Alignment of Cryo-Electron Tomography Images Using Markov Random Fields

Research Area: A.1-Electron Microscopy Year: 2010
Type of Publication: Phd Thesis Keywords: cryo-electron tomography, markov random fields, loopy belief propagation, subtomogram averaging
Publication: PhD Thesis
Cryo-Electron tomography (CET) is the only imaging technology capable of visualizing the 3D organization of intact bacterial whole cells at nanometer resolution in situ. However, quantitative image analysis of CET datasets is extremely challenging due to very low signal to noise ratio (well below 0dB), missing data and heterogeneity of biological structures. In this thesis, we present a probabilistic framework to align CET images in order to improve resolution and create structural models of different biological structures. The alignment problem of 2D and 3D CET images is cast as a Markov Random Field (MRF), where each node in the graph represents a landmark in the image. We connect pairs of nodes based on local spatial correlations and we find the “best” correspondence be- tween the two graphs. In this correspondence problem, the “best” solution maximizes the probability score in the MRF. This probability is the product of singleton potentials that measure image similarity between nodes and the pairwise potentials that measure de- formations between edges. Well-known approximate inference algorithms such as Loopy Belief Propagation (LBP) are used to obtain the “best” solution. We present results in two specific applications: automatic alignment of tilt series using fiducial markers and subtomogram alignment. In the first case we present RAPTOR, which is being used in several labs to enable real high-throughput tomography. In the second case our approach is able to reach the contrast transfer function limit in low SNR samples from whole cells as well as revealing atomic resolution details invisible to the naked eye through nanogold labeling.