title: Optimal Drift Correction for Superresolution Localization Microscopy with Bayesian Inference.
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.
authors.affiliation: Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, Connecticut.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Elmokadem
authors.affiliation: Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, Connecticut. Electronic address: jyu@uchc.edu.
authors.forename: Ji
authors.initials: J
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D045744:Cell Line, Tumor; D003198:Computer Simulation; D005347:Fibroblasts / Q000166:cytology / Q000378:metabolism; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008853:Microscopy / Q000379:methods*; D008958:Models, Molecular; D009010:Monte Carlo Method; D050737:Proto-Oncogene Proteins c-crk / Q000235:genetics / Q000378:metabolism
chemical_list: C485285:CRK protein, human; D050737:Proto-Oncogene Proteins c-crk