Abstract
Confocal microscopy is an oft-used technique in biology. Deconvolution of 3D images reduces blurring from out-of-focus light and enables quantitative analyses, but existing software for deconvolution is slow and expensive. We present a parallelized software method that runs within ImageJ and deconvolves 3D images ~100 times faster than conventional software (few seconds per image) by running on a low-cost graphics processor board (GPU). We demonstrate the utility of this software by analyzing microclusters of T cell receptors in the immunological synapse of a CD4 + T cell and dendritic cell. This software provides a low-cost and rapid way to improve the accuracy of 3D microscopic images obtained by any method.
Highlights
Confocal microscopy has become an important and standard tool in biology because it enables detailed three-dimensional examination of cellular specimens
Confocal microscopy is an oft-used technique in biology
Deconvolution of 3D images reduces blurring from out-of-focus light and enables quantitative analyses, but existing software for deconvolution is slow and expensive
Summary
Confocal microscopy has become an important and standard tool in biology because it enables detailed three-dimensional examination of cellular specimens. The diffraction-limited nature of the optics reduces the ability to precisely localize photons, resulting in blurring, the major source of distortion. Blurring is modeled in software as a point spread function (PSF), essentially a mathematically defined cone of light that a single fluorophore makes. Deconvolution corrects blurring and reduces the effect of noise by applying an inverse of the PSF to each point of the measured image. We present a method to almost instantaneously deconvolve 3D images obtained by any microscopy technique, including confocal, wide-field, or two-photon, by utilizing a low-cost graphics processor unit (GPU), leveraging its multitude of computational cores and capability to accelerate fast Fourier transforms (FFTs). We implemented our software to run on NVIDIA GPUs that support CUDA (Compute Unified Device Architecture)
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