Uncertainty quantification and estimation in differential dynamic microscopy
Uncertainty quantification and estimation in differential dynamic microscopy
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DOI：
10.1103/physreve.104.034610
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发表时间：
2021-09-24
影响因子：
2.4
通讯作者：
Valentine, Megan T.
中科院分区：
文献类型：
--
作者：
Gu, Mengyang;Luo, Yimin;Valentine, Megan T.
微分动态显微镜 (DDM) 是视频图像分析的一种形式，结合了散射的敏感性和显微镜的直接可视化优势。 DDM 在确定动力学特性方面广泛有用，包括许多时空相关系统的中间散射函数。尽管分析简单，但 DDM 尚未被完全采用作为常规表征工具，这主要是由于计算成本和算法鲁棒性的缺乏。我们提出的统计分析可以量化噪声、减少计算顺序并增强 DDM 分析的稳健性。我们通过傅里叶分析传播图像噪声，这使我们能够全面研究模型参数的不同估计量中的偏差，并得出一种不同的方法来检测偏差是否可以忽略不计。此外，通过使用高斯过程回归（GPR），我们发现图像结构函数的预测样本仅需要观测量的傅立叶变换的约0.5%-5%。这极大地降低了计算成本，同时保留了感兴趣的量的信息，例如图像散射函数的分位数，以供后续分析。与传统的 DDM 和多粒子跟踪相比，该方法被我们称为具有不确定性量化的 DDM (DDM-UQ)，通过模拟和实验在准确性和计算效率方面进行了验证。总的来说，我们认为 DDM-UQ 为 DDM 的重要新应用以及高通量表征奠定了基础。
Differential dynamic microscopy (DDM) is a form of video image analysis that combines the sensitivity of scattering and the direct visualization benefits of microscopy. DDM is broadly useful in determining dynamical properties including the intermediate scattering function for many spatiotemporally correlated systems. Despite its straightforward analysis, DDM has not been fully adopted as a routine characterization tool, largely due to computational cost and lack of algorithmic robustness. We present statistical analysis that quantifies the noise, reduces the computational order, and enhances the robustness of DDM analysis. We propagate the image noise through the Fourier analysis, which allows us to comprehensively study the bias in different estimators of model parameters, and we derive a different way to detect whether the bias is negligible. Furthermore, through use of Gaussian process regression (GPR), we find that predictive samples of the image structure function require only around 0.5%-5% of the Fourier transforms of the observed quantities. This vastly reduces computational cost, while preserving information of the quantities of interest, such as quantiles of the image scattering function, for subsequent analysis. The approach, which we call DDM with uncertainty quantification (DDM-UQ), is validated using both simulations and experiments with respect to accuracy and computational efficiency, as compared with conventional DDM and multiple particle tracking. Overall, we propose that DDM-UQ lays the foundation for important new applications of DDM, as well as to high-throughput characterization.