Comparison between SOFI and STORM.
Comparison between SOFI and STORM.
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DOI：
10.1364/boe.2.000408
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发表时间：
2011-01-28
影响因子：
3.4
通讯作者：
Lasser T
中科院分区：
文献类型：
--
作者：
Geissbuehler S;Dellagiacoma C;Lasser T
实现超分辨率的一种简单方法是使用标准的宽视场荧光显微镜拍摄随机闪烁发射器的图像序列。可以使用高精度定位算法（例如，PALM和STORM）或通过分析时间波动的统计（SOFI）。在两种后处理算法的面对面比较中，我们表明基于定位的超分辨率可以提供更高的分辨率增强，但对探针的闪烁行为施加了显着的限制，这限制了其对活细胞成像的适用性。另一方面，SOFI在不同的光开关动力学上更一致地工作，并且还提供关于特定眨眼统计的信息。其适用于低信噪比采集揭示了SOFI作为高速超分辨率成像技术的潜力。
A straightforward method to achieve super-resolution consists of taking an image sequence of stochastically blinking emitters using a standard wide-field fluorescence microscope. Densely packed single molecules can be distinguished sequentially in time using high-precision localization algorithms (e.g., PALM and STORM) or by analyzing the statistics of the temporal fluctuations (SOFI). In a face-to-face comparison of the two post-processing algorithms, we show that localization-based super-resolution can deliver higher resolution enhancements but imposes significant constraints on the blinking behavior of the probes, which limits its applicability for live-cell imaging. SOFI, on the other hand, works more consistently over different photo-switching kinetics and also delivers information about the specific blinking statistics. Its suitability for low SNR acquisition reveals SOFI's potential as a high-speed super-resolution imaging technique.