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Biophys J. 2015 Nov 3;109(9):1772-80. doi: 10.1016/j.bpj.2015.09.017.

Optimal Drift Correction for Superresolution Localization Microscopy with Bayesian Inference.

Author information

1
Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, Connecticut.
2
Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, Connecticut. Electronic address: jyu@uchc.edu.

Abstract

Single-molecule-localization-based superresolution microscopy requires accurate sample drift correction to achieve good results. Common approaches for drift compensation include using fiducial markers and direct drift estimation by image correlation. The former increases the experimental complexity and the latter estimates drift at a reduced temporal resolution. Here, we present, to our knowledge, a new approach for drift correction based on the Bayesian statistical framework. The technique has the advantage of being able to calculate the drifts for every image frame of the data set directly from the single-molecule coordinates. We present the theoretical foundation of the algorithm and an implementation that achieves significantly higher accuracy than image-correlation-based estimations.

PMID:
26536254
PMCID:
PMC4643251
DOI:
10.1016/j.bpj.2015.09.017
[Indexed for MEDLINE]
Free PMC Article

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