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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
Descripción
Sumario: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.