowly, it is important to detect real change due to disease that is beyond normal age-related loss and short-term and long-term fluctuations. This assumption underscores the need for an accurate and reproducible quantitative evaluation of the eye.

During the past three decades, there has been significant development and implementation of several imaging technologies designed to objectively and quantitatively detect glaucomatous neuropathy at early stages of disease. Beyond early detection, quantitative and objective imaging devices offer a more sensitive way to detect glaucomatous progression when compared with clinical qualitative assessments. One of the earliest imaging devices introduced to the ophthalmic field was a confocal scanning laser ophthalmoscope (cSLO), a device developed to assess optic disc topography in the late 1980s (Laser Tomographic Scanner, Heidelberg Instruments). With reductions in cost and the advent of improved hardware, the introduction of the first practical commercial cSLO device was introduced in 1991 [Heidelberg Retina Tomograph (HRT); Heidelberg Engineering, Heidelberg, Germany].

Imaging instruments provide objective, quantitative measures of neuroretinal rim thinning, RNFL atrophy, and excavation of the optic cup, and are increasingly utilized in the clinical management of glaucoma patients. This is due in part to the availability of summary information that can easily be used in clinical management decisions. For example, most instruments now include a reference database for making statistical indications of whether a patient measurement is "Within Normal Limits," "Borderline," or "Outside Normal Limits." In addition, each device provides a measure of image quality so that the clinician can determine whether the image is of sufficient quality to be utilized in clinical management decisions. With continuous developments in imaging technology like spectral domain OCT (SD-OCT) and advancements in research applications of such technologies, the value of these devices in glaucoma management is likely to continue growing.

Although in vivo imaging with cSLO, timedomain OCT (TD-OCT), and spectral-domain OCT (SD-OCT) have been commercially available for the management of glaucoma for over 10 years, interpretation and utilization of such results remains a challenge. However, clinical research continues to significantly advance the relevance and utility of diagnostic imaging devices by enabling visualization of highresolution and detailed images and to provide sophisticated data analysis strategies. Such improvements increase efficiency while providing precise, accurate analysis of the retinal data produced by each device [25–27]. This chapter will review diagnostic imaging techniques that have advanced and continue to advance diagnosis and management of glaucoma.

#### **5.2 The Heidelberg Retina Tomograph: Confocal Scanning Laser Ophthalmoscope (cSLO)**

The first cSLO device developed as a diagnostic aid for glaucoma, the Heidelberg Retina Tomograph (HRT), utilizes confocal optics to obtain multiple measures of retinal height at consecutive focal planes to provide a topographic map that extends from the retinal anterior surface down to the lamina cribrosa. The HRT platform includes comprehensive software that facilitates image acquisition, storage, retrieval, and analysis. After manually delineating the optic disc margin by placing a contour line along the inner edge of the scleral ring at the baseline exam, stereometric parameters are provided to describe the retinal topography of each image. The contour line is automatically transferred to all follow-up examinations. Many of the stereometric parameters are calculated based on a standard reference plane set 50 μm posterior to the average contour line height (i.e., retinal height) at a 5° sector along the temporal rim; an area thought to be least effected by glaucomatous progression and therefore thought to change minimally over time. Stereometric parameters include: disc area (area within contour), rim area (area within contour and above reference plane), cup area (area within contour and below reference plane), rim volume, cup volume, mean cup depth, mean height of contour, an indirect measure of retinal nerve fiber layer (RNFL) thickness, and cup shape (Fig. 5.1).

#### **5.2.1 Clinical Development**

Several key clinical research studies contributed to the advancement and development of the cSLO technology for assessment of the optic nerve head. In 1993, Weinreb et al. recommended acquiring multiple images at each visit, and showed that with three images one could obtain excellent reproducibility within a short time for a reasonable cost [28]. This study led to the implementation of software that automatically acquires three sets of three-dimensional images each time. If the quality of images in at least one of the series is not good enough (for reasons such as fixation loss), the acquisition is automatically continued until three useful series are obtained. The same group also showed the SLO imaging is highly reproducible in patients with [29] or without undilated pupils [30]. There was no significant difference between the standard deviation of a single height measurement in normal and glaucomatous eyes. No correlation was found between standard deviation of the measurements and pupil size or age of the subject [29]. Zangwill et al. demonstrated a moderate agreement between clinicians and a HRT in estimating cup/disc ratios with the highest disagreements in discs with gradual slopes and pallor. New quantitative criteria then were established for characterizing a disc as glaucomatous using HRT [31]. A quantitative method was then developed for analysis of the topographic relationship between structural and functional damage in patients with glaucoma [32].

In 1998, Wollstein and associates introduced the Moorfields Regression Analysis (MRA) to the HRT. The MRA classification technique compares global and local rim area measurements (reference plane dependent) to a normative database taking into account disc area and age [33]. A few years later, a machine-learning based diagnostic classifier called the Glaucoma Probability Score (GPS) was introduced and implemented in the third generation of the HRT (HRT III). The GPS was one of the earliest applications of machine learning in the ophthalmic field, using a geometric model to describe the shape of the optic disc/parapapillary retina (globally and locally) based on five parameters (cup size, cup depth, rim steepness, horizontal retinal nerve fiber layer curvature, and vertical retina nerve fiber layer curvature) [34]. These parameters are then interpreted by a relevance vector machine classifier [35] and the resulting output describes the probability that the eye is glaucomatous (based on fit to training data from healthy and glaucoma eyes). This technique does not depend on an operator drawn contour line or a reference plane and is therefore operator independent (Fig. 5.2). Results from both classification techniques are reported as 'within normal limits', 'borderline' or 'outside normal limits' globally, and for each of six disc sectors relative to the normative data.

In 2000, Chauhan et al. [36] introduced Topographic Change Analysis (TCA), a progression analysis tool that became a gold-standard in the assessment of glaucomatous ONH changes. The TCA quantifies the change in the topography of the ONH using the first image as the baseline and the subsequent images as follow-up examinations. The TCA does not require a defined contour line to determine areas of significant change as it assesses the height of the optic disc and retinal surface at each follow-up measurement and compares these to the baseline measurement. The images are further analyzed using an array of 4 × 4 pixels, called super-pixels. Superpixels allow for pooling over a larger area and yield more repeated measures for analysis. In steep areas like the edge of the cup, the variability is greater than that in flat areas. The topographic measurement of a superpixel (4 × 4 pixels) and an analysis of variance model for each superpixel is then calculated.

**Fig. 5.1** After manually delineating the optic disc margin by placing a contour line along the inner edge of the scleral ring at the baseline exam, stereometric parameters are provided to describe the retinal topography of each image. Stereometric parameters include: disc area (area within contour), rim area (area within contour and above reference plane), cup area (area within contour and below reference plane), rim volume, cup volume, mean cup depth, mean height of contour, an indirect measure of retinal nerve fiber layer (RNFL) thickness, and cup shape

**Fig. 5.2** The Glaucoma Probability Score (GPS) uses a geometric model to describe the shape of the optic disc/ parapapillary retina (globally and locally) based on five parameters. The resulting output describes the probability that the eye is glaucomatous. This technique does not depend on an operator drawn contour line or a reference plane and is therefore operator independent. Results are reported as 'within normal limits', 'borderline' or 'outside normal limits' globally, and for each of six disc sectors relative to the normative data

#### **5.2.2 Clinical Validation**

The combination of these HRT (cSLO) diagnostic tools became the gold standard for imaging and monitoring of the optic nerve head in glaucoma, in the early- to mid-2000s. Several large and seminal studies contributed to the validation of the diagnostic parameters offered by the HRT.

Bowd et al. validated the clinical utility of this tool by showing that TCA parameters can discriminate between progressing glaucoma eyes and longitudinally observed healthy eyes, suggestive of the ability of the HRT with TCA to detect early ONH changes due to glaucoma [37]. The Ocular Hypertension Treatment Study (OHTS), sponsored by the National Institutes of Health/ National Eye Institute, is a multicenter randomized clinical trial designed to evaluate the safety and efficacy of topical glaucoma medication in delaying or preventing the onset of glaucomatous VF loss or optic nerve deterioration in participants with ocular hypertension [14, 38]. In OHTS, the presence of clinically significant disc changes during follow-up was determined by evaluation of serial stereoscopic optic disc photographs.

The Confocal Scanning Laser Ophthalmoscopy Ancillary Study to the Ocular Hypertension Treatment Study was the first multicenter clinical trial to use cSLO imaging to monitor changes in the optic disc [39]. This study evaluated the effectiveness of various HRT parameters in detecting the presence and progression of glaucomatous optic disc damage and determined whether optic disc topographic measurements are an accurate predictor of visual field loss. The 451 participants in the OHTS CSLO ancillary study were recruited from seven of the 22 OHTS centers and the HRT examinations were obtained annually after pupillary dilation at the time of scheduled OHTS fundus examination and optic disc photography.

The baseline data from OHTS indicated that cSLO measurements correlated well with expert evaluation of stereoscopic photography and that differences in topographic optic disc parameters between African Americans with ocular hypertension and other racial groups are largely explained by the larger optic disc area in the African Americans [38–40]. This latter result highlighted the need to consider race and optic disc size when evaluating the appearance of the optic disc in glaucoma. Therefore, several of the parameters implemented in the HRT are reported and compared to a race specific normative database.

#### **5.2.3 Surrogate Endpoints and Progression**

The HRT neuroretinal rim parameters have been shown to be predictive of functional loss and to serve as suitable surrogate endpoints in glaucoma clinical trials [15, 41–44]. The OHTS data also showed that baseline topographic optic disc measurements can predict the onset of primary open angle glaucoma in patients with ocular hypertension [15]. OHTS results suggest that baseline GPS, MRA, and stereometric parameters alone or when combined with baseline clinical and demographic factors can be used to predict the development of POAG endpoints in OHTS participants and are as effective as stereophotographs for estimating the risk of developing POAG in ocular hypertensive subjects [15, 41]. In 2008, Alencar et al. assessed whether baseline GPS results are predictive of progression in patients suspected of having glaucoma. Their study results showed that baseline GPS are in fact predictive and that they perform as well as subjective but expert assessment of the optic disc. They further suggested that the HRT GPS could potentially replace stereophotographs as a tool for estimating the likelihood of conversion to glaucoma [42]. In 2009, Chauhan et al. concluded that patients that presented with glaucomatous visual field progression were up to three times more likely to have prior disc changes as measured on TCA [43]. Medeiros et al., showed that progressive rim area loss, as defined by the HRT parameters, was highly predictive of the development of visual field loss in glaucoma and explained a significant proportion of the effect of treatment on the clinically relevant outcome [44]. They suggested that rim area measurements may be suitable surrogate endpoints in glaucoma clinical trials [44].

The Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES) are large multi-center ongoing studies that include normal subjects, patients with glaucoma, and glaucoma suspects, who are semi-annually evaluated clinically and with several functional and optical imaging tests including HRT and SD-OCT. The 3-site collaboration includes the Hamilton Glaucoma Center at the Veterbi Family Department of Ophthalmology (RN Weinreb), University of California, San Diego (UCSD) (Data Coordinating Center) (L Zangwill), Columbia University and the New York Eye and Ear Infirmary (J Liebmann), and the Department of Ophthalmology, University of Alabama, Birmingham (C Girkin and M Fazio) [45, 46].

Results from DIGS confirmed earlier reports of the comparability of stereophotograph based cup-to-disc ratio measurements and HRT measures in predictive models [47]. The same group also concluded that the presence of optic disc damage on stereophotographs is highly predictive of future development of functional loss [48]. They later showed that the rate of rim area loss measured using HRT is approximately 5 times faster in eyes in which POAG developed compared with eyes in which it did not. The results of this study, suggest that measuring the rate of structural ONH change using cSLO-based parameters can provide important information for the clinical management of ocular hypertensive patients [49].

