Background Estimation and Correction for High-Precision Localization Microscopy
Background Estimation and Correction for High-Precision Localization Microscopy
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DOI：
10.1021/acsphotonics.7b00238
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发表时间：
2017-07-01
期刊：
影响因子：
7
通讯作者：
Hsieh, Chia-Lung
中科院分区：
文献类型：
--
作者：
Cheng, Ching-Ya;Hsieh, Chia-Lung
单个纳米光发射体的局部化在生物成像中有着重要的应用。噪声和叠加在信号上的非均匀背景限制了定位的准确性和精度。虽然噪声的影响已被很好地认识到，但背景的影响较少被讨论。适当的背景校正不仅可以提供更准确的定位数据，还可以提高检测的灵敏度。在这里，我们展示了一种新的背景校正方法，通过从一系列包含空间运动信号的图像中估计和去除不均匀但稳定的背景。我们的方法利用了由测量系统的点扩散函数控制的相邻像素中编码的相关信号信息。这种新的方法使得即使在整个观测过程中信号的总位移是次衍射限制的情况下也可以获得背景，在这种情况下，以前的方法变得无效。在数值模拟中，我们用不同类型的信号在不同的信噪比下系统地描述了我们的方法。然后，我们通过恢复以指定模式移动的单个金纳米颗粒和在细胞表面随机扩散的单个病毒颗粒的纳米位移来实验验证我们的方法。文中还提供了用MatLab编写的算法源代码和一个样本数据集。我们的方法在高精度光学定位测量中立即得到了应用。
Localization of a single nanosized light emitter has substantial applications in bioimaging. The accuracy and precision of localization are limited by the noise and the heterogeneous background superimposed on the signal. While the effects of noise are well recognized, the influence of background is less addressed. Proper background correction not only provides more accurate localization data but also enhances the sensitivity of detection. Here, we demonstrate a new approach to background correction by estimating and removing the heterogeneous but stationary background from a series of images containing a spatially moving signal. Our approach exploits the correlated signal information encoded in the neighboring pixels governed by the point-spread function of the measurement system. This new approach makes it possible to obtain the background even when the total displacement of the signal is subdiffraction limited throughout the observation, the scenario where previous methods become invalid. We characterize our approach systematically with different types of signal motions at various signal-to-noise ratios in numerical simulations. We then verify our method experimentally by recovering the nanoscopic displacements of single gold nanoparticle moving in a specified pattern and a single virus particle randomly diffusing on a cell surface. The source code of our algorithm written in MATLAB is provided together with a sample data set. Our approach has immediate applications in high-precision optical localization measurements.