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Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm

The use of spatial light modulators to project computer generated holograms is a common strategy for optogenetic stimulation of multiple structures of interest within a three-dimensional volume. A common requirement when addressing multiple targets sparsely distributed in three dimensions is the gen...

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Autores principales: Pozzi, Paolo, Maddalena, Laura, Ceffa, Nicolò, Soloviev, Oleg, Vdovin, Gleb, Carroll, Elizabeth, Verhaegen, Michel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481074/
https://www.ncbi.nlm.nih.gov/pubmed/31164587
http://dx.doi.org/10.3390/mps2010002
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author Pozzi, Paolo
Maddalena, Laura
Ceffa, Nicolò
Soloviev, Oleg
Vdovin, Gleb
Carroll, Elizabeth
Verhaegen, Michel
author_facet Pozzi, Paolo
Maddalena, Laura
Ceffa, Nicolò
Soloviev, Oleg
Vdovin, Gleb
Carroll, Elizabeth
Verhaegen, Michel
author_sort Pozzi, Paolo
collection PubMed
description The use of spatial light modulators to project computer generated holograms is a common strategy for optogenetic stimulation of multiple structures of interest within a three-dimensional volume. A common requirement when addressing multiple targets sparsely distributed in three dimensions is the generation of a points cloud, focusing excitation light in multiple diffraction-limited locations throughout the sample. Calculation of this type of holograms is most commonly performed with either the high-speed, low-performance random superposition algorithm, or the low-speed, high performance Gerchberg–Saxton algorithm. This paper presents a variation of the Gerchberg–Saxton algorithm that, by only performing iterations on a subset of the data, according to compressive sensing principles, is rendered significantly faster while maintaining high quality outputs. The algorithm is presented in high-efficiency and high-uniformity variants. All source code for the method implementation is available as Supplementary Materials and as open-source software. The method was tested computationally against existing algorithms, and the results were confirmed experimentally on a custom setup for in-vivo multiphoton optogenetics. The results clearly show that the proposed method can achieve computational speed performances close to the random superposition algorithm, while retaining the high performance of the Gerchberg–Saxton algorithm, with a minimal hologram quality loss.
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spelling pubmed-64810742019-05-31 Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm Pozzi, Paolo Maddalena, Laura Ceffa, Nicolò Soloviev, Oleg Vdovin, Gleb Carroll, Elizabeth Verhaegen, Michel Methods Protoc Article The use of spatial light modulators to project computer generated holograms is a common strategy for optogenetic stimulation of multiple structures of interest within a three-dimensional volume. A common requirement when addressing multiple targets sparsely distributed in three dimensions is the generation of a points cloud, focusing excitation light in multiple diffraction-limited locations throughout the sample. Calculation of this type of holograms is most commonly performed with either the high-speed, low-performance random superposition algorithm, or the low-speed, high performance Gerchberg–Saxton algorithm. This paper presents a variation of the Gerchberg–Saxton algorithm that, by only performing iterations on a subset of the data, according to compressive sensing principles, is rendered significantly faster while maintaining high quality outputs. The algorithm is presented in high-efficiency and high-uniformity variants. All source code for the method implementation is available as Supplementary Materials and as open-source software. The method was tested computationally against existing algorithms, and the results were confirmed experimentally on a custom setup for in-vivo multiphoton optogenetics. The results clearly show that the proposed method can achieve computational speed performances close to the random superposition algorithm, while retaining the high performance of the Gerchberg–Saxton algorithm, with a minimal hologram quality loss. MDPI 2018-12-20 /pmc/articles/PMC6481074/ /pubmed/31164587 http://dx.doi.org/10.3390/mps2010002 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pozzi, Paolo
Maddalena, Laura
Ceffa, Nicolò
Soloviev, Oleg
Vdovin, Gleb
Carroll, Elizabeth
Verhaegen, Michel
Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
title Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
title_full Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
title_fullStr Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
title_full_unstemmed Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
title_short Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg–Saxton Algorithm
title_sort fast calculation of computer generated holograms for 3d photostimulation through compressive-sensing gerchberg–saxton algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481074/
https://www.ncbi.nlm.nih.gov/pubmed/31164587
http://dx.doi.org/10.3390/mps2010002
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