title: A Bayesian adaptive basis algorithm for single particle reconstruction.
abstract: Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. This paper proposes a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background.
authors.affiliation: Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA. alp.kucukelbir@yale.edu
authors.forename: Alp
authors.initials: A
authors.lastname: Kucukelbir
authors.forename: Fred J
authors.initials: FJ
authors.lastname: Sigworth
authors.forename: Hemant D
authors.initials: HD
authors.lastname: Tagare
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020285:Cryoelectron Microscopy / Q000379:methods; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D056004:Molecular Dynamics Simulation; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis