High performance OCTA enabled by combining features of shape, intensity, and complex decorrelation.

Motion contrast optical coherence tomography angiography (OCTA) entails a precise identification of dynamic flow signals from the static background, but an intermediate region with voxels exhibiting a mixed distribution of dynamic and static scatterers is almost inevitable in practice, which degrades the vascular contrast and connectivity. In this work, the static-dynamic intermediate region was pre-defined according to the asymptotic relation between inverse signal-to-noise ratio (iSNR) and decorrelation, which was theoretically derived for signals with different flow rates based on a multi-variate time series (MVTS) model. Then the ambiguous voxels in the intermediate region were further differentiated using a shape mask with adaptive threshold. Finally, an improved OCTA classifier was built by combining shape, iSNR, and decorrelation features, termed as SID-OCTA, and the performance of the proposed SID-OCTA was validated experimentally through mouse retinal imaging.