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

Journal Article (2019)
Author(s)

P. Pozzi (TU Delft - Team Raf Van de Plas)

Laura Maddalena (TU Delft - ImPhys/Charged Particle Optics)

N.G. Ceffa (TU Delft - ImPhys/Charged Particle Optics)

O.A. Soloviev (Flexible Optical B.V., TU Delft - Team Raf Van de Plas)

G.V. Vdovine (Flexible Optical B.V., TU Delft - Team Raf Van de Plas)

Elizabeth C. Carroll (TU Delft - ImPhys/Charged Particle Optics)

MHG Verhaegen (TU Delft - Team Raf Van de Plas)

Copyright
© 2019 P. Pozzi, L. Maddalena, N.G. Ceffa, O.A. Soloviev, Gleb Vdovin, E.C.M. Carroll, M.H.G. Verhaegen
DOI related publication
https://doi.org/10.3390/mps2010002
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 P. Pozzi, L. Maddalena, N.G. Ceffa, O.A. Soloviev, Gleb Vdovin, E.C.M. Carroll, M.H.G. Verhaegen
Issue number
1
Volume number
2
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Abstract

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