Drift correction in localization microscopy using entropy minimization

Journal article (2021)

Authors

J.P. Cnossen Team Carlas Smith - Mechanical, Maritime and Materials Engineering
Tao Ju Cui Kavli institute of nanoscience Delft, Student
C. Joo Kavli institute of nanoscience Delft, BN/Chirlmin Joo Lab - AS
C.S. Smith Team Carlas Smith - Mechanical, Maritime and Materials Engineering
Research Group
Team Carlas Smith (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2021 J.P. Cnossen, Tao Ju Cui, C. Joo, C.S. Smith
DOI: https://doi.org/10.1364/OE.426620
More Info
expand_more

Published Date

2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Carlas Smith

Abstract

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.

Files

Oe_29_18_27961.pdf
(pdf | 5.69 Mb)
Unknown license