vessels and the retinal nerve fiber layer (Fig. 4.1b). Even with the arrival of digital imaging, monochromatic photography continued to be used frequently since monochromatic CCDs were initially less expensive and achieved higher resolution than color CCD cameras. However, with the advent of high-resolution color CCDs there is less reason to specifically take a “red-free” image since the green channel of a color photograph can be digitally selected and inspected. Similar analysis of other wavelengths may be useful as well. For example, a particular type of drusen, originally called reticular pseudodrusen, is much easier to see with either infrared or blue light as opposed to red or green light. In the past, a common technique to evaluate patients for the presence of pseudodrusen involved leaving the excitation filter used for fluorescein angiography in place while removing the barrier filter. Now, with digital color photography, it is easy to separate an image into the principle red, green, and blue channels, and the latter has been suggested for improved diagnosis of pseudodrusen [4].

Further advancements have involved a shift beyond the visible light spectrum. Commercially available scanning laser ophthalmoscopes (SLOs), such as one manufactured by Heidelberg Engineering, utilize near-infrared light to create a monochromatic image of the fundus. With near infrared light, the reflectance characteristics of fundus structures differ from that of visible light, and this information has diagnostic value (Fig. 4.1c). For instance, the optic nerve exhibits low reflectivity in near-infrared light and thus appears dark. Conversely, melanin reflects near infrared light, often accounting for the bright appearance of pigmented scars. In line with these physical principles, reticular pseudodrusen are readily visible with this type of SLO imaging.

Autofluorescence Imaging

Autofluorescent images are derived from stimulated emission of light from photoreactive molecular structures, particularly lipofuscin granules, within the RPE (Fig. 4.2a, b) [5–7]. Lipofuscin consists of a diverse group of molecules [7] that accumulate in all post-mitotic

cells as byproducts of the oxidative breakdown and metabolic rearrangement of various biochemical compounds, including polyunsaturated fatty acids and proteins. The retinoids present in lipofuscin are adept at light absorption in the visible light spectrum due to a moderate number of conjugated double bonds intrinsic to their molecular structure. This also forms the molecular basis for the autofluorescence of retinoid molecules, which can be captured either by an SLO or a fundus camera-based system.

Commercial SLO systems employ horizontal and vertical scanning mirrors to scan a specific region of the retina and create raster images viewable on a computer monitor. They utilize 488 nm light to excite lipofuscin and a long-pass filter starting at 500 nm as a barrier. Advantages of current SLO systems include integrated software that automatically adjusts the brightness and contrast of each image, which improves the overall quality of image acquisition, and confocal imaging methodology, which diminishes extra light by focusing detected light through a small pinhole [8]. In this way, greater image resolution is achieved since light only from the conjugate plane is used. Disadvantages of confocal SLO imaging include image noise, some variability from one image to the next, and a potentially variable but unknown alteration by the software in the production of the final image. In contrast, fundus camera-based systems use filters that are placed on the light path in a manner similar to that for fluorescein angiography, but with an excitation filter of 535– 585 nm (green) which targets the lower end of the absorption curve for lipofuscin, and a barrier filter of 605–715 nm. Fundus camera-based systems have lower incremental cost, low image noise, higher repeatability, and the ability to capture raw information from the patient. Their major drawback is the absence of automated image optimization. Raw images from fundus camera-based systems are often of lower contrast than the processed images that are automatically produced by confocal SLOs; thus enhancements must be performed manually by the user.

The source of lipofuscin in the RPE is the outer segments of the retinal photoreceptors. The main component of lipofuscin in RPE cells is A2E, which comprises two molecules of

Fig. 4.2 Geographic atrophy. (a) Digital color fundus photograph from an eye with geographic atrophy. (b) SLO fundus autofluorescence image showing absence of autofluorescence in the area of RPE atrophy. Areas of hyperautofluorescence at the border of the RPE atrophy are

seen (arrow). (c) SLO infrared image demonstrates the direction of subsequent OCT image. (d) OCT image shows disappearance of the RPE, with appearance of Bruch’s membrane. Increased OCT signal into the choriocapillaris is apparent underneath areas of RPE atrophy

trans-retinal and one molecule of phosphatidlyethanolamine. Precursors of A2E, including A2PE-H2, A2PE, and A2-rhodopsin, are each autofluorescent and aggregate in the outer segments of photoreceptors prior to phagocytosis by the RPE [9, 10]. Collectively, the components of lipofuscin inhibit lysosomal protein degradation [11], are photoreactive [12], and are capable of inducing RPE cellular apoptosis [13], specifically blue light-induced apoptosis [14]. In addition, A2E and its precursors are susceptible to oxidative stress and damage [15, 16], resulting from photo-oxidative reactions within the neighboring milieu that proliferate reactive oxygen species and free radicals [17]. Conditions that produce separation of the retina from the RPE impede the physiologic phagocytosis of shed outer segments. Excessive buildup of lipofuscin

precedes the degeneration of photoreceptors and associated geographic atrophy and represents a common pathway in the pathogenesis of various monogenetic and complex retinal diseases (Fig. 4.2) [18–20].

Pearl

Autofluoresence imaging may be useful for following and measuring geographic atrophy.

Optical Coherence Tomography

Optical coherence tomography (OCT) is an imaging technique capable of evaluating the macula in cross-section and with very high detail.

OCT imaging calculates distances within a target tissue by measuring the “echo time delay” of light, or the time it takes for light to be backscattered from the target tissue. This is analogous to an a-scan ultrasound. Multiple OCT “a-scans” may then be taken in a line, to form an image analogous to a b-scan ultrasound image, but using light instead of sound. Because light travels too fast to be detected directly, the echo-time delay must be measured indirectly, using a technique called low coherence interferometry. First, the light signal emanating from the OCT instrument is split in two using a beam splitter. One beam is sent to the eye and the other beam is sent to a reference mirror. The light then reflects back from both the target and the reference to a Michelson interferometer, where the signals combine to form an interference pattern [21, 22].

