  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.
