A Robust Statistical Estimation (RoSE) algorithm jointly recovers the 3D location and intensity of single molecules accurately and precisely

被引：0

作者：

Mazidi, Hesam ^{[1
]}

Nehorai, Arye ^{[1
]}

Lew, Matthew D. ^{[1
]}

机构：

[1] Washington Univ, Dept Elect & Syst Engn, 1 Brookings Dr, St Louis, MO 63130 USA

来源：

SINGLE MOLECULE SPECTROSCOPY AND SUPERRESOLUTION IMAGING XI | 2018年 / 10500卷

基金：

美国国家科学基金会; 美国国家卫生研究院;

关键词：

single-molecule localization microscopy; multi-dimensional reconstruction algorithm; joint sparsity; joint location and brightness estimation; 3D super-resolution fluorescence microscopy; LOCALIZATION ANALYSIS; RESOLUTION; SPECTROSCOPY; FLUOROPHORES; MICROSCOPY; TRACKING; FIELD;

D O I：

10.1117/12.2289994

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In single-molecule (SM) super-resolution microscopy, the complexity of a biological structure, high molecular density, and a low signal-to-background ratio (SBR) may lead to imaging artifacts without a robust localization algorithm. Moreover, engineered point spread functions (PSFs) for 3D imaging pose difficulties due to their intricate features. We develop a Robust Statistical Estimation algorithm, called RoSE, that enables joint estimation of the 3D location and photon counts of SMs accurately and precisely using various PSFs under conditions of high molecular density and low SBR.

引用

页数：11

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