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Real-time multi-view deconvolution.

ABSTRACT:

Unlabelled

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.

SUBMITTER: Schmid B

PROVIDER: S-EPMC4595906 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Real-time multi-view deconvolution.

Schmid Benjamin B Huisken Jan J

Bioinformatics (Oxford, England) 20150625 20

<h4>Unlabelled</h4>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 p ...[more]

PMID: 26112291

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