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Real-time multi-view deconvolution

Summary: In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution simultaneously fuses and deconvolves the images in 3D, but processing takes a multiple...

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Detalles Bibliográficos
Autores principales: Schmid, Benjamin, Huisken, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595906/
https://www.ncbi.nlm.nih.gov/pubmed/26112291
http://dx.doi.org/10.1093/bioinformatics/btv387
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author Schmid, Benjamin
Huisken, Jan
author_facet Schmid, Benjamin
Huisken, Jan
author_sort Schmid, Benjamin
collection PubMed
description Summary: In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution simultaneously fuses and deconvolves the images in 3D, but processing takes a multiple of the acquisition time and constitutes the bottleneck in the imaging pipeline. Here, we show that MV deconvolution in 3D can finally be achieved in real-time by processing cross-sectional planes individually on the massively parallel architecture of a graphics processing unit (GPU). Our approximation is valid in the typical case where the rotation axis lies in the imaging plane. Availability and implementation: Source code and binaries are available on github (https://github.com/bene51/), native code under the repository ‘gpu_deconvolution’, Java wrappers implementing Fiji plugins under ‘SPIM_Reconstruction_Cuda’. Contact: bschmid@mpi-cbg.de or huisken@mpi-cbg.de Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-45959062015-10-09 Real-time multi-view deconvolution Schmid, Benjamin Huisken, Jan Bioinformatics Applications Notes Summary: In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions. State-of-the-art multi-view (MV) deconvolution simultaneously fuses and deconvolves the images in 3D, but processing takes a multiple of the acquisition time and constitutes the bottleneck in the imaging pipeline. Here, we show that MV deconvolution in 3D can finally be achieved in real-time by processing cross-sectional planes individually on the massively parallel architecture of a graphics processing unit (GPU). Our approximation is valid in the typical case where the rotation axis lies in the imaging plane. Availability and implementation: Source code and binaries are available on github (https://github.com/bene51/), native code under the repository ‘gpu_deconvolution’, Java wrappers implementing Fiji plugins under ‘SPIM_Reconstruction_Cuda’. Contact: bschmid@mpi-cbg.de or huisken@mpi-cbg.de Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2015-10-15 2015-06-25 /pmc/articles/PMC4595906/ /pubmed/26112291 http://dx.doi.org/10.1093/bioinformatics/btv387 Text en © The Author 2015. Published by Oxford University Press. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Schmid, Benjamin
Huisken, Jan
Real-time multi-view deconvolution
title Real-time multi-view deconvolution
title_full Real-time multi-view deconvolution
title_fullStr Real-time multi-view deconvolution
title_full_unstemmed Real-time multi-view deconvolution
title_short Real-time multi-view deconvolution
title_sort real-time multi-view deconvolution
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595906/
https://www.ncbi.nlm.nih.gov/pubmed/26112291
http://dx.doi.org/10.1093/bioinformatics/btv387
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