Fast, single-molecule localization that achieves theoretically minimum uncertainty

By Carlas S. Smith, Nikolai Joseph, B. Rieger, K. Lidke. Nature Methods, 2010

534 citations recorded by the source corpus.

We describe an iterative algorithm that converges to the maximum likelihood estimate of the position and intensity of a single fluorophore. Our technique efficiently computes and achieves the Cramér-Rao lower bound, an essential tool for parameter estimation. An implementation of the algorithm on graphics processing unit hardware achieved more than 105 combined fits and Cramér-Rao lower bound calculations per second, enabling real-time data analysis for super-resolution imaging and other applications.

Subfields: Biophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/acb0ad24483d149368f63673e9cf0c7dcdead333

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Alex Small
2. Shane Stahlheber
3. Suliana Manley
4. M. Bates
5. Michael A. Unser
6. Stefan W. Hell
7. X. Zhuang

In Instrumentation

1. Alex Small
2. Shane Stahlheber

Citations in the corpus

• Quantitative evaluation of software packages for single-molecule localization microscopy (2015, background): “…with some ground-truth molecule are categorized as true positives, TP; the remaining localized molecules are farther than ρ , unpaired, and categorized as false positives, FP; finally, ground-truth molecules that are not associated with any localized molecule are categorized as false negatives, FN.”
• Image Resolution in Optical Nanoscopy (2013, methodology): “We have performed numerical tests using fully vectorial modeling of the PSF of a freely rotating dipole emitter with zero aberrations and zero background, and 500 signal photons [49], and the MLE localization routine implemented on GPU for speed [24].”
• Nuclear pores as versatile reference standards for quantitative superresolution microscopy (2019, unknown): “Astigmatic 3D data were fitted with an experimentally derived PSF model (free fitting parameters: x, y, z, photons per localization, background per pixel), also using an MLE fitter 61 .”
• Fluorophore localization algorithms for super-resolution microscopy (2014, methodology): “The inverse of this quantity is the expected variance of the parameter (position) estimates, and it has the name ‘Cramér-Rao lower bound’ (CRLB) after Cramér and Rao, who independently derived a fundamental limit to statistical estimation in the 1940s 57,58 .”