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Toco Yuen Ping Chui, Davis B Zhou, Maria Maria Castanos, Jorge Santiago Andrade Romo, Rachel E Linderman, Joseph Carroll, Richard B Rosen; Identifying Macular and Peripapillary Perfusion Density Change in Eyes with Retinopathies Using OCT-Angiography. Invest. Ophthalmol. Vis. Sci. 2019;60(9):3014. doi: https://doi.org/.
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Identifying perfusion density change over time could be challenging given its variability within the same individual. In this study, we describe and evaluate a reference-based perfusion density change mapping approach for assessing non-perfusion and re-perfusion in a variety of retinopathies using OCT-A.
20 controls and 10 patients with various retinopathies (6 diabetic retinopathy, 2 sickle cell retinopathy, and 2 glaucoma) were imaged using a commercial SDOCT system (Avanti RTVue-XR; Optovue). Two consecutive 3x3 mm macular or 4.5x4.5mm peripapillary OCT-A scans were obtained for controls. Patients with retinopathies were imaged twice over a range of 2 weeks to 2 years. Image registration was performed for each pair of OCT-A images using ImageJ. Macula and peripapillary OCT-A image processing and perfusion density computation were performed using MATLAB as previously described (Andrade Romo ARVO E-Abstract 4944; Pinhas PMID: 29795576) (Fig. A-D). Within subject variability of perfusion densities were computed between scans in all controls. Identification of perfusion density area change over time was performed in patients (Fig. E). Non-perfusion and re-perfusion in patients were defined as difference in perfusion density between scans with lower than 0.1% or higher than 99.9% of the normal distribution based on the within-subject variability in controls, respectively.
Mean±SD of perfusion density area change in controls were 0.07±2.85% and -0.22±3.23% for macula and peripapillary OCT-A, respectively. Mean±SD of non-perfusion area over time was highest in glaucoma (10.05±5.91%), followed by sickle cell retinopathy (3.89±3.23%) and diabetic retinopathy (0.79±0.52%). Mean±SD of re-perfusion area over time was highest in sickle cell retinopathy (1.73±1.76%), followed by diabetic retinopathy (0.85±0.48%) and glaucoma (0.13±0.00%).
We have demonstrated a reference-based mapping of non-perfusion and re-perfusion which allows qualitative and quantitative analysis of perfusion density change over time on OCT-A scans. The utility of this mapping approach for evaluating retinopathies over time will require further exploration of their potential application for disease detection and monitoring progression.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.
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