IEEE Trans Med Imaging. 2019 Nov;38(11):2695-2704. doi: 10.1109/TMI.2019.2910871. Epub 2019 Apr 12.
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 the ID-OCTA offers a superior visibility of blood vessels, particularly in the deep layer. Finally, the implications of this work on both system design and hemodynamic quantification are further discussed.
在 OCT 血管造影（OCTA）中，去相关计算被广泛用作局部运动指标，以识别来自静态组织的动态血流，但它对 SNR 的依赖性严重降低了血管的可视性，特别是在低 SNR 区域。为了从数学上描述 OCT 信号的去相关-SNR 依赖性，我们开发了一种多变量时间序列 (MVTS) 模型。基于该模型，我们推导出了去相关与逆信噪比 (iSNR) 的通用渐近线性关系，其中静态和噪声区域的方差由平均核大小决定。因此，通过在 iSNR 和去相关 (ID) 空间中明确计算静态和噪声体素的总体分布，开发了一种线性分类器，通过去除所有 SNR 处的静态和噪声体素，生成一种 SNR 自适应 OCTA，称为 ID-OCTA。然后，进行了流动体模和人体皮肤实验以验证所提出的 ID-OCTA。定性和定量评估都表明，ID-OCTA 提供了更好的血管可视性，特别是在深层。最后，进一步讨论了这项工作对系统设计和血流定量的影响。
