Ординатура / Офтальмология / Английские материалы / Optical Coherence Tomography in Age-Related Macular Degeneration_Coscas_2009

Advantages of SD-OCT in AMD

As a result of its improved resolution and scanning

2speed, SD-OCT has several advantages for imaging of AMD, particularly preservation of retinal topography, larger field-of-view of the retina, greater scan density, and improved correlation of the image set to fundus features.

The high-resolution SD-OCT image quality results in finer discrimination of retinal and subretinal layers. For example, small drusen are readily differentiated from normal retinal pigment epithelial (RPE) architecture (Figure 2C).

The scan density of SD-OCT systems can be improved by the increased scanning speed during the same acquisition time. The distance between consecutive images is reduced to tens of microns compared to hundreds or even thousands of microns spacing in TD-OCT (Leitgeb 200320, Choma 200321 ). Small lesions are therefore more likely to be detected on SD-OCT imaging.

Imaging may also extend across a larger field-of-view during the same acquisition time as a result of this high scanning speed.

Motion artifacts causing distortion of the retinal surface and retinal-RPE junction are decreased, avoiding errors of measurement of lesion dimensions, which is particularly important for imaging of small structures at the level of the retinal pigment epithelium (Figures 2A and 2B) (Stopa 200722).

Improved retinal topographic imaging enhances the evaluation of structural changes caused by epiretinal membranes, fluid accumulation, RPE elevations, and RPE detachments (Srinivasan 20065).

Quantitative measurements (eg, total volume) of macular changes such as subretinal fluid, drusen, CNV, and macular edema are more precise, facilitating analysis of response to therapy (Figure 14).

The higher imaging speed of SD-OCT also allows for 3D imaging (Figures 3A, 3B, and 3C).

By summation of all pixels on an axial line, a two-di- mensional image analogous to a fundus image, called the

Summed Voxel Projection (SVP), is created (Jiao 200523) (Figure 3C).

This technique allows the simultaneous creation of fundus images and OCT images. SVPs show features such as the macula, optic disc, and blood vessels and can be used to correlate OCT findings with fundus photographs or fluorescein angiograms (Figures 4 and 5).

1. Macula

Measurement and monitoring of retinal thickness is probably the most frequently used application of OCT in retinal disease, especially AMD, macular edema, diabetic retinopathy, and epiretinal membrane.

The Stratus OCT system can be used to calculate a map of macular thickness from 6 radial B-scans crossing at the fovea. The average thickness in 9 subfields centered on the fovea is calculated by interpolating data from these scans. Total macular volume is calculated in a similar way (Figure 6).

Spectral Domain OCT (SD-OCT) provides much higher resolution images (Figure 7).

However, algorithms are necessary to establish correlations with Stratus* measurements.

Thickness measurements may appear to be different from those obtained by TD-OCT. In TD-OCT imaging, the photoreceptor outer segments are often not differentiated from the RPE and are therefore excluded from the retinal thickness calculation (Figure 2A).

The SD-OCT system acquires higher resolution images of the multiple hyper-reflective layers that comprise the photoreceptor-RPE-choriocapillaris complex.

SD-OCT retinal thickness measurements may include the photoreceptor outer segment layer, which is visibly separate from the RPE in these high-resolution scans (Figure 2B).

Chapter 2 · Applications of Spectral-Domain OCT in AMD

Figure 3: Generation of 3D images.

A): Example of a single B-Scan (Prof. J.A. Yzatt’s research prototype system)

B): 3D representation of 100 B-scan images after axial registration.

C): SVP (summed voxel projection) 2D image.

This image was created by summation of all B-scans in an axial direction.

Figure 4: SD-OCT B-scan (Prof. J.A. Yzatt’s research prototype system).

A): Through the fovea: the edges of the CNV (red), cystoid macular edema (yellow), diffuse macular edema (green), and subretinal fluid (blue) are highlighted on each scan.

B): SVP (summed voxel projection) on OCT and color coding delineate the various lesions on an en face image (Nolan 200715).

The short acquisition time also allows imaging of the entire macular region and non-interpolated thickness and volumetric analysis based on images acquired simultane-

2ously and co-registered with the fundus image (Figures 5, 7, and 8), thereby allowing measurement of retinal thickness at any point on the macula.

This allows a reduction of “erroneous” foveal thickness measurements due to decentration of macular images on TD-OCT.

With SD-OCT, the center of the fovea can be identified manually or automatically. Images in 3D can be obtained to analyze drusen, pigment epithelial detachments (PEDs), and choroidal neovascular complexes.

2. Retinal Layer Segmentation

Time-domain OCT can be used to measure the thickness of the nerve fiber layer or the entire retina from the vitreoretinal interface to the RPE. However, changes external to the RPE, such as PEDs or CNV, are not measured with conventional software.

OCT Reading Centers for clinical studies of AMD use custom programs or manual measurement, as conventional TD-OCT is unable to isolate a specific layer of the posterior segment other than the nerve fiber layer (Zhang 200724).

Consequently, the volume of a PED or the subretinal space cannot be calculated in the context of long-term follow-up.

