Schmid Benjamin, Huisken Jan
Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany.
Bioinformatics. 2015 Oct 15;31(20):3398-400. doi: 10.1093/bioinformatics/btv387. Epub 2015 Jun 25.
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.
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'.
bschmid@mpi-cbg.de or huisken@mpi-cbg.de
Supplementary data are available at Bioinformatics online.
在光片显微镜中,通过从不同方向获取并融合样本的多个视图,整体图像内容和分辨率得以提高。最新的多视图(MV)反卷积可在三维空间中同时融合和解卷积图像,但处理时间是采集时间的数倍,这构成了成像流程中的瓶颈。在此,我们表明,通过在图形处理单元(GPU)的大规模并行架构上单独处理横截面平面,最终可以实时实现三维MV反卷积。我们的近似方法在旋转轴位于成像平面的典型情况下是有效的。
源代码和二进制文件可在github(https://github.com/bene51/)上获取,原生代码在“gpu_deconvolution”仓库下,实现Fiji插件的Java包装器在“SPIM_Reconstruction_Cuda”下。
bschmid@mpi-cbg.de或huisken@mpi-cbg.de
补充数据可在《生物信息学》在线获取。