title: Real-Space x-ray tomographic reconstruction of randomly oriented objects with sparse data frames.
abstract: Schemes for X-ray imaging single protein molecules using new x-ray sources, like x-ray free electron lasers (XFELs), require processing many frames of data that are obtained by taking temporally short snapshots of identical molecules, each with a random and unknown orientation. Due to the small size of the molecules and short exposure times, average signal levels of much less than 1 photon/pixel/frame are expected, much too low to be processed using standard methods. One approach to process the data is to use statistical methods developed in the EMC algorithm (Loh & Elser, Phys. Rev. E, 2009) which processes the data set as a whole. In this paper we apply this method to a real-space tomographic reconstruction using sparse frames of data (below 10(-2) photons/pixel/frame) obtained by performing x-ray transmission measurements of a low-contrast, randomly-oriented object. This extends the work by Philipp et al. (Optics Express, 2012) to three dimensions and is one step closer to the single molecule reconstruction problem.
authors.forename: Kartik
authors.initials: K
authors.lastname: Ayyer
authors.forename: Hugh T
authors.initials: HT
authors.lastname: Philipp
authors.forename: Mark W
authors.initials: MW
authors.lastname: Tate
authors.forename: Veit
authors.initials: V
authors.lastname: Elser
authors.forename: Sol M
authors.initials: SM
authors.lastname: Gruner
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D021621:Imaging, Three-Dimensional / Q000379:methods*; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014961:X-Ray Diffraction / Q000379:methods*
chemical_list: D011506:Proteins