Low coherence refers to the light source used in OCT. In OCT, the axial and transverse resolutions are uncoupled. The axial resolution is determined by the bandwidth, one of the determinants of temporal coherence, of the light source. Coherence refers to correlation of different physical characteristics of light. Highly coherent light produces minimal interference, while less coherent light produces more complex interference patterns. Previous generations of OCT instruments used superluminescent diode light sources with bandwidths of ~25 um centered near a wavelength of 810 nm, and capable of 10 um axial resolution in the human eye. With wider bandwidth light sources (or less temporally coherent), the interference pattern becomes more complex, and allows for improved ability to localize echoes within a target, thereby increasing axial resolution [23]. Transverse resolution is independent of the bandwidth of the light, and is dependent on the optical focusing of light on the retina. Transverse resolution in commercially available OCT instruments is limited to 10 –15 mm by natural aberrations in the human eye [23, 24].

Interference between the reference and sample arms of the interferometer will only occur if the arms are nearly the exact length, therefore the distance that light travels to and from the reference mirror must equal the distance that light travels when it is reflected from a given intraocular

structure. In the original OCT systems (referred to as “time domain” OCT), the position of the reference mirror was continuously moved so that the time delay of the reference light beam matched the time delay of light echoes from various intraocular structures. As the reference mirror was moved, variations in optical reflectivity could be detected within the target tissue. In “Fourier-” or “spectral-domain” OCT systems, the interference signal is detected using a stationary reference arm. Instead of moving the reference arm to detect distances within tissue, multiple depths within tissue may be localized by analyzing the pattern of the interference signal, by taking the Fourier transform of this interference spectrum [25]. Because all light echoes from different axial depths in the sample are measured simultaneously rather than sequentially, imaging can be performed at much greater speeds than in previous “time domain” systems, and a greater wealth of OCT data becomes available.

Another integral component of OCT is the utilization of analysis software. Computer algorithms are used to calculate retinal thickness by automatically delineating the inner and outer retinal borders. When a series of OCT images are obtained through the macula, a topographic map of retinal thickness may be created. Previous time domain OCT (TD-OCT) systems used scanning modes that acquired six linear images centered at the point of fixation, spaced 30° apart and typically 6 mm in length. Newer spectral-domain OCT (SD-OCT) systems create macular maps using a much larger amount of OCT data; typically, a “raster” series of horizontal OCT images are used in macular mapping protocols. These various retinal thickness calculations may be compared to normative databases, or be followed over time or before and after treatment with extreme precision due to registration of a particular point of interest within the macula.

OCT measures changes in optical reflectivity within the retina, and can be useful in analyzing retinal microstructure. Although these optical reflections do not equate to in-vivo retinal structures, a high correlation between OCT and retinal histology has been shown [26, 27]. Nerve fiber layers (retinal nerve fiber layer, inner and outer plexiform layers) are more highly reflective on

Fig. 4.3 Classic choroidal neovascularization. OCT image demonstrates a partially-detached posterior hyaloid, subretinal fluid (SRF), CNV, and hard exudates (HE). The RPE is partially detached from Bruch’s membrane in the area of the foveola. Besides the RPE, two other hyper-reflective

OCT, while the cellular layers (ganglion cell layer, inner and outer nuclear layers) are less highly reflective. The external limiting membrane is visible as a moderately-reflective line toward the outer part of the outer nuclear layer. The outer aspect of the neurosensory retina is bounded by three closely-spaced reflective layers [28]. The inner of these two layers represents part of the photoreceptors, likely the junction between the inner and outer segments, and the outermost reflective layer represents the RPE. There is also an apparent layer between the RPE and inner segment/outer segment (IS/OS) junction, which may represent the tips of the photoreceptors or the interdigitation between the RPE and photoreceptor outer segments (Fig. 4.3). Retinal blood vessels are evident in OCT images by their shadowing of deeper retinal structures, as blood is highlyreflective, and scattering of OCT signal. In some cases, the posterior hyaloid is visible as a thin, moderately reflective line anterior to the retina. In areas of RPE loss or detachment, Bruch’s membrane may become visible, and in areas of RPE loss, OCT signal is able to penetrate into the choroid and sclera more readily (Fig. 4.4). OCT is also able to clearly delineate choroidal neovascularization (CNV). CNV that appears “classic” on FA typically appear anterior to the RPE, and “occult” CNV appears underneath the RPE.

outer retinal lines are noted. The photoreceptor inner-outer segment junction (IS/OS) is the innermost of these lines, and the outer segment/RPE interdigitation (OS/RPE) is the middle of these lines

Pearl

OCT imaging is an essential diagnostic tool for managing neovascular AMD.

Enhanced Depth Imaging

A limitation of SD-OCT is that the quality of the interference signal is not the same at all tissue depths. At the top of the SD-OCT image (the “zero-delay line”), signal as well as resolution are highest, but further down in the image, signal and resolution are worse due to increasing mismatches in path length. The degradation of image signal is perhaps more significant since it correlates with increasing image darkness. In a standard OCT image, the vitreoretinal interface is placed near the top of the image by the OCT technician, therefore the best OCT imaging capability is in the vitreous, while the poorest OCT signal and resolution are in the outer retina and choroid.

Spaide and colleagues described a technique called enhanced depth imaging OCT (EDI-OCT) that permits imaging of the choroid, and in very high myopes, of the sclera as well [29]. This technique involves pushing the SD-OCT instrument closer to the eye to obtain an inverted image. In contrast to a typical SD-OCT image, deeper