A reconstruction algorithm for single-particle diffraction imaging experiments

TL;DR

This paper presents the EMC algorithm for reconstructing 3D diffraction intensities from noisy 2D measurements with unknown orientations, enabling better analysis of single-particle imaging data.

Contribution

The paper introduces a novel EMC algorithm that efficiently reconstructs 3D diffraction intensities from noisy, orientation-unknown measurements, advancing single-particle imaging techniques.

Findings

Demonstrated successful reconstructions with simulated data

Analyzed effects of particle complexity and measurement parameters

Provided estimates for data processing requirements

Abstract

We introduce the EMC algorithm for reconstructing a particle's 3D diffraction intensity from very many photon shot-noise limited 2D measurements, when the particle orientation in each measurement is unknown. The algorithm combines a maximization step (M) of the intensity's likelihood function, with expansion (E) and compression (C) steps that map the 3D intensity model to a redundant tomographic representation and back again. After a few iterations of the EMC update rule, the reconstructed intensity is given to the difference-map algorithm for reconstruction of the particle contrast. We demonstrate reconstructions with simulated data and investigate the effects of particle complexity, number of measurements, and the number of photons per measurement. The relatively transparent scaling behavior of our algorithm provides a first estimate of the data processing resources required for future single-particle imaging experiments.