title: Fast maximum-likelihood refinement of electron microscopy images.
abstract: BACKGROUND
  Maximum-likelihood (ML) image refinement is a promising candidate to improve attainable resolution limits in 3D-EM. However, its large CPU requirements may prohibit application to 3D-structure optimization.
  
  
  RESULTS
  We speeded up ML image refinement by reducing its search space over the alignment parameters. Application of this reduced-search approach to a cryo-EM dataset yielded practically identical results as the original approach, but in approximately one day instead of one week of CPU.
  
  
  BACKGROUND
  This work has been implemented in the public domain package Xmipp. Documentation and download instructions may be found at: http://www.cnb.uam.es/~bioinfo
authors.affiliation: Centro Nacional de Biotecnología-CSIC, Campus Universidad Autónoma, Madrid, Spain.
authors.forename: Sjors H W
authors.initials: SH
authors.lastname: Scheres
authors.forename: Mikel
authors.initials: M
authors.lastname: Valle
authors.forename: José-María
authors.initials: JM
authors.lastname: Carazo
mesh_terms: D000465:Algorithms; D020285:Cryoelectron Microscopy / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016013:Likelihood Functions; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity