Le Nhan, Song Shaozhen, Zhang Qinqin, Wang Ruikang K
Department of Bioengineering, University of Washington, Seattle, WA, USA.
Quant Imaging Med Surg. 2017 Dec;7(6):654-667. doi: 10.21037/qims.2017.12.05.
Recent development of optical micro-angiography (OMAG) utilizes principal component analysis (PCA), where linear-regression filter is employed to separate static and blood flow signals within optical coherence tomography (OCT). While PCA is relatively simple and computationally efficient, the technique is sensitive to and easily skewed by outliers. In this paper, robust PCA (RPCA) is thus introduced to tackle this issue in traditional PCA.
We first provide brief theoretical background of PCA and RPCA in the context of OMAG where coherent (complex) OCT signals are utilized to contrast blood flow. We then compare PCA and RPCA on sets of 4D-OCT complex data (3 dimensions in space and 1 dimension in time), which are collected from microfluidic phantoms and nail-fold tissue.
In phantom experiments, both analyses perform relatively well since there are little motion within our observation time window, albeit small tail-noise artifacts from PCA. In nail-fold experiment, PCA suffers from tissue motion, from which RPCA does not seem to be affected. Results from RPCA also show enhancements of other dynamic signals, which are likely from the intercellular fluid. This unwanted result is yet to be proven useful for clinical applications.
Traditional PCA method employs linear-regression filter and is sensitive to outliers (tail-noise and motion artifacts). RPCA method is robust against outliers, but is currently computationally expensive.
光学微血管造影术（OMAG）的最新进展利用主成分分析（PCA），其中采用线性回归滤波器在光学相干断层扫描（OCT）内分离静态和血流信号。虽然PCA相对简单且计算效率高，但该技术对异常值敏感且容易被其扭曲。因此，本文引入鲁棒主成分分析（RPCA）来解决传统PCA中的这一问题。
我们首先在OMAG的背景下提供PCA和RPCA的简要理论背景，其中利用相干（复数）OCT信号来对比血流。然后，我们在从微流控模型和甲襞组织收集的4D - OCT复数数据集（空间3维，时间1维）上比较PCA和RPCA。
在模型实验中，由于在我们的观察时间窗口内运动很少，两种分析都表现得相对较好，尽管PCA存在小的尾部噪声伪影。在甲襞实验中，PCA受到组织运动的影响，而RPCA似乎不受此影响。RPCA的结果还显示了其他动态信号的增强，这些信号可能来自细胞间液。这一意外结果在临床应用中的实用性还有待证实。
传统的PCA方法采用线性回归滤波器，对异常值（尾部噪声和运动伪影）敏感。RPCA方法对异常值具有鲁棒性，但目前计算成本较高。