The ADAGES group characterized the rate and pattern of age-related and glaucomatous neuroretinal rim area changes in subjects of African and European descent, using HRT parameters. They showed that compared with healthy eyes, the mean rate of global rim area loss was 3.7 times faster and the mean rate of global percentage rim area loss was 5.4 times faster in progressing glaucoma eyes [50].

#### **5.2.4 Summary**

Heidelberg Retina Tomograph cSLO technology introduced the ability to objectively quantify various diagnostic parameters for the assessment and management of glaucoma. The technology's diagnostic parameters offer clinicians with an objective and precise method to aid their decision in the diagnosis and management of the disease, while also serving as potential surrogate endpoints in clinical trials. A more recent imaging technology, spectral domain optical coherence tomography (SD-OCT), which also enables to objectively image and assess the optic nerve head as well as the retinal nerve fiber layer and macula, has been established as the most current commonly used diagnostic imaging aid for glaucoma.

#### **5.3 SPECTRALIS SD-OCT**

With the introduction of SD-OCT, it has become possible to image ocular structures in three dimensions with high axial resolution, fast scan rates, and high contrast. These advances have improved visualization of small details and provided a platform for precise analytics. The benefits of SD-OCT technology, combined with eye-tracking algorithms that enable precise scan registration from session to session allow for reliable removal of errors induced by eye movements. The resulting scans offer detailed visualization of retinal structures and provide accurate segmentation of the anatomical boundaries used in advanced analytics.

The SPECTRALIS SD-OCT (Heidelberg Engineering GmbH, Heidelberg, Germany) incorporates a real-time eye tracking system (TruTrack™) that couples cSLO and SD-OCT scanners to adjust for eye movements and to ensure that the same precise location of the retina is scanned time after time (Fig. 5.3) reducing variability across longitudinal measurements used for monitoring disease progression. This method also allows B-Scans to be re-sampled in the same location to improve the signal-to-noise ratio (SNR), a technique called Automatic Realtime Tracking (ART).

The standard SPECTRALIS SD-OCT glaucoma software includes RNFL thickness measurements derived from a 12° circle scan,

**Fig. 5.3** A core technology of the SPECTRALIS is TruTrack (**a**), a dual-beam tracking system which provides very important clinical benefits such as retinal recognition, follow-up scanning, precise co-localization of

fundus images with depth-resolved information in OCTscans. This system also enables the Automatic Real-time Tracking (ART) technique for image averaging to improve image quality by reducing noise (**b**)

manually centered by the operator on the optic disc. The RNFL thickness analysis then provides sectorial and global measurements that are compared with a reference database of healthy controls. The RNFL thickness values for a scan are classified using a 6-sector analysis, and sectors can be flagged green ("Within Normal Limits"), yellow ("Borderline"), and red ("Outside Normal Limits"). RNFL parameters of the SPECTRALIS have been evaluated relative to other commercially available SD-OCT devices and shown to be as specific, sensitive, and repeatable [51, 52].

While several SD-OCT devices are commercially available, Heidelberg Engineering has included several unique features and functionalities with the intent to account for relevant factors that may influence the resulting diagnostic parameters. The Glaucoma Module Premium Edition (GMPE) offers new scan patterns and an updated reference database that was acquired using these new features and functionalities, designed to enhance and support the clinical assessment of glaucoma by accounting for anatomic variability of each eye to improve classifications.

#### **5.3.1 Clinical Assessment of Optic Nerve Head Parameters**

The retinal ganglion cell (RGC) axons comprise the RNFL with axons exiting the eye via the optic nerve head. The health of the optic nerve head can be assessed based on the amount of neuroretinal rim tissue present. Because the axons exiting the eye make up a significant portion of the neuroretinal rim, its loss is associated with RGC and axonal degeneration, both of which are characteristic of glaucomatous damage. The optic disc constitutes the clinically visible surface of the neural and connective tissues of the ONH. The health of the neuroretinal rim is defined using two landmarks: the optic disc margin and the optic cup margin. These two landmarks define the outer edge (clinical disc margin) and inner edge (optic cup margin) of the neuroretinal rim. The amount of rim tissue then is estimated within the apparent plane of the disc margin as either the ratio of the size of the cup to the size of the disc [53] or the rim area [54]. An eye that exhibits a large CDR is indicative of potential glaucomatous damage as axonal loss results in expansion of the optic disc cup. However, both the optic disc and cup margin are defined subjectively and are difficult to delineate consistently [55]. Therefore, the resultant CDR and clinical neuroretinal rim quantification are variable. Furthermore, these concepts are applied whether the examination is performed with direct ophthalmoscopy, slit-lamp biomicroscopy, optic disc photography, or a number of quantitative imaging methods.

The more recent SPECTRALIS SD-OCT with OCT2 Module technology provides a new level of high-resolution imaging of ONH anatomic features that are affected in glaucoma. Clinicians can now visualize optic nerve structures such as the anterior and posterior lamina cribrosa surfaces, Bruch's membrane-retinal pigment epithelium complex and its termination within the ONH border, tissue of Elschnig, and the scleral canal opening [55–65]. Studies show that accurate colocalization of fundus photographs to SD-OCT image data allows clinicians to identify structures that correspond to common clinical landmarks such as the optic disc margin [56, 66]. SPECTRALIS imaging and measurement of the ONH landmarks support interpretation of fundus images and can objectively assist in clinical assessment of the nerve in three dimensions.

#### **5.3.2 Bruch's Membrane Opening (BMO) in SD-OCT-Based Neuroretinal Rim Measurements**

More recent SD-OCT imaging studies have challenged concepts of the clinical disc margin and rim quantification from both anatomic [55] and geometric [66–68] perspectives. The termination of Bruch's membrane at the ONH marks the opening through which retinal ganglion cell axons exit the eye to form the choroidal and scleral portions of the neural canal. Because axons cannot pass through an intact Bruch's membrane to exit the eye, this anatomic opening, termed Bruch's membrane opening (BMO), is a true anatomical border of the neural tissue. Thus, the BMO is a stable anatomical landmark from which neuroretinal rim measurements can be made (Fig. 5.4) [69].

However, Bruch's membrane is a very thin anatomical structure that is about 2–5 μm thick, and it appears as a hyper-reflective layer on SD-OCT that is approximately as thick as the RPE, around ~14–16 μm [70]. High-resolution and excellent signal-to-noise ratio (SNR) SD-OCT images are needed in order to detect the reflectance of bounding surfaces. It has been shown that the SPECTRALIS SD-OCT can consistently identify the BMO and that these images correlate to ground-truth ONH histology, as

**Fig. 5.4** The BMO represents a stable structure through which all axons exit the eye. Because blood vessels and axons cannot pass through Bruch's membrane, it is considered an appropriate anatomical boundary of the optic disc

**Fig. 5.5** (**a**) Electron microscopy of Bruch's membrane shows the basal membrane of the choriocapillaris, collagen layers, elastic layer, and basal membrane of the

shown in Fig. 5.5 [56, 66, 71]. Finally, the stability of BMO under a variety of conditions provides another rationale for its usefulness as a landmark over time [69]. BMO also is unaltered by large changes in IOP induced by glaucoma surgery, as the two-dimensional plane that best fits BMO is axially stable with surgical reduction of IOP [72].

The orientation of the neuroretinal rim relative to the BMO varies around the ONH because axons can exit the eye along varying paths, ranging from parallel to the visual axis to perpendicular to it [73]. In order to correctly account for these variations, studies have demonstrated that the minimum distance from BMO to the internal limiting membrane represents the most geometrically accurate measurement of neuroretinal rim width [66–68]. This neuroretinal rim measurement has been termed Bruch's Membrane Opening—Minimum Rim Width (BMO-MRW). Studies have demonstrated the usefulness of BMO-MRW in the detection of progressive ONH change in experimental animal models of glaucoma and in human eyes [69, 74]. The BMO-MRW is diagnostically specific and sensitive for detecting glaucoma, and it enhances the clinical

RPE. Panels (**b**) and (**c**) show ONH histology of Bruch's membrane, along with its corresponding appearance on SD-OCT. Images courtesy Christian Mardin, MD

assessment of the optic disc [75]. The BMO-MRW parameter provides better diagnostic performance than the original gold-standard HRT analyses [76]. Ultimately, the BMO-MRW and RNFLT measurements have been shown to complement each other in the assessment and monitoring of glaucoma [77].

The SPECTRALIS GMPE optic nerve headradial and circle (ONH-RC) scan acquires 24 radial and three concentric circle scans, with diameters of 3.5, 4.1 and 4.7 mm, centered on the BMO (Fig. 5.6). The radial scans define 48 BMO points that serve as the basis for the BMO-MRW measurements, and the three circumpapillary RNFL scans offer complementary RNFL thickness measurements that capture valuable information away from the optic nerve head. The BMO-MRW measurements of the radial scans and the RNFL measurements of the three circle scans are both adjusted for BMO area and age. The circle scans show comparable diagnostic performance, and in patients with large areas of peripapillary atrophy, the outer scans may offer reliable RNFL measurements when the conventional 3.5 mm scan is confounded by atrophy [78].

**Fig. 5.6** The ONH-RC scan pattern produces 24 line scans and 48 BMO endpoints, shown in (**a**). The three concentric circumpapillary scans have a diameter of 3.5 mm (**b**), 4.1 mm (**c**), and 4.7 mm (**d**). The three scans often offer complementary information and can confirm the presence of focal RNFL wedge defects that broaden away from the ONH (white arrows). Images courtesy Maria Pilar Bambó, MD, PhD

#### **5.3.3 Anatomic Variation: Position of the Fovea Relative to the Center of the ONH**

On average, the fovea is located 7° below the level of the center of the ONH, but the angle can vary from 6° above to 29° below [79]. Although the positions of the fovea and ONH center vary considerably between subjects, the anatomic path of RNFL bundles is governed primarily by these two structures as the bundles approach the ONH and exit the eye [80, 81]. In fundus images, the positions of the fovea and ONH also may vary slightly within the same individual from day to day because of cyclotorsion [82], but the path of RNFL bundles remains constant relative to the fovea-BMO center (FoBMOC) axis as shown in Fig. 5.7 [81].

The FoBMOC axis could significantly vary between the two eyes of one individual. A clinical example of this disparity is shown in Fig. 5.8. If these variations are not taken into account, it may lead to artificially large inter-individual differences in sectoral measurements, reducing the diagnostic precision of the device. Errors in mapping ocular structures to the visual field also may be induced that could contribute to the somewhat poor correlation between measures of structure and function observed in glaucoma [83, 84].

To account for these issues, the SPECTRALIS GMPE offers a proprietary feature called the "Anatomic Positioning System" (APS). Image acquisition using the APS ensures that OCT images are acquired at fixed and known retinal locations relative to certain anatomical landmarks: the center of the fovea and the center of Bruch's membrane opening. The process of defining the APS landmarks is semi-automated within data acquisition, and the operator is able to adjust and confirm the landmarks that the device detects (Fig. 5.9). All subsequent GMPE scans are aligned to the baseline landmarks and are automatically oriented according to the patient's FoBMOC axis. FoBMOC-aligned scans ensure all eyes are anatomically aligned correctly and compared with healthy control eyes regardless of anatomical differences, thereby improving accuracy of the sector analysis (Fig. 5.8).

