An adaptive Expectation-Maximization algorithm with GPU implementation for electron cryomicroscopy.
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.
Department of Diagnostic Radiology, Yale University, New Haven, CT 06520, USA.
Hemant D
HD
Tagare
Andrew
A
Barthel
Fred J
FJ
Sigworth
D000465:Algorithms; D020285:Cryoelectron Microscopy / Q000379:methods*; D016013:Likelihood Functions