Fast, single-molecule localization that achieves theoretically minimum uncertainty
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.
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.
Abstract
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.
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