While the APS landmarks serve as the basis for accurate baseline measures of each individual eye, the previously mentioned TruTrack eyetracking feature accounts for changes in head position and ensures precise placement of follow up scans, both important for maintaining accurate

**Fig. 5.7** Even as the angle of the fovea to the center of the ONH changes due to cyclotorsion or anatomical differences, the arcuate path of the RNFL bundles remains constant to this axis. This can be seen from the tracing of the RNFL fibers from the nerve to the fovea in (**a**). For 11

different eyes, the optic nerve position varies relative to the fovea as shown in (**b**), but alignment of the images in (**c**) illustrates the consistent path of the RNFL bundles. Figure 15 of [81], reprinted with permission

**Fig. 5.8** The range of angles for the fovea to center of ONH axis is large across a population. In these five examples, the fovea is +1.5° in (**a**), −3.3° in (**b**), −8.3° in (**c**), −11.9° in (**d**), and −16.0° in (**e**). Variability can also occur between the two eyes of a single patient, as seen in (**f**) with

an angle of +0.4° OD and −11.0° OS. The three white lines through the ONH in (**f**) represent the 6-sector Garway-Heath classification regions and are shifted according to the angle

comparison to the reference database and when assessing progression [85].

#### **5.3.4 Anatomic Variation: ONH size and Ocular Magnification Impact RNFL Measurements**

In 1996, Schuman et al. reported that a circle diameter of 3.4 mm was the most accurate and reproducible scan size for RNFL thickness measurements [86]. Since then, most OCT instruments and studies have used circular scans with a diameter very close to 3.4 mm, independent of ONH size. However, it is now generally recognized that the optic disc size shows a high interindividual variability, with areas ranging between 0.8 and 6.00 mm2 in normal eyes [87]. Histological studies have shown that RNFL thickness decreases with increasing distance from the optic

**Fig. 5.9** The operator confirms the automated detection of both the fovea (**a**) and the BMO (**b**) positioning within the acquisition window. These anatomic positioning sys-

disc margin [88]. Because of this, RNFL thickness is increased in larger optic discs when measured with a fixed scan size [89–91]. These studies indicate that using a fixed scan diameter

tem (APS) landmarks serve as the placement points for the ONH-RC scan

without adjusting for ONH size introduces inconsistencies and reduces measurement accuracy. Scaling scan diameter according to the ONH margin may provide a more accurate diagnostic RNFL thickness measurement. For this reason, the SPECTRALIS GMPE results account for the size of the ONH (defined as BMO area) when comparing each eye's RNFL thickness and BMO-MRW values with the respective reference database.

Visualization and SD-OCT imaging of the ONH are also affected by ocular magnification [92–96]. Magnification is determined by two factors: axial length and corneal power. In eyes with longer axial length, the actual diameter of a fixedsize OCT scan will be larger when it reaches the retinal plane. The cornea provides approximately two-thirds of the eye's total optical power and plays a significant role in determining the scan size on the retina. Without ocular magnification corrections, OCT scans may not correctly scaled, leading to inconsistent measurements of RNFL thickness. The SPECTRALIS GMPE software allows the user to adjust for magnification during acquisition by entering individual corneal curvature values and by bringing the optic nerve into sharp focus on the cSLO image (Fig. 5.10).

#### **5.3.5 Factors that May Confound Measurements and Classifications: Age, Axial length, and Tilted Discs**

There is a negative association between RNFL thickness and age that may explain the higher rate of glaucoma detection in older individuals [97]. The GMPE reference database accounts for this known decline in RNFL thickness due to age. The values collected for the reference database also show a statistically significant negative correlation between age and BMO-MRW, and the software RBD comparisons take into consideration this relationship. The reference database also shows a negative correlation between BMO area and MRW (larger BMO area associated

**Fig. 5.10** The operator can input the corneal curvature value for each eye so that the fixed-millimeter circle scans are correctly scaled relative to the fundus cSLO image and corresponding ONH size. This figure shows the range that the circle scans cover across the minimum and maximum corneal curvature values. This eye has a measured corneal curvature (CC) of 7.7 mm. Note that with a high CC value, the circle scans are closer to the disc margin than they

should be. With lower CC values, the scans are farther from the disc margin than they should be. Because the RNFL is thinner with increasing distance from the disc margin, the sectorial and global results compared with the reference database are considerably different due to changes in scaling and magnification that are caused by the disparate corneal curvature values

with thinner MRW) and a positive correlation between BMO area and circumpapillary RNFL thickness (larger BMO area associated with thicker cpRNFL measurements), so the reference database comparisons also are scaled by the BMO area.

As axial length and spherical equivalent (SE) refractive error increase, the measured average RNFL thickness decreases [98, 99]. This anatomic relationship can decrease the diagnostic power of a reference database, and most devices have a defined range of SE values of −6 to +6 diopters. Even within the included diopter range, the RNFL thickness profile plotted in the circular TSNIT (temporal-superior-inferior-nasal-temporal) profile can be shifted (Fig. 5.11). Considering these anatomical variations, when the SD-OCT reference database metrics do not agree with visual field tests and/or the clinical examination, it may be helpful to review the SD-OCT B-Scans in order to evaluate the overall appearance of the RNFL to help confirm a positive or negative glaucoma diagnosis. The high-quality SPECTRALIS B-Scans offer the detail to evaluate these structures. It may also be useful to consider macular GCL thickness and BMO-MRW measurements in such cases [100, 101].

The tilted disc phenomenon is another confounding anatomical feature that can affect glaucoma diagnosis. Tilted discs can be congenital or, more commonly, can occur in cases of myopia. In these eyes, the axons exit the eye via the ONH at angles that make assessment of the BMO center and BMO-MRW challenging. In addition, such anatomic anomalies result in arcuate RNFL patterns that are not accounted for in SD-OCT reference databases, making RNFL thickness comparison inconclusive, especially in the temporal region [100, 102]. Also, the distribution of RNFL in eyes with tilted discs is shifted according to the direction of the tilt [103]. Visual field defects also may mimic glaucomatous defects, further confounding the presentation [104]. These characteristics should be considered when applying SD-OCT to the interpretation of RNFL measurements in eyes with tilted discs (Fig. 5.12). A brief inspection of the SPECTRALIS cSLO image and the BMO-MRW radial B-Scans can clearly show BMO-MRW asymmetry and confirm the presence of a tilted disc.

#### **5.3.6 Posterior Pole: Macular and Asymmetry Analyses**

The density of RGCs is highest in the macula, and the ganglion cell layer (GCL) measured by SD-OCT is thickest surrounding the fovea. Loss of these cell bodies has been shown to be indicative of early glaucomatous damage [105]. Studies have also shown that glaucomatous damage results in characteristic patterns of ganglion cell degeneration in the macula. These patterns of loss present as arcuate patterns that correspond to the arcuate RNFL patterns of loss, confirming that the ganglion cell somas and their respective axons are degenerating. Such observations have led to the concept of "macular vulnerability zones" in the temporal inferior and temporal superior sectors [81]. Therefore, when reviewing OCT diagnostic results, it is important to assess anatomically corresponding ONH, RNFL and macula data in order to detect patterns common to glaucomatous damage.

The superior and inferior GCL are symmetric across the fovea in healthy eyes, and assessment of vertical GCL asymmetry across the fovea may be a sensitive method for detection of early glaucomatous damage [106]. Early damage also may be present asymmetrically between eyes [107, 108]. Macular analysis may be especially important for patients classified as "Glaucoma Suspects" by ophthalmic exam, and a study found that glaucoma suspects with macular thinning were more likely to subsequently present with visual field loss [109].

The GMPE posterior pole horizontal (PPoleH) scan offers macular thickness maps of total retinal thickness as well as the individual GCL, inner plexiform layer (IPL), and macular RNFL thicknesses. The Posterior Pole Asymmetry Analysis (PPAA) is derived from PPoleH total retinal thickness, and offers a quantitative and illustrative method to assess the asymmetric loss of macular tissues between the superior and inferior macula as well as between eyes (Fig. 5.13). This

**Fig. 5.11** In cases of increased axial length, the RNFL thickness values in the superior and inferior regions may be shifted towards the nasal or temporal sectors. In (**a**), the normal TSNIT profile has peaks that correspond to the age and BMO-adjusted reference database mean (green shading, solid green line). In a case of myopia (**b**), the increased axial length causes a shift of the superior and inferior

feature allows clinicians to confirm that the patterns of loss observed on the PPAA and the total retinal thickness maps is in agreement with ganglion cell degeneration patterns that are charac-

RNFL thickness peaks. This causes the 6-sector Garway-Heath analysis to flag the sectors "Outside Normal Limits," but visual inspection of the SD-OCT B-Scans shows a normal, healthy RNFL. Images courtesy Daniel Fuller, OD, Michael Gerstner, OD, and Christopher Lievens, OD, MS

teristic of glaucoma (Fig. 5.14). Regardless of which layers are assessed, macular parameters have been shown to add value as diagnostic tools in the detection of glaucomatous damage [110].

**Fig. 5.12** A titled disc can be seen on the fundus photograph (**a**), and the corresponding BMO endpoints can be identified on the radial scans through the ONH (**b**). Similar to cases of increased axial length, there can be shifts in the TSNIT profile of BMO-MRW that cause

false-positive comparisons to the reference database. Visual inspection of the SD-OCT B-Scans reveals a healthy neuroretinal rim. Images courtesy Mohammad Rafieetary, OD

#### **5.3.7 Detection of Glaucomatous Progression with OCT**

Considering that most forms of glaucoma are slowly progressing optic neuropathies characterized by the loss of RGCs and their axons, the detection of glaucomatous progression is a critical aspect of disease management. The identification of structural glaucomatous changes, such as progressive thinning of the RNFL and narrowing of the neuroretinal rim, assists clinicians in confirming the initial diagnosis. More importantly, detecting these changes over time provides clinicians with the information they need to make decisions about further treatment to prevent additional glaucomatous visual impairment.

In order to accurately assess progression, test measurements need to have adequate reproducibility [111, 112]. If test measurements have poor reproducibility or are contaminated by noise, detection of true structural loss is not possible. Pierro et al. evaluated retinal nerve fiber layer thickness (RNFLT) measurements using seven different OCTs (spectral- and time-domain), to assess inter- and intra-operator reproducibility of RNFLT [113]. They determined that the SPECTRALIS SD-OCT showed the best reproducibility among the tested devices. The SPECTRALIS eye tracking and ART image acquisition help provide high-quality OCT B-Scans, which in turn allow for reproducible segmentation of the RNFL. In a separate study of

**Fig. 5.13** The 61-line posterior pole horizontal scan produces a total retinal thickness color map (lower left). The 8 × 8 grid of thickness values serves as the basis for the Posterior Pole Asymmetry Analysis (PPAA) in the lower right panel. The difference in thickness for each corresponding square across the horizontal meridian is shown,

with darker values indicating a larger asymmetric difference in thickness. This analysis can be used to visualize and quantify areas of retinal thickness loss that are characteristic of glaucoma. Images courtesy Shinji Ohkubo, MD, PhD, and Kazuhisa Sugiyama, MD, PhD

clinically relevant reproducibility, Wessel et al. used the SPECTRALIS SD-OCT to measure circumpapillary RNFLT (cpRNFLT) in healthy controls and glaucoma patients over 3 years [114]. Glaucoma subjects were classified as progressing or non-progressing by masked grading of stereoscopic optic disc photographs, and the study showed that the SPECTRALIS measured a cpRNFLT loss of 0.6 μm/year in healthy eyes, a loss of 1.2 μm/year in non-progressing eyes, and a loss of 2.1 μm/year in progressing subjects. Miki et al. showed that the rate of global RNFL loss was more than twice as fast in eyes that developed visual field defects compared with eyes that did not develop a defect [16]. These findings indicate that SD-OCT imaging can be used to detect glaucomatous changes beyond losses from normal aging and beyond the possible noise in repeated measurements.

