Optimized localization analysis for single-molecule tracking and super-resolution microscopy.
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TL;DR: Both theory and experimental data showed that unweighted least-squares fitting of a Gaussian squanders one-third of the available information, a popular formula for its precision exaggerates beyond Fisher's information limit, and weighted least-Squares may do worse, whereas maximum-likelihood fitting is practically optimal.
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Abstract: We optimally localized isolated fluorescent beads and molecules imaged as diffraction-limited spots, determined the orientation of molecules and present reliable formulas for the precision of various localization methods. Both theory and experimental data showed that unweighted least-squares fitting of a Gaussian squanders one-third of the available information, a popular formula for its precision exaggerates beyond Fisher's information limit, and weighted least-squares may do worse, whereas maximum-likelihood fitting is practically optimal.
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ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging
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Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging
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TL;DR: This analysis quantitatively characterized the switching properties of 26 organic dyes and directly related these properties to the quality of super-resolution images, providing guidelines for characterization ofsuper-resolution probes and a resource for selecting probes based on performance.
Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes
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TL;DR: MINFLUX as discussed by the authors is a concept for localizing photon emitters in space by probing the emitter with a local intensity minimum of excitation light, which minimizes the fluorescence photons needed for high localization precision.
Content-aware image restoration: pushing the limits of fluorescence microscopy.
Martin Weigert,Uwe Schmidt,Tobias Boothe,Andreas Müller,Alexandr Dibrov,Akanksha Jain,Benjamin Wilhelm,Deborah Schmidt,Coleman Broaddus,Siân Culley,Siân Culley,Mauricio Rocha-Martins,Fabián Segovia-Miranda,Caren Norden,Ricardo Henriques,Ricardo Henriques,Marino Zerial,Michele Solimena,Jochen C. Rink,Pavel Tomancak,Loic Royer,Florian Jug,Eugene W. Myers,Eugene W. Myers +23 more
TL;DR: This work shows how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy by bypassing the trade-offs between imaging speed, resolution, and maximal light exposure that limit fluorescence imaging to enable discovery.
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TL;DR: A localization algorithm motivated from least-squares fitting theory is constructed and tested both on image stacks of 30-nm fluorescent beads and on computer-generated images (Monte Carlo simulations), and results show good agreement with the derived precision equation.
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