title: Real-time GPU-based 3D Deconvolution.
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.
authors.affiliation: Stanford Immunology, Stanford University, Stanford, California 94305, USA.
authors.forename: Marc A
authors.initials: MA
authors.lastname: Bruce
authors.forename: Manish J
authors.initials: MJ
authors.lastname: Butte
mesh_terms: D003196:Computer Graphics; D003199:Computer Systems; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D018613:Microscopy, Confocal / Q000295:instrumentation / Q000379:methods*; D012815:Signal Processing, Computer-Assisted