The ability of a device to measure change over long periods of time also depends on its dynamic range (i.e. the maximum and minimum RNFL thickness measurements that can be reliably made). The lower end of the dynamic range is the "floor" of the measurement, or the minimum layer thickness that is reliably measureable. It is important for an SD-OCT device to have a large dynamic range and a low "floor" value in order to be able to monitor disease progression over time, particularly in advanced glaucoma. The global cpRNFLT dynamic range of the SPECTRALIS has been shown to be larger than comparable devices, while also offering a lower floor [115]. Nevertheless, monitoring advanced glaucoma

**Fig. 5.14** Segmentation of the PPoleH scans between the RNFL and the ganglion cell layer (GCL) provides a GCL thickness color map. In this eye, the same as in Fig. 5.13, there is clear GCL loss in the inferior temporal region

using standard structural and functional testing is extremely difficult for the treating clinician because both standard structural and functional tests that usually guide treatment decisions are of diminished value. Standard structural measures have a limited dynamic range and visual field (VF) test points are more variable in advanced disease [81, 115–120]. However, Belghith et al. have shown that even in very advanced glaucoma, structural loss can be detected in some eyes using standard global structural measures with macular GCIPL identifying the highest proportion of eyes with detectable change, followed by MRW and cpRNFL [121]. In a subsequent study, Bowd et al. concluded that in advanced glaucoma, more macular tissue remains above the measurement floor compared with other measurements, suggesting that macular thickness is the better candidate for detecting progression of such eyes and that progression with SD-OCT measurements is observable in advanced disease [122–125].

#### **5.3.8 Summary**

Because every eye is unique, it is also important for imaging technologies to incorporate analysis tools that allow for the detection of change in a specific eye over time. The SPECTRALIS SD-OCT provides follow-up scans that are coregistered to baseline imaging, which improves repeatability and makes measurement more precise. The combination of high reproducibility, large dynamic range, a low measurement floor and multiple diagnostic parameters such as the BMO-MRW, cpRNFL and macular thickness measurements allows the device to offer a precise and clinically sensitive progression analysis for all stages of the disease.

Several studies have investigated the diagnostic performance of various SD-OCT parameters in a standard ophthalmic environment, and the literature suggests that circumpapillary RNFL thickness, ONH, and macular parameters are specific and sensitive for the detection of glaucoma [107, 126–128]. The SPECTRALIS GMPE software offers multiple measurements of ocular structures that, taken together, can offer confirmatory information and increase confidence in the diagnosis. It has been shown that multiple parameters, when used effectively and with caution, are better than any single parameter for diagnosis and management of glaucoma [129]. More importantly, there is clear utility in looking beyond reports at the individual OCT scans, as these can help clarify and reconcile outlying and aberrant outputs.

#### **5.4 Summary and Outlook**

The development and clinical implementation of cSLO technology (Heidelberg Retina Tomograph) was one of the first imaging technologies in ophthalmology that introduced the ability to objectively quantify various diagnostic parameters for the assessment and management of glaucoma. The technology's diagnostic parameters offered clinicians an objective and precise method to aid their decision in the diagnosis and management of the disease. While the clinical implementation of the HRT served to improve patient care, several clinical research studies employed SLO technology to derive potential surrogate endpoints in clinical trials.

SD-OCT is a more recent technology which also provides objective and quantitative methods to assess the optic nerve head as well as the retinal nerve fiber layer and macula. A current version of this technology, the SPECTRALIS SD-OCT with OCT2 Module, provides a new level of high-resolution imaging of ONH anatomic features that are affected in glaucoma. This device provides clinicians with the ability to visualize optic nerve structures such as the anterior and in some eyes posterior lamina cribrosa surfaces, Bruch's membrane-retinal pigment epithelium complex and its termination within the ONH border, tissue of Elschnig, choroid, and the scleral canal opening.

The ability to visualize the relevant ocular structures in three dimensions in-vivo for the diagnosis and management of glaucoma and to carefully implement multiple objective parameters enables clinicians to make more confident diagnostic decisions. Structural assessment using the imaging technologies discussed in this chapter provides reproducible quantitative measurements of posterior segment ocular structures relevant to the disease.

A newer development in OCT technology, OCT Angiography (OCTA), has sparked interest in evaluating vascular alterations in the retina and ONH for diagnosis, staging, and monitoring in glaucoma. OCTA is an extension of OCT which allows non-invasive visualization of the retinal vasculature by detecting signal changes induced within perfused blood vessels without the use of exogenous dye. In principle, OCTA compares sequential B-Scans acquired at the same location to detect change. As stationary structures would appear static in sequential B-Scans, changes detected by OCTA are largely attributed to erythrocyte movement in the perfused vasculatures.

Because OCTA offers the non-invasive assessment of microvasculature in the peripapillary retina and macula, it is being investigated for its potential to assess ocular hemodynamics in various diseases [130]. Several OCTA studies have shown reduced microcirculation in the peripapillary retina and the superficial macula of openangle glaucoma eyes, with a moderate relationship between microvasculature and function [131–133].

An early OCTA study suggested that the OCTA vessel density parameter may identify glaucomatous damage before focal visual field defects are detectable [132]. Another study showed that macular vessel density results correlate with central 10-2 visual field defects [133]. The same group also concluded that macular and peripapillary vascular density measurements detected changes in retinal microvasculature before visual field damage in the unaffected eyes of glaucoma patients presenting with unilateral glaucoma [134]. Moghimi et al. showed that lower baseline OCTA parameters were associated with a faster rate of RNFL progression in mild to moderate glaucoma over a mean follow-up of 27 months, suggesting that decreased vessel density may be a risk indicator for progression [135].

It remains controversial whether OCTA measurements have a higher diagnostic performance for glaucoma detection compared to conventional OCT measurements such as RNFL thickness, neuroretinal rim width, and macular ganglion cell and inner plexiform layer thickness. Chen and colleagues demonstrated that OCTA-measured vessel density and circumpapillary RNFL thickness measurements had comparable diagnostic performance for the detection of glaucoma suspect and glaucoma eyes [136]. Another recent study compared the diagnostic performance for glaucoma detection between OCTA vessel density measurements (defined using a non-commercially available method) and structural OCT RNFL thickness measurements. Results indicated that vessel area density measurements had a significantly smaller area under the receiver operating characteristic curve for classifying healthy versus glaucoma eyes than RNFL thickness measurements [137], in disagreement with several similar studies. However, classification performance of healthy and glaucoma suspect eyes were similar for vessel area density and RNFL thickness measurements. There is also evidence that deep-layer microvasculature dropout of the parapapillary choriocapillaris or microvasculature within the sclera occurs in eyes with glaucoma and is associated with more severe visual field damage and occurs more frequently in eyes with disc hemorrhages and RNFL thinning [63, 138–140].

While findings from the literature comparing OCTA and structural OCT measurements for the detection of glaucoma and evaluation of structurefunction association are divergent, the potential ability to elucidate the temporal sequence of vascular changes and optic nerve damage in glaucoma may pave the way for better understating and management of the disease.

During the past three decades, advancements in ophthalmic diagnostic imaging technologies have enabled the ability to detect glaucomatous neuropathy at early stages of disease. These advanced imaging technologies provide large amounts of reproducible data, allowing clinicians to discriminate between normal and glaucomatous optic nerves in a more systematic way. From the earlier stages of cSLO imaging to the more recent implementations of OCT technology, several decades of technical and clinical work have been united and integrated in a high-technology solution to give physicians the ability to look deep in the pathological process of glaucoma and to develop better diagnostics and therapeutic strategies. The development of ophthalmic diagnostic imaging technology and the continued efforts to enhance it continues to benefit patients that suffer from glaucoma.

Gerhard Zinser, PhD was an inspiration to us all. He was a visionary in ophthalmic imaging and an innovative scientist who pursued collaborations and supported new ideas from investigators and clinicians from all over the world. He recognized the importance of designing instruments that produce the best quality scans possible, while also making sure users understood the strengths and limitations of the technology. I have strong memories of sitting for hours with Gerhard and our reading center team in San Diego reviewing images, developing and refining quality control criteria and discussing strategies for analysis to ensure that the highest quality information would be available for the Diagnostic Innovations in Glaucoma Study (DIGS), the African Descent and Glaucoma Evaluation Study (ADAGES) and the Ocular Hypertension Treatment Study (OHTS). Our research was accelerated through his support of promising research in our laboratory, and numerous discussions about how to translate the results into clinical care. His passing is a great loss to the vision science community. (Linda Zangwill, PhD)

**Acknowledgement** Supported in part by National Institutes of Health/National Eye Institute grants R01 EY029058 (RNW), R01 EY027510 (LZ), R01 EY011008, R01 EY014267, Core Grant P30EY022589, and by an unrestricted grant from Research to Prevent Blindness (New York, NY).

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coherence tomography optic disc margin anatomy. Invest Ophthalmol Vis Sci. 2009;50(10):4709–18.

over time? Longitudinal analysis using san diego automated layer segmentation algorithm (SALSA). Invest Ophthalmol Vis Sci. 2016;57:675–82.

domain-optical coherence tomography in advanced glaucoma. J Glaucoma. 2014;23:341–6.

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# **OCT Angiography (OCTA) in Retinal Diagnostics**

Roland Rocholz, Federico Corvi, Julian Weichsel, Stefan Schmidt, and Giovanni Staurenghi

#### **6.1 Introduction**

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique which can be used to provide three-dimensional visualization of perfused vasculature of the retina and choroid [1, 2]. In contrast to standard structural optical coherence tomography (OCT, see Chap. 5), OCTA analyzes not only the intensity of the reflected light but also the temporal changes of the OCT signal. Based on repeated OCT section images (B-Scans) from the same location of the retina, it is possible to separate the temporal signal changes caused by moving particles (such as erythrocytes flowing through vessels) from other sources of signal change (i.e. eye motion or noise in the OCT signal). Thereby image contrast between perfused vessels and static surrounding tissues can be created as illustrated in Fig. 6.1.

Using dense volume scans, it is possible to obtain OCTA images that are similar to fluorescence angiography images, which are the clinical gold standard. In contrast to fluorescence angiography, OCTA has the advantage of not requiring any dye injection. Moreover, while fluorescence angiography provides only two-dimensional images of the fundus, OCTA enables the visualization of structure and blood flow within the vitreous, the retina, and the choroid, separately (see Sects. 6.2.2 and 6.2.3). Using appropriately adjusted segmentation boundaries, it is also possible to examine the distinct capillary networks of the retina (with vessel diameters as small as approx. 8 μm) [3]. The definition of the separating boundaries has evolved since the introduction of OCTA in the clinical practice and is described in Sect. 6.2.4.

