Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions
dc.citation.articleNumber | 30826 | |
dc.citation.journalTitle | Scientific Reports | |
dc.contributor.author | Shuang, Bo | |
dc.contributor.author | Wang, Wenxiao | |
dc.contributor.author | Shen, Hao | |
dc.contributor.author | Tauzin, Lawrence J. | |
dc.contributor.author | Flatebo, Charlotte | |
dc.contributor.author | Chen, Jianbo | |
dc.contributor.author | Moringo, Nicholas A. | |
dc.contributor.author | Bishop, Logan D.C. | |
dc.contributor.author | Kelly, Kevin F. | |
dc.contributor.author | Landes, Christy F. | |
dc.date.accessioned | 2016-09-30T20:52:22Z | |
dc.date.available | 2016-09-30T20:52:22Z | |
dc.date.issued | 2016 | |
dc.description.abstract | Super-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. | |
dc.identifier.citation | Shuang, 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. | |
dc.identifier.doi | http://dx.doi.org/10.1038/srep30826 | |
dc.identifier.uri | https://hdl.handle.net/1911/91633 | |
dc.language.iso | eng | |
dc.publisher | Springer Nature | |
dc.rights | This 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 ma | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Generalized recovery algorithm for 3D super-resolution microscopy using rotating point spread functions | |
dc.type | Journal article | |
dc.type.dcmi | Text | |
dc.type.publication | publisher version |
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