Expansion-maximization-compression algorithm with spherical harmonics for single particle imaging with X-ray lasers

Abstract

In 3D single particle imaging with X-ray free-electron lasers, particle orientation is not recorded during measurement but is instead recovered as a necessary step in the reconstruction of a 3D image from the diffraction data. Here we use harmonic analysis on the sphere to cleanly separate the angu- lar and radial degrees of freedom of this problem, providing new opportunities to efficiently use data and computational resources. We develop the Expansion-Maximization-Compression algorithm into a shell-by-shell approach and implement an angular bandwidth limit that can be gradually raised during the reconstruction. We study the minimum number of patterns and minimum rotation sampling required for a desired angular and radial resolution. These extensions provide new av- enues to improve computational efficiency and speed of convergence, which are critically important considering the very large datasets expected from experiment.

References in corpus (2)

• Gas Dynamic Virtual Nozzle for Generation of Microscopic Droplet Streams
• Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

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