Opt Lett. 2021 Jan 15;46(2):368-371. doi: 10.1364/OL.405751.
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.
运动对比度光相干断层扫描血管造影术（OCTA）需要从静态背景中准确识别动态血流信号，但在实际中，几乎不可避免地会出现中间区域，其中体素表现出动态和静态散射体的混合分布，这会降低血管对比度和连通性。在这项工作中，根据反向信噪比（iSNR）与去相关之间的渐近关系，预先定义了静态-动态中间区域，该关系是基于多变量时间序列（MVTS）模型针对不同流速的信号从理论上推导出来的。然后，使用具有自适应阈值的形状掩模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，称为 SID-OCTA，并通过小鼠视网膜成像实验验证了所提出的 SID-OCTA 的性能。
