Use of the retinal vascular histology to validate an optical coherence tomography angiography technique

Paula K. Yu, Andrew Mehnert, Arman Athwal, Marinko V. Sarunic, Dao Yi Yu

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Purpose: To determine the fidelity of optical coherence tomography angiography (OCTA) techniques by direct comparison of the retinal capillary network images obtained from the same region as imaged by OCTA and high-resolution confocal micro-scope. Method: Ten porcine eyes were perfused with red blood cells for OCTA image acqui-sition from the area centralis and then perfusion-fixed, and the vessels were labeled for confocal imaging. Two approaches involving post-processing of two-dimensional projection images and vessel tracking on three dimensional image stacks were used to obtain quantitative measurements. Data collected include vessel density, length of visible vessel track, count of visible branch points, vessel track depth, vessel diameter, angle of vessel descent, and angle of dive for comparison and analysis. Results: Comparing vascular images acquired from OCTA and confocal microscopy, we found (1) a good representation of the larger caliber retinal vessels, (2) an underrepre-sentation of retinal microvessels smaller than 10 μm and branch points in all four retinal vascular plexuses, particularly the intermediate capillary plexus, (3) reduced visibility associated with an increase in the angle of descent, (4) a tendency to loss visibility of vessel track at a branch point or during a sharp dive, and (5) a reduction in visibility with increase in retinal depth on OCTA images. Conclusions: Current OCTA techniques can visualize the retinal capillary network, but some types of capillaries cannot be detected by OCTA, particularly in the middle to deeper layers. Translational Relevance: The information indicates the limitation in clinical use and scopes for improvement in the current OCTA technologies.

Original languageEnglish
Article number29
Pages (from-to)1-19
Number of pages19
JournalTranslational Vision Science and Technology
Volume10
Issue number1
DOIs
Publication statusPublished - 2021

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