Purpose
To demonstrate that Slit-Lamp adapted Ultra-High Resolution OCT (SL-UHR-OCT) along with custom-built image processing methods is capable of imaging and quantitatively characterizing the human retinal neural tissue structure and vascular morphology.
Methods
SL-UHR-OCT images from two healthy volunteers were acquired using a newly developed SL-UHR-OCT with ~3 μm depth resolution (24,000 A-scans/second). Raster scans were performed to obtain both macular and optic-disk-centered image frames (6x6 mm). After correcting for eye motion and filtering images to reduce speckle noise, selected retinal structures were automatically segmented and maximum intensity projection (MIP) images were generated which further facilitated the segmentation of the local vasculature network. A local self-correlation analysis was implemented on the extracted vessel architecture to characterize the features and integrity of the retinal vasculature network.
Results
A clear visualization of the retinal neural tissue structure and vasculature morphology was observed (Figure 1). The foveal avascular zone was clearly visible in both subjects’ eyes. The vascular network was comprehensively visible for most of the retinal layers and segments. Vessels oriented in a radial pattern were observed for the RNFL while a random oriented pattern was observed for the GCL-ONL segment and RPE layer. Arterial tree and major blood vessels were characterized by positive autocorrelation (lower fractal dimension) and higher luminance in both subjects.
Conclusions
We have demonstrated the capability of a SL-UHR-OCT device to image retinal neural tissue structure as well as depth-resolved information of the vascular morphology, which might facilitate the investigation of the neurovascular relationship in the human retina without the need of aligning retinal images acquired with different ophthalmic imaging instruments or at different times.
Keywords: 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) •
549 image processing •
688 retina