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.

中文翻译：

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通过结合形状、强度和复杂去相关的特征实现高性能 OCTA

运动对比光学相干断层扫描血管造影 (OCTA) 需要从静态背景中精确识别动态流信号，但在实践中几乎不可避免地会出现体素呈现动态和静态散射体混合分布的中间区域，这会降低血管对比度和连通性. 在这项工作中，根据逆信噪比（iSNR）和去相关之间的渐近关系预先定义了静态-动态中间区域，这是基于多变量时间从理论上推导出不同流速的信号系列 (MVTS) 模型。然后使用具有自适应阈值的形状掩码进一步区分中间区域中的模糊体素。最后，通过结合形状、iSNR 和去相关特征构建了改进的 OCTA 分类器，