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An adaptive Expectation-Maximization algorithm with GPU implementation for electron cryomicroscopy

Hemant D. Tagare, Andrew Barthel, Fred J. Sigworth
Department of Diagnostic Radiology, Yale University, New Haven CT 06520, United States
Journal of Structural Biology, Volume 171, Issue 3, September 2010, Pages 256-265 (09 June 2010)

@article{tagare2010adaptive,

   title={An Adaptive Expectation-Maximization Algorithm with GPU Implementation for Electron Cryomicroscopy},

   author={Tagare, H.D. and Barthel, A. and Sigworth, F.J.},

   journal={Journal of structural biology},

   issn={1047-8477},

   year={2010},

   publisher={Elsevier}

}

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Maximum-likelihood (ML) estimation has very desirable properties for reconstructing 3D volumes from noisy cryo-EM images of single macromolecular particles. Current implementations of ML estimation make use of the Expectation-Maximization (EM) algorithm or its variants. However, the EM algorithm is notoriously computation-intensive, as it involves integrals over all orientations and positions for each particle image. We present a strategy to speedup the EM algorithm using domain reduction. Domain reduction uses a coarse grid to evaluate regions in the integration domain that contribute most to the integral. The integral is evaluated with a fine grid in these regions. In the simulations reported in this paper, domain reduction gives speedups which exceed a factor of 10 in early iterations and which exceed a factor of 60 in terminal iterations.
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