We developed an offline analytical technique to increase the utility of time-domain OCT. This can be used on existing OCT data sets, and could easily be modified for real-time analysis. One of the advantages of this analytical technique is that it is platform-independent and could be applied to any OCT data set (including SD-OCT), as long as one has access to retinal thickness data (or raw images from which thickness could be calculated or the difference between the ILM/RPE contours plotted). While SD-OCT technology has superior axial resolution and speed compared with time-domain systems,19
both modalities capture foveal morphology equally well.
Time-domain OCT devices have permeated the ophthalmology and vision research communities and there is tremendous interest in the development of post-processing methods to expand the utility of these devices. For example, averaging multiple Stratus line scans from the same retinal location can be used to increase the signal to noise ratio. The resultant image quality is significantly improved, and these images can reveal subtle alterations of intraretinal architecture where individual scans cannot.20
In addition, these high signal to noise time-domain images enable assessment of retinal lamination.21
Bernardes et al
(2008) modified existing scan protocols on the Stratus to improve the spatial resolution of retinal thickness maps by reducing the interpolation.22
These studies, together with ours, illustrate how the clinical utility of time-domain devices (Stratus) could be improved through offline image processing. There is no doubt that SD-OCT offers superior image quality and resolution. However, given the abundance of time-domain systems and the significant cost associated with upgrading to SD-OCT, for some clinics the implementation of similar image processing techniques may prove to be a valuable, if not necessary, intermediate step.
We observed remarkable variation in foveal morphology across clinically normal individuals. It is somewhat difficult to make direct comparisons between the values presented here and those from previous studies. One of the main reasons is that we relied on automated data acquisition based on objective definitions of the various morphological features. For example, the diameters presented here represent the upper bound of typical measurements, which are often based on other features of the fovea, such as the avascular zone or rod-free region. A recent study by Hammer et al
(2008) defined depth arbitrarily as the distance from base of the pit to the point where the radius is 0.728 µm.8
Making the same measurement on our data we found no difference between the data sets, supporting the idea that any differences between our data and those of others rests solely on the definition of the foveal metrics used. Peak cone density in normal subjects can vary by a factor of three,6
so future work will examine whether this variation is correlated with variation in gross foveal morphology, and how pit morphology compares with other anatomical landmarks, such as the rod-free region or the foveal avascular zone. Finally, normative data such as these should prove useful in quantitatively characterising clinically foveal hypoplasia/fovea plana.23