COL0RME:基于稀疏闪烁/波动荧光团定位和强度估计的超分辨率显微镜

Biological imaging Pub Date : 2022-02-16 eCollection Date: 2022-01-01 DOI:10.1017/S2633903X22000010
Vasiliki Stergiopoulou, Luca Calatroni, Henrique de Morais Goulart, Sébastien Schaub, Laure Blanc-Féraud
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引用次数: 0

摘要

摘要为了克服光衍射引起的物理障碍,超分辨率技术经常应用于荧光显微镜。现有技术的方法需要特定且经常要求苛刻的采集条件,以实现足够的空间和时间分辨率。分析荧光分子的随机波动为上述限制提供了解决方案,因为使用普通显微镜和常规荧光染料可以实现足够高的活细胞成像时空分辨率。基于这一思想,我们提出了COL0RME,这是一种基于协方差的具有强度估计的${\mathrm{\ell}}_0$超分辨率显微镜方法,通过解决协方差域中的稀疏优化问题来实现良好的时空分辨率,并讨论了参数的自动选择策略。该方法由两个步骤组成:前者利用发射器的独立性和荧光分子的稀疏分布来提供准确的定位;后者在给定计算的支持的情况下估计真实强度值。本文提供了合成和真实荧光显微镜图像的几个数值结果,并与现有技术进行了一些比较。我们的结果表明,COL0RME优于利用类似时间波动的竞争方法;特别地,它实现了更好的定位,减少了背景伪影,并避免了参数微调。
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COL0RME: Super-resolution microscopy based on sparse blinking/fluctuating fluorophore localization and intensity estimation.

To overcome the physical barriers caused by light diffraction, super-resolution techniques are often applied in fluorescence microscopy. State-of-the-art approaches require specific and often demanding acquisition conditions to achieve adequate levels of both spatial and temporal resolution. Analyzing the stochastic fluctuations of the fluorescent molecules provides a solution to the aforementioned limitations, as sufficiently high spatio-temporal resolution for live-cell imaging can be achieved using common microscopes and conventional fluorescent dyes. Based on this idea, we present COL0RME, a method for covariance-based super-resolution microscopy with intensity estimation, which achieves good spatio-temporal resolution by solving a sparse optimization problem in the covariance domain and discuss automatic parameter selection strategies. The method is composed of two steps: the former where both the emitters' independence and the sparse distribution of the fluorescent molecules are exploited to provide an accurate localization; the latter where real intensity values are estimated given the computed support. The paper is furnished with several numerical results both on synthetic and real fluorescence microscopy images and several comparisons with state-of-the art approaches are provided. Our results show that COL0RME outperforms competing methods exploiting analogously temporal fluctuations; in particular, it achieves better localization, reduces background artifacts, and avoids fine parameter tuning.

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