Various OCTA algorithms have been proposed and utilized in research and in clinical devices for OCTA image construction (see Sect. 6.2.1). Therefore OCTA images from different devices vary in appearance [4, 5], which may result in different clinical diagnostic interpretations. While each unique OCTA algorithm is subject to slightly different limitations that are attributed to its overall approach, there are certain confounding factors and/or limitations that impact all algorithms and are innate characteristics of this imaging modality [5]. These factors include, but are not limited to, reduced light penetration in deeper layers and image artifacts projected from more superficial layers to deeper ones. Artifacts can originate from image acquisition, eye motion,

<sup>©</sup> The Author(s) 2019 135 J. F. Bille (ed.), *High Resolution Imaging in Microscopy and Ophthalmology*, https://doi.org/10.1007/978-3-030-16638-0\_6

R. Rocholz (\*) · J. Weichsel · S. Schmidt Heidelberg Engineering GmbH, Heidelberg, Germany

F. Corvi · G. Staurenghi Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco", Sacco Hospital, University of Milan, Milan, Italy

**Fig. 6.1** Example of how the OCT signal intensity changes over time, after bulk motion correction. (**a**, **b**) Structural OCT images were acquired with a time difference of 8 ms. The location of a larger blood vessel (yellow circle) and of static tissue (blue circle) is indicated in both

images. (**c**) Upon magnification of these areas and calculation of the absolute differences, larger signal changes can be seen within the blood vessel compared to the static tissue

image processing, and display strategies [5]. Section 6.3 describes some of the major artifacts related to OCTA as well as state-of-the-art countermeasures.

Section 6.2.5 briefly introduces OCTA metrics, which are intended for quantitative evaluation of OCTA data. Such numerical aggregates of the image data enable an objective analysis of disease progression and statistical conclusions in larger studies of diseases.

With fluorescence angiography, namely Fluorescein Angiography (FA) and Indocyanine Green Angiography (ICGA), dynamic phenomena such as dye leakage, pooling, and staining can additionally be observed. These phenomena cannot be observed with OCTA because no motion of blood cells is involved. While these phenomena are also used in clinical diagnosis [6], retinal pathology can also be obscured by leakage or hemorrhage. In contrast, OCTA can generate high contrast, well-defined images of the microvasculature below areas of leakage or hemorrhage [7]. Therefore, dye-based angiography and OCTA are giving complementary information. To illustrate the similarities and differences of OCTA with respect to the gold standard dye-based angiography, Sect. 6.4 provides side-by-side comparisons for clinical cases of diabetic retinopathy, retinal vein occlusion, macular telangiectasia, and age-related macular degeneration.

#### **6.2 Technical Foundation for Clinical OCTA Imaging**

OCT systems typically produce section images as shown in Fig. 6.1. Such images are commonly referred to as B-Scans. As can be seen in Fig. 6.1a, the B-Scans show a grainy pattern, also known as speckle pattern. These speckles are inherent to the interferometric OCT measurement. If two B-Scans are taken from the very same location of the retina (cf. Fig. 6.1a, b), the speckle pattern at locations of static tissue basically stays the same. In contrast, at locations of perfused blood vessels, the speckle pattern changes over time. The basic principle of OCTA is therefore to analyze the temporal variation of the OCT signal in order to derive an image of the perfused retinal vasculature.

To allow for the creation of images similar to fundus images from standard angiography, volume scans are performed. This means, that multiple adjacent B-Scans are acquired to cover extended regions of the retina. Eventually, these B-Scans are combined to form a three-dimensional sample of the retinal structure and blood flow.

#### **6.2.1 OCTA Signal Processing and Image Construction**

In OCTA imaging, several OCT B-Scans of the same retinal cross section are acquired repeatedly in short succession. Within this retinal cross section, at locations of static tissue, the microscopic configuration of illuminated scattering particles in the beam focus is well preserved over sequential acquisitions and consequently yields a consistent OCT signal over time. Contrarily, at locations where directed motion is present in the sample, like in retinal blood vessels, the scattering particles are continuously replaced by other particles in subsequent acquisitions. This continuous exchange of microscopic particles modulates the OCT signal and introduces an additional source of variability to the repeated measurements. Overall, the smaller the remaining portion of conserved scattering particles in the beam waist, the higher the variability in the OCT signal of sequential acquisitions. Maximum OCT signal variability is observed when the scattering particles are completely replaced by others in subsequent measurements. Clinical OCTA imaging on current commercially available OCT device hardware is typically operating in this regime, as the physiological blood flow speeds in most of the perfused retinal vasculature [8] significantly exceeds the velocity limit (i.e. typically only few mm/s) determined by the product of OCT beam waist diameter and scan repetition rate. Hence, it is unlikely to observe the same red blood cell configuration in two successive B-Scans. This, however, is a necessary requirement for deriving quantitative blood flow velocity measurements from the OCT signal variation in repeated scans (cf. Chap. 7). Accepting this current technical limitation of clinical OCT devices, OCTA algorithms for signal construction rather focus on reliably differentiating locations of significant blood flow from static tissue, instead of measuring blood flow velocity quantitatively. In this context, OCTA image construction is a quasibinary classification problem with the goal to optimally distinguish significant flow from static tissue at each location of the retina.

Different algorithmic strategies have been suggested in the past for optimally addressing this classification task. While some algorithms are using exclusively either amplitude or phase of the complex OCT signal, others are combining information from both. As the microscopic pattern of illuminated particles in the sample and its detailed configurational change according to motion is practically inaccessible to measurement, its influence on the resulting OCT signal is typically rather considered probabilistically as a stochastic contribution to the overall measurement. OCTA algorithms are thus quantifying the amount of variability in the random realizations of the measured OCT signal from repeated acquisitions in one or the other way. Practically, for instance the temporal correlation [3] or the overall variance [4] of the signal or other more involved statistical parameters [2] with an expected relation to the OCT signal variability are assessed in different approaches. These statistical parameters, potentially after additional post-processing and contrast enhancement, are subsequently taken as the resulting OCTA signal in arbitrary units. As an alternative to these statistical parameter based OCTA signal construction methods, underlying probabilistic statistical models for the random OCT signal from sample locations with and without directed flow can be derived from theory and experiment [9–11]. Based on these underlying models and the repeated OCT signal observations, a probability for being static (versus in flow) can be assigned to each measured location. This holds the advantage of yielding easily interpretable probability values and no further contrast enhancement of the resulting OCTA signal is needed.

#### **6.2.2 OCTA Data Visualization**

The acquisition of OCTA volume scans yields three-dimensional data of the retinal structure and blood flow. To visualize and analyze such rich datasets, different image representations are used, as illustrated in Fig. 6.2. To separately visualize the vascular networks (or plexuses) in the retina (cf. Sect. 6.2.4) and pathological alterations in normally avascular tissue, the review of OCTA data is typically based on so-called *en face* images that are generated from within slabs of the acquired volume. In this context, the term slab refers to a section of finite axial extent in the volumetric data delimited by an anterior and a posterior boundary surface. These slab boundaries are usually determined by layer **Fig. 6.2** Visualization of OCT angiography data is mainly based on *en face* images and section images. (**a**) *En face* image of the structural OCT data within the superficial vascular plexus. In the background, an infrared cSLO fundus image is shown. (**b**) *En face* image of the corresponding OCTA data. (**c1**–**c3**): OCT/ OCTA fusion images of a section along the fast scanning axis (B-Scan direction, green). (**c1**) Section image shows structural OCT in the background and the OCTA data as yellow overlay. (**c2**) Same as (**c1**), but the structural OCT data is faded out. (**c3**) Same as (**c1**), but the OCTA data is faded out. (**d**) OCT/OCTA fusion image where section is along the slow scanning axis (orthogonal to B-Scan direction, blue)

**d**

segmentations of the structural OCT data (see Sect. 6.2.4). The OCTA signal between the two boundaries is accumulated in axial direction using different projection methods (see Sect. 6.2.3) and is displayed as a two-dimensional image. The resulting images give the impression of looking onto the retina and are therefore referred to as *en face* images or transverse section images. An example is given in Fig. 6.2a, showing the *en face* image of the structural OCT, and Fig. 6.2b, showing the respective *en face* image of the OCTA signal.

In addition to *en face* images, section images are used for review of the spatial relationship of retinal structure and blood flow. The section images may have the same orientation as the originally acquired B-Scans (Fig. 6.2c) but may also be arbitrarily oriented within the volume; for instance Fig. 6.2d shows a section orthogonal to the original B-Scans. To provide a direct visual correlation of structural and flow information, structural OCT section images and the corresponding OCTA blood flow information at the same location can be superimposed; see Fig. 6.2c1–c3, d.

#### **6.2.3 Projection Methods**

The generation of two-dimensional *en face* images from the three-dimensional data, *OCTA*(*x*, *y*,*z*), employs a projection along the *z*-direction (i.e. axial direction). Common projection methods are the mean projection, μ(*x*, *y*), and the sum projection, *s*(*x*, *y*), which can be discretized at voxel-level and written as

$$\mu\left(\mathbf{x},\boldsymbol{\upchi}\right) = \frac{1}{z\_l\left(\mathbf{x},\boldsymbol{\upchi}\right) - z\_u\left(\mathbf{x},\boldsymbol{\upchi}\right)} \sum\_{z=z\_l\left(\mathbf{x},\boldsymbol{\upchi}\right)}^{z\_u\left(\mathbf{x},\boldsymbol{\upchi}\right) - 1} OPT\left(\mathbf{x},\boldsymbol{\upchi},z\left(\mathbf{x},\boldsymbol{\upchi}\right)\right)$$

$$s\left(\mathbf{x},\boldsymbol{\upchi}\right) = \sum\_{z=z\_l\left(\mathbf{x},\boldsymbol{\upchi}\right)}^{z\_u\left(\mathbf{x},\boldsymbol{\upchi}\right) - 1} OPT\left(\mathbf{x},\boldsymbol{\upchi},z\left(\mathbf{x},\boldsymbol{\upchi}\right)\right)$$

where *zl*(*x*, *y*) is the posterior slab boundary surface, and *zu*(*x*, *y*) is the anterior slab boundary surface, so that the slab comprises all data with *z* ∈ (*zu*,*zl*] ⊂ *ℤ*. Note, the only difference between these two projection methods is the normalization factor. The mean projection is normalized by the local slab thickness, *d*(*x*, *y*) ≡ *zl*(*x*, *y*) − *zu*(*x*, *y*), while the sum projection is not normalized. This has important implications for the visual interpretation of the resulting *en face* images and also for the use of these images in OCTA analytics. To appreciate the differences, assume that the OCTA algorithm achieves a perfect result without any artifacts or noise, i.e. *OCTA*(*x*, *y*,*z*) is 1 for any voxel corresponding to a perfused retinal location and 0 for any voxel corresponding to a nonperfused location. In this idealized setting μ(*x*,*z*) can be interpreted as a density or fill-factor of perfused vessels in the slab, i.e. the ratio of perfused voxels to all voxels in the depth range from *zl*(*x*, *y*) to *zu*(*x*, *y*).The sum projection, *s*(*x*, *y*), corresponds to the total lumen of perfused vessels in the given depth range. Both measures can be meaningful, depending on how the images are read or further processed. The density of perfused vessels in a given slab may give direct insight into the relative oxygenation of this slab. But due to laterally varying slab thickness, *d*(*x*, *y*), the contribution of small capillaries in the mean projection also laterally varies within the same slab. This is illustrated in Fig. 6.3. The contribution to the mean projection is greater if a capillary is located somewhere with small slab thickness (Fig. 6.3b) as compared to a same sized capillary at a location of greater slab thickness (Fig. 6.3c). Considering that the slab thickness might also vary over time (due to swelling of the retina or treatment of fluid accumulations), the contribution of capillaries in the mean projection also changes over time, even if the capillary's perfusion is unchanged. In contrast, the sum projection always gives equal weight for each voxel, independent on the local slab thickness. This means that all capillaries of a given diameter contribute equally to the sum projection. On the other hand, using the sum projection for slabs with laterally varying thickness may give the false impression of non-perfusion at locations where the slab is thin and only comprises single capillaries, when compared to a location where the slab is thick enough to embrace more than one layer of capillaries. To avoid this false impression, it is important to realize that the sum projection is related to the perfused vessel lumen at each lateral location, which is naturally confined by the local slab thickness.

