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Optimal model-based sensorless adaptive optics for epifluorescence microscopy
We report on a universal sample-independent sensorless adaptive optics method, based on modal optimization of the second moment of the fluorescence emission from a point-like excitation. Our method employs a sample-independent precalibration, performed only once for the particular system, to establi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860766/ https://www.ncbi.nlm.nih.gov/pubmed/29558510 http://dx.doi.org/10.1371/journal.pone.0194523 |
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author | Pozzi, Paolo Soloviev, Oleg Wilding, Dean Vdovin, Gleb Verhaegen, Michel |
author_facet | Pozzi, Paolo Soloviev, Oleg Wilding, Dean Vdovin, Gleb Verhaegen, Michel |
author_sort | Pozzi, Paolo |
collection | PubMed |
description | We report on a universal sample-independent sensorless adaptive optics method, based on modal optimization of the second moment of the fluorescence emission from a point-like excitation. Our method employs a sample-independent precalibration, performed only once for the particular system, to establish the direct relation between the image quality and the aberration. The method is potentially applicable to any form of microscopy with epifluorescence detection, including the practically important case of incoherent fluorescence emission from a three dimensional object, through minor hardware modifications. We have applied the technique successfully to a widefield epifluorescence microscope and to a multiaperture confocal microscope. |
format | Online Article Text |
id | pubmed-5860766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-58607662018-03-28 Optimal model-based sensorless adaptive optics for epifluorescence microscopy Pozzi, Paolo Soloviev, Oleg Wilding, Dean Vdovin, Gleb Verhaegen, Michel PLoS One Research Article We report on a universal sample-independent sensorless adaptive optics method, based on modal optimization of the second moment of the fluorescence emission from a point-like excitation. Our method employs a sample-independent precalibration, performed only once for the particular system, to establish the direct relation between the image quality and the aberration. The method is potentially applicable to any form of microscopy with epifluorescence detection, including the practically important case of incoherent fluorescence emission from a three dimensional object, through minor hardware modifications. We have applied the technique successfully to a widefield epifluorescence microscope and to a multiaperture confocal microscope. Public Library of Science 2018-03-20 /pmc/articles/PMC5860766/ /pubmed/29558510 http://dx.doi.org/10.1371/journal.pone.0194523 Text en © 2018 Pozzi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Pozzi, Paolo Soloviev, Oleg Wilding, Dean Vdovin, Gleb Verhaegen, Michel Optimal model-based sensorless adaptive optics for epifluorescence microscopy |
title | Optimal model-based sensorless adaptive optics for epifluorescence microscopy |
title_full | Optimal model-based sensorless adaptive optics for epifluorescence microscopy |
title_fullStr | Optimal model-based sensorless adaptive optics for epifluorescence microscopy |
title_full_unstemmed | Optimal model-based sensorless adaptive optics for epifluorescence microscopy |
title_short | Optimal model-based sensorless adaptive optics for epifluorescence microscopy |
title_sort | optimal model-based sensorless adaptive optics for epifluorescence microscopy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860766/ https://www.ncbi.nlm.nih.gov/pubmed/29558510 http://dx.doi.org/10.1371/journal.pone.0194523 |
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