High performance OCT angiography enabled by combining features of shape, intensity and complex decorrelation
Posted on 12.01.2021 - 17:29
The motion contrast OCT angiography (OCTA) entails a precise identification of dynamic flow signals from the static background, but a transition region with mixed distribution of dynamic and static voxels is almost inevitable in practice, which degrades the vascular contrast and connectivity. In this work, the static-dynamic transition region was pre-defined according to the asymptotic relation between inverse SNR (iSNR) and decorrelation which was theoretically derived for signals with different flow rates based on a multi-variate time series (MVTS) model. And then the ambiguous voxels in the transition 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.
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Li, Huakun; Liu, Kaiyuan; Cao, TongTong; Yao, Lin; zhang, ziyi; Deng, Xiaofeng; et al. (2021): High performance OCT angiography enabled by combining features of shape, intensity and complex decorrelation. The Optical Society. Collection. https://doi.org/10.6084/m9.figshare.c.5256308.v2
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