Retinal blood flow speed quantification at the capillary level using temporal autocorrelation fitting OCTA
Yunchan Hwang, Jungeun Won, Antonio Yaghy, Hiroyuki Takahashi, Jessica, M. Girgis, Kenneth Lam, Siyu Chen, Eric M. Moult, Stefan B. Ploner, Andreas, Maier, Nadia K. Waheed, James G. Fujimoto

TL;DR
This paper introduces a novel OCTA method called VISTA that quantifies blood flow speed at the capillary level using temporal autocorrelation, enabling detailed analysis of retinal vasculature and disease states.
Contribution
The study presents a second-generation VISTA OCTA technique that measures blood flow speed via autocorrelation decay, improving spatial and temporal resolution for retinal capillaries.
Findings
VISTA accurately measures blood flow speed at the capillary level.
Different flow speeds are observed in various retinal capillary plexuses.
VISTA can differentiate healthy eyes from diabetic retinopathy cases.
Abstract
Optical coherence tomography angiography (OCTA) can visualize vasculature structures, but provides limited information about the blood flow speeds. Here, we present a second generation variable interscan time analysis (VISTA) OCTA, which evaluates a quantitative surrogate marker for blood flow speed in vasculature. At the capillary level, spatially compiled OCTA and a simple temporal autocorrelation model, {\rho}({\tau}) = exp(-{\alpha}{\tau}), were used to evaluate a temporal autocorrelation decay constant, {\alpha}, as the blood flow speed marker. A 600 kHz A-scan rate swept-source provides short interscan time OCTA and fine A-scan spacing acquisition, while maintaining multi mm2 field of views for human retinal imaging. We demonstrate the cardiac pulsatility and repeatability of {\alpha} measured with VISTA. We show different {\alpha} for different retinal capillary plexuses in…
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Taxonomy
TopicsOptical Coherence Tomography Applications · Retinal Imaging and Analysis · Photoacoustic and Ultrasonic Imaging
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
