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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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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 |
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. |
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