The previous examples were given to illustrate the difference when reading the *en face* images. For OCTA analytics based on *en face* images there are further aspects that need to be

**Fig. 6.3** Comparison of OCTA sum and mean projection. (**a**) Fusion image with SVC slab boundary segmentation (red lines). (**b**, **c**) Detailed fusion images show the physiological thinning of the superficial vascular plexus from

the macula to the periphery. (**d**) Sum projection of the SVC slab. (**e**) Mean projection of the same slab. Blue and orange arrows show the location of (**b**) and (**c**), respectively

considered. When using the mean projection as a measure for vessel density within several slabs of varying thickness, it is not possible to directly compare the results because voxels were given different weights in the different slabs. Also averaging of mean projections from different slabs will not give the same result as the computation of the mean projection of the combined slab.

#### **6.2.4 Retinal Vascular Plexuses**

The retinal vascular network in the human eye is axially divided into four distinct capillary plexuses. While the vasculature within each plexus is densely linked, interconnecting vessels between these sub-networks are sparse in comparison. Each separate capillary plexus holds characteristic morphometric features that have been confirmed ex-vivo in confocal microscopy [3, 12] as well as by using OCTA in vivo [13, 14]. From the anterior boundary of the retina to more posterior axial locations, the four distinct plexuses are, nerve fiber layer vascular plexus (NFLVP), superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP); compare Fig. 6.4.

While the three deeper layers can be observed and axially separated even in the periphery of the retina using OCTA at sufficient axial resolution, the NFLVP is most pronounced at locations where the nerve fiber layer holds a substantial width, like in the peripapillary region as well as parafoveally (Fig. 6.5) [14].

In order to accurately detect and manage retinal vascular conditions, it is important to precisely discern the different retinal vascular plexuses. It is also important that slabs enable a continuous representation of the retinal and choroidal vasculature so that possible vascular abnormalities are not missed during image review. Currently, conflicting definitions of the axial location of bound-

**Fig. 6.4** Definition of the slab boundaries. Left: Schematic figure of the layers and vessel networks in the human retina (www.he-academy.com/Retinal-Layers-Interactive). Right: Schematic figure of the slab definitions. *SVC* superficial vascular complex, *NFLVP* nerve fiber layer vascular plexus (part of SVC), *SVP* superficial vascular plexus (part of SVC), *DVC* deep vascular complex, *AC* avascular complex, *ICP* intermediate capillary

plexus (part of DVC), *DCP* deep capillary plexus (part of DVC), *CC* choriocapillaris. Figure modified from https:// www.heidelbergengineering.com/download.php?https:// media.heidelbergengineering.com/uploads/Products-Downloads/210111-001\_SPECTRALIS\_Tutorial\_ SPECTRALIS-OCTA-Principles-and-Clinical-Applications\_EN.pdf [16]

aries between retinal plexuses make the direct comparison of *en face* images from different devices difficult [16].

To best separate distinct capillary plexuses within the deep vascular complex, Campbell et al. suggested to define optimum slab boundaries based on the location of minima of the axial flow density profiles [13]. They introduced boundary definitions relative to the thickness of the segmented retinal layer. As an alternative to this approach, a subsequent study using full spectrum OCTA at higher axial resolution found that it is also possible to define these interfaces between the three deeper layers at constant absolute offsets to the retinal IPL-INL interface (Fig. 6.5) [14]. This conveniently reduces the number of required retinal segmentations that are necessary for creating individual *en face* visualizations of these plexuses (cf. Fig. 6.6c, d).

#### **6.2.5 Quantification of OCTA Data**

For objective assessment of disease progression and its documentation, and to enable comparisons to normative data, a concise summary of the

Temporal Displacement from Fovea along Fovea-BMOC Axis (degrees)

**Fig. 6.5** (**a**) Representative *en face* OCTA image of the superficial retinal vasculature from the optic nerve head across the fovea to the temporal periphery in a healthy eye. (**b**) Heat map of the OCTA signal in depth averaged over 22 healthy eyes, spatially averaged within the transparent red overlay displayed in (**a**). Hot locations (white) indicate strongest OCTA signal while cold locations (black) indicate minimal OCTA signal. Up to four axially distinct capillary plexuses can be detected in the peripap-

image data in terms of numerical measurements is desirable. Such numeric parameters, describing the structure of the vasculature network as derived from the OCTA images, are also referred to as "OCTA metrics" or "OCTA analytics". Clearly, this must not be confused with OCT-based flowmetry (subject of Chap. 7), where physical blood flow velocity is measured quantitatively.

Typical examples of OCTA metrics parameters, quantifying static structural aspects of the

illary region as well as parafoveally. At peripheral temporal locations, three distinct plexuses are separated. The retinal layer interface between IPL and INL, when shifted anteriorly and posteriorly by constant appropriate distances, represents a conveniently defined separating boundary for visualizing the three deeper plexuses independently within *en face* projections. Image from [14] reproduced without changes according to license http:// creativecommons.org/licenses/by/4.0/

eye's vasculature, are various vessel density measures (vessel/perfusion density, binarized vessel density or vessel area density, skeletonized vessel density or vessel length index). These parameters are suitable for capturing dropout of vasculature that occurs in diseases like diabetic retinopathy, retinal vein occlusion or glaucoma [17–20]. A quantification of flow void area is also possible, for instance for assessing the choriocapillaris structure [21].

**Fig. 6.6** Comparison of two examinations of the same eye. (**a**) *En face* image of the superficial vascular plexus (SVP) from 30° × 15° scan acquired with resolution of 11 μm/pixel, providing a large field of view. (**b**–**d**) *En face* images acquired of a 10° × 10° scan with 5.7 μm/pixel resolution. (**b**) The small capillaries are better resolved in the SVP *en face* image of high resolution scan, compare yellow outline in A which shows the same region of the

Besides vessel density, other parameters that summarize morphological features of vessel branches and vessel network structure, including complexity measures such as vessel tortuosity, fractal dimension, or branching point densities and vessel diameter statistics, are in common use [17– 19, 22, 23]. These measures aim at capturing pathological alterations of vessel shape and spatial arrangement, as occurring for example in diabetic

same eye. (**c**) The intermediate capillary plexus (ICP) can be clearly distinguished from the deep capillary plexus (**d**) due to the high axial resolution of ~3.9 μm/pixel (SPECTRALIS OCTA). The ICP and DCP vessel networks show clearly distinct geometric structures. In the DCP, star-like vascular intersections can be discerned which may represent a connection to the venous superficial network

retinopathy, macular telangiectasia, or neovascularization in age related macular degeneration.

These characteristics may be analyzed for the whole scan area, or alternatively as aggregates over sectors defined by specific grids (e.g. ETDRS grids), which are usually adapted to the eye anatomy and allow for the detection of spatially localized changes over time or deviations from the statistics of normal reference data.

In currently available approaches, vessel density measures are interpreted as a two-dimensional density, i.e. the fraction of area of a slab projection occupied by detected vessels (typically after applying a thresholding operation). As long as the instrument is able to axially resolve the different layers of capillaries that can be anatomically distinguished, quantitative OCTA parameters can be derived for each of them independently. To suppress the influence of larger vessels, two approaches are commonly used: Either slab projections of vessels are reduced to their centerlines (i.e. "skeletonization"), or larger vessels are simply masked out. This emphasizes thinner capillaries in the analysis.

Further quantitative parameters derived from OCTA data are area and shape measurements of specific vasculature regions, in particular the foveal avascular zone (FAZ) [19, 22] or segmented neovascular lesions [24].

There is high interest to use results from quantitative OCTA parameters as endpoints in clinical studies [25–27]. For this purpose, OCTA metrics need to be both repeatable and reproducible. Therefore, initial studies mostly focused on analyzing the robustness of the measurements. Errors in scan geometry, evaluation grid placement, layer segmentation, and parameters such as variable signal strength can negatively impact the measurement precision. Furthermore, data from instruments of different vendors are not directly comparable [28, 29]. This is due to differences in resolution as well as signal generation and postprocessing algorithms such as filtering, artifact suppression and thresholding [30]. Also differences in the slab definitions (cf. Sect. 6.2.4) and layer segmentation results of different devices need to be carefully taken into account when comparing images or quantitative analysis results across devices.

#### **6.3 Image Artifacts and Countermeasures**

#### **6.3.1 Projection Artifacts**

The OCT light passage through larger superficial vessels can introduce disturbances to the probing beam in deeper retinal layers. The light fluctuates because it has passed through and is altered by moving blood cells above. Current OCTA algorithms cannot distinguish these signal fluctuations from the fluctuations of moving blood cells in the deeper layer. This gives rise to apparent replications of superficial vessels in posterior layers. These erroneous replications are referred to as OCTA projection artifacts [5]. Projection artifacts are introduced during data acquisition and are not a consequence of the projection method for *en face* image generation. While projection artifacts were understood as the most confounding factor early on in the inception of OCTA technology [5, 7], this limitation has been addressed in current state-of-the-art devices by means of a post-processing step which is referred to as projection artifact removal [4, 31, 32]. Typically, in this step either the undesirable replication is subtracted from *en face* projections of the deeper layer by algorithmic comparison to its source in the superficial vasculature or the deeper axial signal is suppressed at selected locations based on heuristic rules [32]. In general, projection artifacts are more prominent in deeper layers of high signal intensity, such as the retinal pigment epithelium (RPE). Therefore, it is important that the projection artifact removal does not lead to disruption of the visualization of structures such as choroidal neovascularization (CNV) which may grow through the RPE. For example in Fig. 6.7a, various large vessels seem to be connected to a large CNV and the actual extent cannot be easily assessed. With projection artifact removal, Fig. 6.7b, the artificial replication of the vessels from superficial layers can be removed without disrupting the visualization of the pathology.

#### **6.3.2 Segmentation Artifacts**

Considering that slabs are mainly defined by automatically segmented retinal layer boundaries, careful review of the segmentation is critical for correct interpretation of the *en face* projections. Segmentation failures are especially common in diseases where the appearance and shape of retinal layer is altered. For instance, intrareti-

**Fig. 6.7** A subject with a large type 1 CNV was examined with 11.4 μm/pixel resolution using SPECTRALIS OCTA. (**a**) OCTA image without projection artifact removal, various large vessels seem to be connected with

the neovascularization. (**b**) After removal of the projection artifact, the actual size and the extent of the neovascularization can be assessed

nal fluid, large pigment epithelial detachments, choroidal neovascularization, and certain atrophies often cause segmentation errors in state-ofthe-art OCTA devices. A manual correction of such errors is cumbersome, if the correction is based on individual B-Scans within the dense volume. To facilitate and speed up the process of correcting compromised slab boundaries, interactive segmentation correction tools have been introduced. These tools propagate manual corrections of only a few B-Scans to the remainder of the volume, by fusing automatic segmentation results with these user-provided hints [33]. These countermeasures are merely a workaround until more robust segmentation methods for the vascular networks are available.

#### **6.3.3 Motion Artifacts**

Adequate compensation of eye motion is one of the most critical aspects of OCTA acquisition. In order to detect temporal changes in the OCT signal related to blood flow in capillaries, eye motion needs to be detected and compensated for very accurately. Therefore sampling schemes and different eye tracking implementations play a crucial role for each device's overall performance of blood flow visualization at the capillary level.

Eye movements affect the acquisition of OCTA data in two distinct ways. First of all, the detection of blood flow related changes in the OCT signal from repeated B-Scans requires spatial overlap of these scans (see Sect. 6.2.1). This means, that the scans must overlap within the lateral width of the probing beam, which is typically in the order of 15 μm. Secondly, larger eye movements can lead to geometrical distortion or missing data in the OCTA volume scans. The volume scans are typically acquired within several seconds. On this time scale, it is very likely that larger eye movement occurs due to saccades, change of fixation, or change of head pose.

