Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions

Simple item page
dc.citation.articleNumber30826en_US
dc.citation.journalTitleScientific Reportsen_US
dc.contributor.authorShuang, Boen_US
dc.contributor.authorWang, Wenxiaoen_US
dc.contributor.authorShen, Haoen_US
dc.contributor.authorTauzin, Lawrence J.en_US
dc.contributor.authorFlatebo, Charlotteen_US
dc.contributor.authorChen, Jianboen_US
dc.contributor.authorMoringo, Nicholas A.en_US
dc.contributor.authorBishop, Logan D.C.en_US
dc.contributor.authorKelly, Kevin F.en_US
dc.contributor.authorLandes, Christy F.en_US
dc.date.accessioned2016-09-30T20:52:22Zen_US
dc.date.available2016-09-30T20:52:22Zen_US
dc.date.issued2016en_US
dc.description.abstractSuper-resolution microscopy with phase masks is a promising technique for 3D imaging and tracking. Due to the complexity of the resultant point spread functions, generalized recovery algorithms are still missing. We introduce a 3D super-resolution recovery algorithm that works for a variety of phase masks generating 3D point spread functions. A fast deconvolution process generates initial guesses, which are further refined by least squares fitting. Overfitting is suppressed using a machine learning determined threshold. Preliminary results on experimental data show that our algorithm can be used to super-localize 3D adsorption events within a porous polymer film and is useful for evaluating potential phase masks. Finally, we demonstrate that parallel computation on graphics processing units can reduce the processing time required for 3D recovery. Simulations reveal that, through desktop parallelization, the ultimate limit of real-time processing is possible. Our program is the first open source recovery program for generalized 3D recovery using rotating point spread functions.en_US
dc.identifier.citationShuang, Bo, Wang, Wenxiao, Shen, Hao, et al.. "Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions." <i>Scientific Reports,</i> (2016) Springer Nature: http://dx.doi.org/10.1038/srep30826.en_US
dc.identifier.doihttp://dx.doi.org/10.1038/srep30826en_US
dc.identifier.urihttps://hdl.handle.net/1911/91633en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the maen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleGeneralized recovery algorithm for 3D super-resolution microscopy using rotating point spread functionsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
srep30826.pdf
Size:
1.26 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Faculty Publications
Chemistry Publications
ECE Publications