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Editors Selection IGR 17-4

RNFL Imaging: Thickness GCL, RNFL, IPL in preperimetric glaucoma

Comment by Tae-Woo Kim on:

48256 Macular ganglion cell layer imaging in preperimetric glaucoma with speckle noise-reduced spectral domain optical coherence tomography, Nakano N; Hangai M; Nakanishi H et al., Ophthalmology, 2011; 118: 2414-2426

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During the past years, examination of the macular inner retinal structure for diagnosing glaucoma has been the focus of research in many studies. Measuring the retinal ganglion cell complex, which includes the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL) and inner plexiform layer (IPL), has been demonstrated to detect glaucoma. However, morphologic changes of the macula caused by RGC loss in glaucoma are not necessarily expected to be identical in each layer. Yet, the damage of each layer has not been evaluated separately. This is probably related with the difficulty to visualize each layer individually. The current study by Nakano et al. (1868) demonstrated that macular GCL can be delineated using Speckle Noise reduced SD-OCT. Using this technology, they measured the thickness of each layer in eyes with pre-perimetric glaucoma and normal eyes. They found that both the GCL and RNFL were diminished in pre-perimetric glaucoma. An interesting result is that the GCL was substantially more diminished than RNFL. Moreover, the sensitivity of macular GCL on vertical B-scans exhibited higher than circumpapillary RNFL thickness measurement (81.1% vs. 54.1%) with comparable specificity. These findings open a new avenue for detecting early structural damage in glaucoma. The macular GCL may be a separate imaging target to clinically assess the ganglion cell loss, which is known to precede detectable visual field deficits.

The macular GCL may be a separate imaging target to clinically ssess the ganglion cell loss

As the current SD-OCT devices do not provide automated segmentation algorithm for the GCL, the authors delineated the GCL manually. The result of the current study encourages the development of an automated segmentation algorithm by the manufacturers.

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