Open AccessDrift correction in localization microscopy using entropy minimization
Author(s) -
Jelmer Cnossen,
Tao Cui,
Chirlmin Joo,
Carlas Smith
Publication year - 2021
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.426620
Subject(s) - fiducial marker , python (programming language) , computer science , algorithm , single shot , minification , microscopy , entropy (arrow of time) , optics , artificial intelligence , computer vision , physics , quantum mechanics , programming language , operating system
Localization microscopy offers resolutions down to a single nanometer but currently requires additional dedicated hardware or fiducial markers to reduce resolution loss from the drift of the sample. Drift estimation without fiducial markers is typically implemented using redundant cross correlation (RCC). We show that RCC has sub-optimal precision and bias, which leaves room for improvement. Here, we minimize a bound on the entropy of the obtained localizations to efficiently compute a precise drift estimate. Within practical compute-time constraints, simulations show a 5x improvement in drift estimation precision over the widely used RCC algorithm. The algorithm operates directly on fluorophore localizations and is tested on simulated and experimental datasets in 2D and 3D. An open source implementation is provided, implemented in Python and C++, and can utilize a GPU if available.