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Real-Space x-ray tomographic reconstruction of randomly oriented objects with sparse data frames

Cornell Affiliated Author(s)

Author

K. Ayyer
H.T. Philipp
M.W. Tate
V. Elser
Sol Gruner

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. © 2014 Optical Society of America.

Date Published

Journal

Optics Express

Volume

22

Issue

3

Number of Pages

2403-2413,

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893977346&doi=10.1364%2fOE.22.002403&partnerID=40&md5=2f28d5d2f3277ae0ea159ba6e27cec3f

DOI

10.1364/OE.22.002403

Group (Lab)

Sol M. Gruner Group
Veit Elser Group

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