Fast, single-molecule localization that achieves theoretically minimum uncertainty

Department of Imaging Science and Technology, Delft University of Technology, The Netherlands.
Nature Methods (Impact Factor: 32.07). 04/2010; 7(5):373-5. DOI: 10.1038/nmeth.1449
Source: PubMed


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 10(5) 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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    • "Single molecule localization and trajectory connection were carried out using GPU computing as previously described (Smith et al., 2010). For details of SPT analysis, see Lowdisplacement analysis and two-component fitting, as previously described (de Keijzer et al., 2008; Low-Nam et al., 2011). "
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    • "In [24] and [25] the fundamental limit of localization accuracy was introduced as the Cramér-Rao lower bound (CRLB) for the location estimation problem, in the context of ideal experimental conditions such as an infinite size photon detector without pixilation artefacts and without other extraneous noise sources. This measure has proved a reliable predictor for the best possible accuracy that can be achieved with a specific single molecule experiment [26], [27]. Due to the importance of registration in single molecule experiments the question therefore arises how the uncertainty introduced during the registration process influences the localization accuracy for a single molecule that has been registered. "
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    • "Calculating the CRLB thus provides insight into the amount of information about the bead position stored in any given image. In fluorescence microscopy, the CRLB has been computed to determine the theoretical limit for the localization of point spread functions modeled via 2D Gaussian functions (Smith et al., 2010). We have similarly applied the CRLB to the case of spherical bead localization by replacing the 2D Gaussian with a measured LUT that defines the probability distribution for each pixel in the image. "
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