Simultaneous multiple-emitter fitting for single molecule super-resolution imaging

By Fang Huang, Samantha L. Schwartz, Jason M. Byars, K. Lidke. Biomedical Optics Express, 2011

303 citations recorded by the source corpus.

Single molecule localization based super-resolution imaging techniques require repeated localization of many single emitters. We describe a method that uses the maximum likelihood estimator to localize multiple emitters simultaneously within a single, two-dimensional fitting sub-region, yielding an order of magnitude improvement in the tolerance of the analysis routine with regards to the single-frame active emitter density. Multiple-emitter fitting enables the overall performance of single-molecule super-resolution to be improved in one or more of several metrics that result in higher single-frame density of localized active emitters. For speed, the algorithm is implemented on Graphics Processing Unit (GPU) architecture, resulting in analysis times on the order of minutes. We show the performance of multiple emitter fitting as a function of the single-frame active emitter density. We describe the details of the algorithm that allow robust fitting, the details of the GPU implementation, and the other imaging processing steps required for the analysis of data sets.

Subfields: Biomedical Engineering, Biophysics, Structural Biology

View Paper: https://www.semanticscholar.org/paper/f589c8887a049d8311b76d9aad08342671ace04d

How researchers see this paper

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

In Biomedical Engineering

In Biophysics

In Structural Biology

Citations in the corpus

Deep learning massively accelerates super-resolution localization microscopy (2018, background): “Increasing the number of active fluorophores per frame can reduce acquisition time, but despite algorithms designed to handle overlapping fluorescent spots 9–13 , this approach necessarily degrades spatial resolution 4,14 .”
Fluorophore localization algorithms for super-resolution microscopy (2014, methodology): “The most mathematically rigorous approach is, of course, MLE, and it has been implemented for multiple-fluorophore models by multiple groups 87 , including GPU implementations 49,88,89 .”
Deep-STORM: super-resolution single-molecule microscopy by deep learning (2018, methodology): “Existing classes of algorithms are based on sequential fitting of emitters, followed by subtraction of the model PSF [10–13]; blinking statistics [14–16]; sparsity [17–23]; multi-emitter maximum likelihood estimation [24]; or even single-image superresolution by dictionary learning [25, 26].”
Quantitative evaluation of software packages for single-molecule localization microscopy (2015, unknown): “Both gave similar results.”