Super-resolution microscopy depends on steps that can contribute to the formation of image artifacts, leading to misinterpretation of biological information. We present NanoJ-SQUIRREL, an ImageJ-based analytical approach that provides quantitative assessment of super-resolution image quality. By comparing diffraction-limited images and super-resolution equivalents of the same acquisition volume, this approach generates a quantitative map of super-resolution defects and can guide researchers in optimizing imaging parameters.
%0 Journal Article
%1 Culley2018
%A Culley, Siân
%A Albrecht, David
%A Jacobs, Caron
%A Pereira, Pedro Matos
%A Leterrier, Christophe
%A Mercer, Jason
%A Henriques, Ricardo
%D 2018
%I Nature Publishing Group
%J Nature Methods
%K Image microscopy processing software superresolutionartifacts team
%N 4
%P 263--266
%R 10.1038/nmeth.4605
%T Quantitative mapping and minimization of super-resolution optical imaging artifacts
%V 15
%X Super-resolution microscopy depends on steps that can contribute to the formation of image artifacts, leading to misinterpretation of biological information. We present NanoJ-SQUIRREL, an ImageJ-based analytical approach that provides quantitative assessment of super-resolution image quality. By comparing diffraction-limited images and super-resolution equivalents of the same acquisition volume, this approach generates a quantitative map of super-resolution defects and can guide researchers in optimizing imaging parameters.
@article{Culley2018,
abstract = {Super-resolution microscopy depends on steps that can contribute to the formation of image artifacts, leading to misinterpretation of biological information. We present NanoJ-SQUIRREL, an ImageJ-based analytical approach that provides quantitative assessment of super-resolution image quality. By comparing diffraction-limited images and super-resolution equivalents of the same acquisition volume, this approach generates a quantitative map of super-resolution defects and can guide researchers in optimizing imaging parameters.},
added-at = {2020-03-23T21:12:34.000+0100},
author = {Culley, Si{\^{a}}n and Albrecht, David and Jacobs, Caron and Pereira, Pedro Matos and Leterrier, Christophe and Mercer, Jason and Henriques, Ricardo},
biburl = {https://www.bibsonomy.org/bibtex/2bf34d9d4fa51608ba6803b8d625c5ccd/kfriedl},
doi = {10.1038/nmeth.4605},
file = {:C$\backslash$:/Users/Karoline/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Culley et al. - 2018 - Quantitative mapping and minimization of super-resolution optical imaging artifacts.pdf:pdf},
interhash = {3457dff05425f59cb46383ce90b4ceae},
intrahash = {bf34d9d4fa51608ba6803b8d625c5ccd},
issn = {15487105},
journal = {Nature Methods},
keywords = {Image microscopy processing software superresolutionartifacts team},
month = apr,
number = 4,
pages = {263--266},
publisher = {Nature Publishing Group},
timestamp = {2020-04-07T12:30:15.000+0200},
title = {{Quantitative mapping and minimization of super-resolution optical imaging artifacts}},
volume = 15,
year = 2018
}