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通过压缩感知格奇伯格-萨克斯顿算法快速计算用于3D光刺激的计算机生成全息图

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

作者信息

Pozzi Paolo, Maddalena Laura, Ceffa Nicolò, Soloviev Oleg, Vdovin Gleb, Carroll Elizabeth, Verhaegen Michel

机构信息

Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands.

Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands.

出版信息

Methods Protoc. 2018 Dec 20;2(1):2. doi: 10.3390/mps2010002.

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.

摘要

使用空间光调制器投射计算机生成的全息图是在三维空间中对多个感兴趣的结构进行光遗传学刺激的常用策略。在处理三维空间中稀疏分布的多个目标时,一个常见的要求是生成点云,将激发光聚焦在整个样本的多个衍射极限位置。这种类型的全息图计算最常用的方法是高速、低性能的随机叠加算法,或者低速、高性能的格尔奇贝格 - 萨克斯顿算法。本文提出了一种格尔奇贝格 - 萨克斯顿算法的变体,根据压缩感知原理,仅对数据的一个子集进行迭代,从而在保持高质量输出的同时显著提高了速度。该算法有高效和高均匀性两种变体。该方法实现的所有源代码作为补充材料和开源软件提供。该方法与现有算法进行了计算测试,并在定制的体内多光子光遗传学装置上进行了实验验证。结果清楚地表明,所提出的方法可以实现接近随机叠加算法的计算速度性能,同时保留格尔奇贝格 - 萨克斯顿算法的高性能,且全息图质量损失最小。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2927/6481074/45e4cc8251f8/mps-02-00002-g001.jpg

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