Optimizing 3D retinal vasculature imaging in diabetic retinopathy using registration and averaging of OCT-A

Arman Athwal, Chandrakumar Balaratnasingam, Dao Yi Yu, Morgan Heisler, Marinko V. Sarunic, Myeong Jin Ju

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

High resolution visualization of optical coherence tomography (OCT) and OCT angiography (OCT-A) data is required to fully take advantage of the imaging modality's three-dimensional nature. However, artifacts induced by patient motion often degrade OCT-A data quality. This is especially true for patients with deteriorated focal vision, such as those with diabetic retinopathy (DR). We propose a novel methodology for software-based OCT-A motion correction achieved through serial acquisition, volumetric registration, and averaging. Motion artifacts are removed via a multi-step 3D registration process, and visibility is significantly enhanced through volumetric averaging. We demonstrate that this method permits clear 3D visualization of retinal pathologies and their surrounding features, 3D visualization of inner retinal capillary connections, as well as reliable visualization of the choriocapillaris layer.

Original languageEnglish
Pages (from-to)553-570
Number of pages18
JournalBiomedical Optics Express
Volume12
Issue number1
DOIs
Publication statusPublished - 1 Jan 2021

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