A stochastic kinematic model of class averaging in single-particle electron microscopy

  • Authors:

  • Wooram Park;Charles R Midgett;Dean R Madden;Gregory S Chirikjian

  • Affiliations:

  • Department of Mechanical Engineering, University ofTexas at Dallas, Richardson, TX, USA;Department of Biochemistry, Dartmouth Medical School,Hanover, NH, USA;Department of Biochemistry, Dartmouth Medical School,Hanover, NH, USA;Department of Mechanical Engineering, Johns HopkinsUniversity, Baltimore, MD, USA

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2011

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Abstract

Single-particle electron microscopy is an experimental technique that is used to determine the three-dimensional (3D) structure of biological macromolecules and the complexes that they form. In general, image processing techniques and reconstruction algorithms are applied to micrographs, which are 2D images taken by electron microscopes. Each of these planar images can be thought of as a projection of the macromolecular structure of interest from an a priori unknown direction. A class is defined as a collection of projection images with a high degree of similarity, presumably resulting from taking projections along similar directions. In practice, micrographs are very noisy and those in each class are aligned and averaged in order to reduce the background noise. Errors in the alignment process are inevitable due to noise in the electron micrographs. This error results in blurry averaged images. In this paper, we investigate how blurring parameters are related to the properties of the background noise in the case when the alignment is achieved by matching the mass centers and the principal axes of the experimental images. We observe that the background noise in micrographs can be treated as Gaussian. Using the mean and variance of the background Gaussian noise, we derive equations for the mean and variance of translational and rotational misalignments in the class averaging process. This defines a Gaussian probability density on the Euclidean motion group of the plane. Our formulation is validated by convolving the derived blurring function representing the stochasticity of the image alignments with the underlying noiseless projection and comparing with the original blurry image.