ArticleMethods and protocols2018
Fast Calculation of Computer Generated Holograms for 3D Photostimulation through Compressive-Sensing Gerchberg-Saxton Algorithm.
Article in Methods and protocols, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Active pixel power control for crosstalk-free all-optical neural interrogation.Nature communications · 2026Article
- An integrated photonic engine for programmable atomic control.Nature communications · 2025Article
- Ultrafast light targeting for high-throughput precise control of neuronal networks.Nature communications · 2023Article
- Advances in computer-generated holography for targeted neuronal modulation.Neurophotonics · 2022Review
- Laser-Material Interactions of High-Quality Ultrashort Pulsed Vector Vortex Beams.Micromachines · 2021Article
- Real Time Generation of Three Dimensional Patterns for Multiphoton Stimulation.Frontiers in cellular neuroscience · 2021Article
- Optical Micromachines for Biological Studies.Micromachines · 2020Review
- Holographic two-photon activation for synthetic optogenetics.Nature protocols · 2019Article
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Authors and funding
7 authors.
Funding
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.
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