Slow eye drifts within the B-Scan plane can be compensated for by image registration of the successive B-Scans, effectively removing the temporal signal change in regions of static tissue (cf. Fig. 6.1). Larger eye motion (saccades) or eye motion perpendicular to the B-Scan plane can strongly deteriorate the OCTA signal, because of insufficient spatial overlap of the B-Scan samples. This leads to missing data and geometrical distortions in the OCTA volume scans, if no countermeasures are taken. The goal of any motion artifact compensation is to ensure that OCTA data acquired from visit to visit is devoid of errors that may reduce the precision of quantitative change analyses.

There are two major approaches to mitigate motion artifacts in OCTA. One approach is to acquire several independent OCTA volume scans and combine the information in post-processing [34–36]. The second approach is using real-time tracking.

The post-processing approach has the advantage of relatively short acquisition times. In practice, often only two volume scans with perpendicular orientation of the fast scanning axis are used [36]. In effect, the resulting volume is obtained by interpolation and averaging of the different input volumes. Larger data gaps from one volume are normally filled up with information from the other volume, but in general, there is no guarantee to obtain distortion-free results [5].

The real-time tracking approach employs accurate measurements of the eye motion in realtime (see also Chap. 3). B-Scans which are affected by too strong motion are re-acquired. During periods of slow eye motion (eye drifts) the real-time eye motion measurements can also be used to actively control the OCT scanners to keep the beam on the nominal scanning path. Due to the data filtration in the event of strong eye motion, the real-time tracking approach is sometimes slow in acquisition. However, using realtime tracking, the obtained volumes are geometrically accurate and uniformly sampled without gaps from missing data.

#### **6.3.4 Lateral and Axial Resolution**

The lateral resolution of an OCT system is determined by both the optical point spread function (PSF) as well as sampling density, i.e. the digital resolution. The OCTA signal from one voxel can be seen as a mixture of contributions from scatterers within the support of the combined (optical and digital) PSF. The larger the PSF, the more scatterers contribute to the mixture so that is becomes more challenging to separate the individual contributions, in particular the components due to flowing scatterers (blood cells) from the static components (tissue) according to their discriminating statistics.

The optical PSF is influenced by the imaging system as well as the imaged eye. Poor adjustment of the instrument's focus as well as aberrations in the eye widens the PSF and lead to suboptimal signal separation. Similarly, using wide-field optics with smaller numerical aperture as well as less dense digital sampling in favor of covering larger fields of view compromises the ability to resolve small capillary details. The effect of the sampling density on the visibility of small capillaries is illustrated in Fig. 6.6, compare a and b.

The axial resolution in OCT is independent of the lateral resolution and is determined by the spectral bandwidth of the light source [37]. However, using split-spectrum approaches [34] for OCTA processing, an algorithmic trade-off can be made between axial resolution and signal to noise ratio. Splitting the spectrum allows to obtain, from a single scan, several B-Scan sections of lower axial resolution (due to the lower bandwidth of the spectral sub-bands), which are then used as additional samples with independent shot noise contributions for improving the OCTA signal. This loss of resolution may impede the ability to separate axially closely spaced but distinct vascular layers, in comparison to algorithms that maintain the axially high optical resolution of the underlying OCT signals (cf. Fig. 6.6c, d) [14].

#### **6.4 Clinical Application of OCTA**

#### **6.4.1 Diabetic Retinopathy**

Diabetic retinopathy (DR) is classified into different stages according to stereoscopic color fundus photographs. The typical features of DR are microaneurysms, intraretinal hemorrhages, intraretinal microvascular abnormalities, venous beading, cotton wool spots, hard exudates and neovascularization.

The gold standard imaging technique for the assessment of macular perfusion is fluorescein angiography [38]. It is able to show leaking microaneurysms and capillary non-perfusion. However, it produces only two-dimensional images in which fluorescence signals of the superficial and deep capillary networks overlap and are difficult to distinguish, especially when the dye leaks [38, 39]. In this context, OCTA is a useful imaging technique for the assessment of retinal vasculature in the different capillary networks.

Fluorescein angiography is a dynamic examination that can be used to accurately evaluate the perfusion status of the retina at the posterior pole and in the far periphery. In contrast, the application of OCTA in the assessment of peripheral vasculature is quite controversial. In fact, one of the main deficiencies of this imaging technique is related to its limited field of view suggesting a use for diseases affecting the macular region. However, several features of DR have the potential to be imaged and more accurately classified with OCTA as it offers the opportunity to show various retinal vascular layers [40, 41]. In fact, OCTA is currently being included into clinical trials of patients with DR. There may be prospects to describe the evolution of the DR as OCTA offers the opportunity to evaluate the quantification of perfusion through vascular density maps, and the potential for feature identification such as identifying microaneurysms or specified regions of non-perfusion (Figs. 6.8, 6.9, and 6.10) [7, 17].

#### **6.4.2 Retinal Vein Occlusion**

Retinal vein occlusion is the second most prevalent blinding vascular retinal disorder [42, 43]. The main types are central retinal vein occlusion (CRVO) and branch retinal vein occlusion (BRVO), depending on the site of obstruction [44]. Examples are shown in Figs. 6.11 and 6.12.

The ophthalmoscopic signs are venous dilation, tortuosity, intraretinal hemorrhage, retinal edema and hemorrhage in the vicinity of the occluded vein. Fluorescein angiography typically reveals delayed filling in the distribution of the involved retinal vessels. The veins are dilated and tortuous. There is leakage from capillaries with dye accumulating in the substance of the retina or within cystoid spaces. Fluorescein angiography has been the gold standard for visualizing retinal non-perfusion areas which appear as dark areas in the images [38]. Retinal neovascularization can be identified by leakage of the fluorescein dye. Fluorescein angiography has been critically important for the diagnosis and the treatment decision.

OCTA shows the areas of vascular nonperfusion, the dilated tortuous venous segments, the microvascular abnormalities and the neovascularization. However, one of the most sight threatening complications is macular edema. In this context, it is crucial to verify the segmentations used for OCTA visualization [45]. The difficulty with *en face* imaging is that selective enlargement of retinal layers occurs and these layers are typically not segmented correctly. The resultant *en face* vascular images may not show the correct representations of the actual flow characteristics.

#### **6.4.3 Macular Telangiectasia**

Idiopathic perifoveal or juxtafoveolar retinal telangiectasia are retinal capillary ectasia limited to the perifoveal area without any apparent specific cause [46].

Initially, idiopathic macular telangiectasia were divided into four groups according to the Gass classification [47]. Subsequently, Yannuzzi proposed a new classification in which type 1 was defined as aneurysmal telangiectasia and type 2 as perifoveal idiopathic macular telangiectasia [48].

Type 1 is closely related to Coats disease, or more specifically a milder form of Coats previously known as Leber miliary aneurysms. It generally involves only one eye, and both the peripheral retina and macula can be affected. Fluorescein angiography reveals telangiectasia and multiple capillary, venular and arteriolar aneurysms with late leakage. Type 2 are bilateral, temporal and symmetrical, however, there have been reports of unilateral, asymmetric, and asymptomatic cases [49]. It has been hypothe-

**Fig. 6.8 Multimodal imaging of diabetic retinopathy.** Fluorescein angiography (**a**, **b**) and the corresponding optical coherence tomography angiography images segmented at the superficial retinal capillary plexus (**c**) and the deep capillary plexus (**d**). Fluorescein angiography revealing vascular changes with microaneurysms and an area of non-perfusion in the temporal part. Optical coherence tomography angiography showing microaneurysms and a similar aspect of the area of non-perfusion

**Fig. 6.9 Multimodal imaging of macroaneurysm.** Infrared reflectance (**a**) showing fundus abnormalities on a temporal vascular arcade corresponding to vascular dila-

tion on OCT (**b**). Fluorescein angiography (**c**, **d**) and OCTA (**e**) revealing the macroaneurysm with a similar representation

**Fig. 6.10** Multimodal imaging of diabetic retinopathy. OCTA images segmented at the superficial retinal capillary plexus (**a**, **b**) and the deep capillary plexus (**c**, **d**), the corresponding fluorescein angiography in the early (**e**, **f**) and more advanced phase (**g**, **h**) and the corresponding

B-Scan (**i**, **j**). OCTA showing microvascular changes, microaneurysms, vascular dropout and a ragged appearance of the foveal avascular zone. Some microaneurysms are seen on the fluorescein angiography but not on the OCTA images

**Fig. 6.11** Multimodal imaging of retinal vein occlusion. Fluorescein angiography (**a**) and the corresponding optical coherence tomography angiography images segmented at the superficial retinal capillary plexus (**b**) and the deep capillary plexus (**c**). Fluorescein angiography revealing vascular changes and area of non-perfusion in the temporal part of the retina. Optical coherence tomography angiography showing well the area of vascular nonperfusion with a similar appearance of fluorescein angiography

**Fig. 6.12** Multimodal imaging of retinal vein occlusion. Fluorescein angiography (**a**–**c**) and optical coherence tomography angiography (**b**, **d**) showing vascular

changes, areas of non-perfusion and neovascularizations. The B-Scans (**e**, **f**) reveal the retinal neovascularizations projecting into the vitreous space

sized that the primary involvement is the alteration of Müller cells and secondary the vascular and tissue remodeling. Fluorescein angiography displays the capillary telangiectasia with dilated and blunted retinal venules at right angles into the temporal parafoveolar area.

OCTA shows the prominent right-angle veins with the distortion of the foveal avascular zone with cavitations. Idiopathic macular telangiectasia could be complicated by retinal choroidal anastomosis, visible in indocyanine green angiography and OCTA (Fig. 6.13).

#### **6.4.4 Age Related Macular Degeneration**

Several studies have shown the utility of OCTA in the diagnosis and monitoring of age related macular degeneration (AMD). The advent of OCTA offered the opportunity to non-invasively visualize the neovascular networks and correlate the OCTA appearance with the standard imaging techniques and observe new findings.

In this context, age related macular degeneration is characterized by different neovascular lesions. Type 1 neovascularization occurs between the retinal pigment epithelium and Bruch's membrane, while Type 2 neovascularization is characterized by the growth of the neovascular tissue through the retinal pigment epithelium-Bruch's membrane-choriocapillaris complex to the subretinal space [50, 51]. OCTA is able to show the neovascular network with a similar appearance to indocyanine green angiography. However, indocyanine green angiography is a dynamic examination that may reveal the feeder vessel of the lesion, while OCTA is not able to provide a dynamic representation of blood flow and to detect the feeder vessel (Fig. 6.14, 6.15, 6.16, and 6.17).

**Fig. 6.13** Multimodal imaging of Type I idiopathic macular telangiectasia. Fluorescein angiography (**a**, **b**) and indocyanine green angiography (**c**, **d**) showing the dilated telangiectatic perifoveal vessels with leakage in the late

phase. OCTA at the superficial retinal capillary plexus (**e**) and deep (**f**) retinal capillary plexus revealing the dilated telangiectatic perifoveal vessels. The B-Scan (**g**) displaying the dilated telangiectatic vessel with flow sign inside

In the context of Type 1 lesions, focal polypoidal changes of the neovascular tissue could be found [52]. The gold standard to detect polypoidal lesions is indocyanine green angiography where single or multiple focal nodular areas of hyperfluorescence are arising from the choroidal circulation with or without an associated branching vascular network. The ability to detect polypoidal lesions by OCTA is not well defined. In the majority of cases, OCTA is not able to show the lesion due to the low velocity of the blood flow inside and then the final appearance is an area of absence of signal (Fig. 6.18).

