Accelerating iterative deconvolution and multiview fusion by orders of magnitude
2019
We describe theoretical and practical advances in algorithm and software design, resulting in ten to several thousand-fold faster
deconvolutionand multiview fusion than previous methods. First, we adapt methods from medical imaging, showing that an unmatched back projector accelerates
Richardson-Lucy deconvolutionby at least 10-fold, in most cases requiring only a single iteration. Second, we show that improvements in 3D image-based registration with GPU processing result in speedups of 10-100-fold over CPU processing. Third, we show that deep learning can provide further accelerations, particularly for
deconvolutionwith a spatially varying
point spread function. We illustrate the power of our methods from the subcellular to millimeter spatial scale, on diverse samples including single cells, nematode and zebrafish embryos, and cleared mouse tissue. Finally, we show that our methods facilitate the use of new microscopes that improve spatial resolution, including dual-view cleared tissue light-sheet microscopy and reflective
lattice light-sheet microscopy.
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