SNR-adaptive OCT angiography enabled by statistical characterization of intensity and decorrelation with multi-variate time series model

TL;DR

This paper introduces a statistical model for OCT angiography that adaptively accounts for SNR variations, improving vascular visualization especially in low-SNR regions.

Contribution

It develops a multi-variate time series model to mathematically characterize decorrelation dependence on SNR and creates a SNR-adaptive OCTA method called ID-OCTA.

Findings

ID-OCTA improves vascular visibility in low-SNR regions.

The model accurately predicts decorrelation behavior across SNR levels.

Experimental validation confirms enhanced deep-layer vessel imaging.

Abstract

In OCT angiography (OCTA), decorrelation computation has been widely used as a local motion index to identify dynamic flow from static tissues, but its dependence on SNR severely degrades the vascular visibility, particularly in low- SNR regions. To mathematically characterize the decorrelation-SNR dependence of OCT signals, we developed a multi-variate time series (MVTS) model. Based on the model, we derived a universal asymptotic linear relation of decorrelation to inverse SNR (iSNR), with the variance in static and noise regions determined by the average kernel size. Accordingly, with the population distribution of static and noise voxels being explicitly calculated in the iSNR and decorrelation (ID) space, a linear classifier is developed by removing static and noise voxels at all SNR, to generate a SNR-adaptive OCTA, termed as ID-OCTA. Then, flow phantom and human skin experiments were performed to validate the proposed ID-OCTA. Both qualitative and quantitative assessments demonstrated that ID-OCTA offers a superior visibility of blood vessels, particularly in the deep layer. Finally, implications of this work on both system design and hemodynamic quantification are further discussed.