SD-OCT facilitates image segmentation by improved boundary identification: it is now possible to distinguish certain layers of the retina such as the plexiform layer subretinal space, and subretinal pigment epithelial space

(Figures 9 and 10).

These segmented layers can be used for further analysis, such as measuring the thickness and volume of individual retinal layers and monitoring these changes over time (Farsiu 200525, Haeker 200726).

Segmentation of retinal layers into a neurosensory component and a subretinal space component is also a promising new option to monitor the course of the disease.

3. Drusen

For decades, in vivo imaging of drusen was based on color fundus photography. Epidemiologic studies use this modality to evaluate the distinctness, area, and number of drusen.

This assessment is used to predict the risk of AMD progression, despite the limited agreement between observers (AREDS 200127)

Imaging of drusen remains limited on TD-OCT due to various artifacts.

With SD-OCT (or UHR-OCT), improved imaging of drusen constitutes a major progress (Pieroni 20067) While color photography may suggest a certain degree of phenotypic homogeneity, SD-OCT studies demonstrate the detailed structure of drusen (Figure 11).

In vivo evaluation of drusen composition is important in view of recent histopathologic and genetic findings demonstrating phenotypic variability related to the presence of various components such as amyloid

β (Anderson 200428), activated complement products (Anderson 200229), glycoproteins, and choroidal dendritic cell processes (Hageman 2001 30). However, the correlation between these findings and genetic markers and the risk of progression is unknown at the present time.

Segmentation techniques can precisely identify areas of RPE elevation (Figures 9A and 9B) and variability in drusen structure (Figures 2, 10, 11, 12, and 13), taking into account differences in shape, internal reflectivity, and homogeneity, which provide considerable precision to the analysis of drusen (Khanifar 200731 ).

Imaging of the internal structure of drusen is now possible and could be an indirect measurement of comple- ment-related activity (Figure 13). However, this work has not yet been validated by clinical studies.

These segmentation techniques can also be used to measure total drusen volume in the context of monitoring of disease progression and to assess the risk of progression (Figure 14). Computer-assisted analysis can be used to automate these studies.

4. Choroidal Neovascularization

Conventional TD-OCT imaging revolutionized the assess-

2ment of retinal response to CNV by visualizing the frequent appearances of macular edema and subretinal fluid

These images also established a correlation between visual acuity and the presence of cystoid macular edema, and between OCT measurements and response to therapy (Ting 2002 13 ).

TD-OCT can also demonstrate the presence of fibrovascular complexes in the subretinal and sub-RPE space.

SD-OCT appears to be more easily quantifiable and more reliable to monitor response to treatment (Figure 4).

With SD-OCT, it is easier to differentiate between type 1 and type 2 membranes and to create a three-dimensional reconstruction of the neovascular complex and associated retinal lesions (Schmidt-Erfurth 2005 32).

These relationships can be projected onto SVP images and correlated with fluorescein angiography or microperimetry for long-term follow-up (Figure 3).

Small foci of PED, CNV, or subretinal fluid are also more easily detected (Stopa 200722).

This is particularly useful for the diagnosis of chorioretinal anastomoses or retinal angiomatous proliferation.

SLO SD-OCT will also be particularly useful to generate 3D datasets incorporating both angiographic data and high-resolution SD-OCT imaging (Figure 15).

Co-registration of SD-OCT with the fundus image, fluorescein angiography, and indocyanine green angiography facilitates precise identification of the site of CNV and changes in CNV over time.

This new imaging modality and these volume studies will allow more precise clinical studies.

5. Geographic Atrophy

Clinical studies are currently based on color photographs or, more recently, autofluorescence (Holz 2007 33 , Bindewald 2005 34).

Areas of atrophy can sometimes be demonstrated: RPE atrophy induces increased choroidal hyper-reflectivity.

The improved transverse resolution of SD-OCT more clearly delineates the borders between normal and abnormal RPE (Figure 16) and associated lesions (drusen, pigment changes, and photoreceptors).

As OCT measures changes of tissue reflectivity rather than physiologic changes (eg, lipofuscin deposition visible on autofluorescence), it provides a different method for identifying these atrophic areas.

The optimal method would be to fuse the information derived from these two imaging modalities, as with the Heidelberg SLO-OCT, which captures autofluorescent imaging integrated with 3D SD-OCT volume.

Once again, segmentation techniques allow correlation of RPE changes with changes of the overlying photoreceptor layer (Figure 16).

Comparative Analysis

Integrating data from different imaging sources is often very challenging, but certainly very useful for diagnostic and prognostic purposes, especially to identify the location relative to a specific retinal landmark, of an abnormality found on a 3D OCT image set.

This is made possible by co-registration of OCT and fundus images and the eye-tracking system (Figures 4, 5, and 10) (Srinivasan 2006 5).

Clinicians are currently able to define pathology on anteroposterior OCT scans based on previous clinicopathologic correlation studies (Toth 1997 10,11 ).

However, this information is not sufficiently precise to define the margins of the lesion, and SD-OCT data are not integrated with conventional fundus imaging.

SVP images can be used to orient SD-OCT data in relation to a fundus image. It would also be very useful to combine en face images with OCT images.

Most communication still uses printed documents and therefore translation of 3D images into smaller 2D images that are easier to use in clinical practice than video sequences.