Type 3 neovascularization may originate from both circulations simultaneously as initial focal retinal proliferation and progression, or focal retinal proliferation with preexisting or simultaneous choroidal proliferation, or initial focal choroidal proliferation and progression [53]. Type 3 neovascularization can be visualized as a discrete

**Fig. 6.14** Multimodal imaging of Type 1 neovascularization. Early to late phase of fluorescein angiography (**a**) showing pinpoints of hyperfluorescence. Early to late phase of indocyanine green angiography (**b**) revealing central hyperfluorescent area corresponding to type 1 neovascularization. Optical coherence tomography angiography (**c**) displaying a well-defined neovascular network under the retinal pigment epithelium (**d**)

**Fig. 6.15** Multimodal imaging of Type 2 neovascularization. Early (A) phase of fluorescein angiography showing a well-defined neovascular network with leakage in the late phase (**b**). Early (**c**) and late (**d**) phases of indocyanine green angiography revealing the neovascular network. Optical coherence tomography angiography in the

3 × 3 mm (**e**) and 6 × 6 mm (**f**) showing the neovascular network with clearly visible and defined margins. Optical coherence tomography (**g**) displaying the detachment of retinal pigment epithelium with subretinal hyperreflective material and subretinal fluid

high flow linear structure extending from the middle retinal layers into the deep retina, which sometimes extent throughout the retinal pigment epithelium on OCTA. In Fig. 6.19 OCTA reveals a branching vessel anastomoses with the deep retinal capillary plexus and goes into the outer retina and eventually into the subretinal pigment epithelium space.

In contrast, geographic atrophy (GA) is a well-established end-stage manifestation of AMD [54, 15]. It results from the degeneration of photoreceptors, retinal pigment epithelium, and choriocapillaris. In this context OCTA is a very useful imaging modality to observe the presence of CNV at the peripheral border of atrophy. In fact, the ability to detect neovascularization with the standard angiographic examination could be challenging due to the alteration of retinal pigment epithelium and exposure of normal choroidal vessels. In this context, OCTA is a very useful technique that is able to show the neovascular network with the appropriate segmentation (Fig. 6.20). Moreover, OCTA was used to evaluate the status of choriocapillaris in patients with GA. A general loss of choriocapillaris flow associated with drusen and subretinal drusenoid deposits was found in OCTA.

**Fig. 6.16** Multimodal imaging of Type 2 neovascularization secondary to pathologic myopia. MultiColor imaging (**a**) and blue autofluorescence (**b**) showing fundus abnormalities related to pathologic myopia. Early phase (**c**) and late phase of fluorescein angiography (**d**) revealing the Type 2 neovascularization as an hyperfluorescent area that become

more intense with moderate leakage in the late phase (**d**). Early phase (**e**), late phase (**f**) of indocyanine green angiography and OCTA (**g**) displaying the neovascular network with well circumscribed appearance at the border of atrophy. OCT/OCTA B-Scan (**h**) showing the subretinal hyperreflective material corresponding to the neovascular lesion

**Fig. 6.17** Multimodal imaging of Type 2 neovascularization secondary to angioid streaks. Blue autofluorescence (**a**) revealing the fundus alterations secondary to angioid streaks. Indocyanine green angiography (**b**) and fluorescein angiography (**c**) showing an hyperfluorescent area that become more intense with moderate leakage in the late phase (**d**), as Type 2 neovascularization. Optical coherence tomography displaying the area of atrophy with the neovascular tissue above the retinal pigment epithelium without sub/intraretinal fluid (**e**). Optical coherence tomography angiography in the 6 × 6 mm (**f**) and 3 × 3 mm (**g**) showing a choroidal neovascularization with a defined network that closely follows the trajectory of the angioid streak—well appreciable on the *en face* optical coherence tomography (**h**)

**Fig. 6.18** Multimodal imaging of polypoidal neovascularization. Fundus autofluorescence (**a**) showing diffuse alteration of retinal pigment epithelium and areas of atrophy. Fluorescein angiography (**b**, **c**) revealing a diffuse hyperfluorescence and hypofluorescence points. Indocyanine green angiography in the different phases

(**d**–**g**) revealing the Type 1 neovascular network with hyperfluorescent polypoidal lesion. OCTA (**h**, **i**) showing the central neovascular network, as Type 1 neovascularization, one polypoidal lesion but not the polypoidal lesion in the temporal area

**Fig. 6.19** Multimodal imaging of Type 3 neovascularization. Fluorescein angiography (**a**, **b**) and indocyanine green angiography (**c**) revealing the neovascularizations (arrowheads) as two round hyperfluorescent points with leakage in the late phases. The two white lines indicate the exact location of the optical coherence tomography sections (**d**, **e**) showing the detachment of retinal pigment epithelium with intraretinal cystoid space. Optical coherence tomography angiography (**f**) revealing a tuft-shaped, high-flow lesion (open arrowheads) in the outer retinal layers abutting into the sub-retinal pigment epithelium space

**Fig. 6.20** Multimodal imaging of geographic atrophy. Fluorescein angiography (**a**, **b**) revealing the central area of atrophy as a hyperfluorescence area with staining. Indocyanine green angiography (**c**, **d**) showing well evidently the medium-large choroidal vessels under the atrophic area. *En face* optical coherence tomography (**e**) and optical coherence tomography angiography (**f**) showing

the area of atrophy with rarefied choriocapillaris and Sattler layer. Optical coherence tomography (**g**, **h**) displaying hypertransmission of the signal below the level of the retinal pigment epithelium and into the choroid resulting from loss of scatter or attenuation from overlying retinal pigment epithelium and neurosensory retina

**Fig. 6.20** (continued)

#### **6.5 Conclusion**

In this article the basic principles, major sources of artifacts and the clinical application of OCTA were discussed. The discussion of the clinical examples showed that OCTA provides diagnostic value in several vascular diseases of the eye. However, the current state of OCTA has not yet fully replaced the gold standard dye-based angiography because of important limitations.

Ongoing endeavors to improve OCTA are addressing these shortcomings.

This includes targeting greater fields of view, which is especially important in DR. Improvements to automatic segmentation algorithms in the context of pathological alterations are necessary for reliable results. Based on these future improvements it is expected that robust metrics and sensitive monitoring of disease progression can be achieved.

#### **References**

1. Spaide RF, Klancnik JM Jr, Cooney MJ. Retinal vascular layers imaged by fluorescein angiography and optical coherence tomography angiography. JAMA Ophthalmol. 2015;133(1):45–50.

noise-bias correction for optical coherence tomography of the retina. Biomed Opt Express. 2018;9(2):486.

related macular degeneration in remission. PLoS One. 2018;13(10):e0205513.

tion in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns. Biomed Opt Express. 2012;3(6):1182–99.

G., Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110(9):1677–82.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

**7**

**OCT-Based Velocimetry for Blood Flow Quantification**

Boy Braaf, Maximilian G. O. Gräfe, Néstor Uribe-Patarroyo, Brett E. Bouma, Benjamin J. Vakoc, Johannes F. de Boer, Sabine Donner, and Julian Weichsel

#### **7.1 Introduction**

A complex network of blood vessels within the retina and the choroid ensures the perfusion of the photoreceptors, ganglion cells, retinal nerve fiber bundles and other retinal tissues essential for the visual system. Because various retinal diseases originate from pathologic changes in the local hemodynamics, localized blood flow measurements could potentially serve as a disease biomarker.

Fluorescence angiography is the standard clinical tool for qualitatively visualizing the structure of the blood vessel network in the back of the eye. Early detection of perfusion-related

M. G. O. Gräfe · J. F. de Boer Vrije Universiteit Amsterdam, HV, Amsterdam, The Netherlands

B. E. Bouma · B. J. Vakoc Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

S. Donner · J. Weichsel (\*) Heidelberg Engineering GmbH, Heidelberg, Germany e-mail<EMAIL_ADDRESS>diseases, monitoring disease progression, and evaluating the effectiveness of therapeutic intervention, however, could potentially be improved by robust methods for quantifying blood flow dynamics. Studies have shown the overall clinical potential of quantitative flow measurements in retinal diseases like AMD [1], glaucoma [1–4], diabetic retinopathy [5, 6] and others. Consequently, a number of different techniques have been developed for this purpose and the individual strengths and weaknesses of the various approaches have been discussed comprehensively in previously published review articles [1, 2, 7]. Despite progress, it remains difficult to reliably quantify blood velocities within individual blood vessels using these existing techniques. In addition, some methods report rather complex hemodynamic parameters, which are not easily interpreted and often dependent on the specific measurement method employed. Measurement uncertainty and variation have even led to contradicting study results in the published literature for the same disease and under otherwise similar conditions [7]. To date, no gold standard has been established for quantitative ophthalmic blood flow- and velocimetry.

Recently, OCTA became commercially available as a tool for qualitative, non-invasive, 3D visualization of the blood vessel network of the posterior eye. In research studies as well as in the daily clinical routine, OCTA is on the way to becoming an accepted imaging modality for the

B. Braaf · N. Uribe-Patarroyo

Wellman Center for Photomedicine, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA

<sup>©</sup> The Author(s) 2019 161 J. F. Bille (ed.), *High Resolution Imaging in Microscopy and Ophthalmology*, https://doi.org/10.1007/978-3-030-16638-0\_7

diagnosis of retinal vascular disorders including neovascularization [8, 9] and retinal vein occlusion [10]. Current clinical implementations of OCTA provide an almost binary discrimination between static tissue and blood vessels with sufficient flow, and a very limited ability for flow velocity quantification. This makes OCTA especially applicable in clinical scenarios when vessel perfusion drops out completely or when pathologic vasculature is growing into otherwise avascular regions of the retina. Statistical analysis of the OCTA signal mainly focuses on numerically evaluating the qualitative vascular signal and local features in the microvasculature geometry as well as the area of extended ischemic regions [11, 12] and avascular zones. Although these analyses are sometimes termed 'quantitative OCTA' in the literature, they are not based on actual quantitative OCT velocimetry measurements of blood flow. Instead the outcome of different established OCTA signal reconstruction algorithms is used in the analysis. As the visibility and detectability of blood flow can be highly dependent on the sensitivity of the specific OCTA algorithm [13, 14], these measurements are again not easily comparable between different devices. In order to arrive at a consistent uniform metric for quantitative blood flow measurement in the future, a robust, easy to use, and reliable tool for clinical flow quantification in the eye is in demand. OCT velocimetry holds the potential to meet these requirements using non-invasive measurements. The principle of OCT-based flow velocity measurements is straightforward; an incident OCT beam is scattered from moving erythrocytes within retinal vessels (Fig. 7.1) and by carefully controlling the spatial and temporal sampling of repeated measurements the underlying blood velocity can be derived from the modulation of the back-scattered signal.

This chapter describes the current status of quantitative OCT-based velocimetry methods for blood flow quantification. It builds upon the description of OCT and OCTA signal processing in Chaps. 3 and 6, respectively. In the first part of this chapter, the motivation and potential for OCT-based retinal flow measurements in clinical B. Braaf et al.

**Fig. 7.1** Schematic drawing of an OCT measurement on a blood vessel. The Doppler angle *α* is defined between the incoming OCT light beam and the direction of the flow velocity *V*. The incident light wave **Ψ***i* is partially reflected by the moving red blood cells into the scattered light wave **Ψ***s*. The blood cell velocity is derived by examining the modulation of the scattered light wave from repeated measurements in time. The figure is adopted from Braaf [15]