Solving Structure with Sparse, Randomly-Oriented X-ray Data

TL;DR

This paper introduces an expectation maximization algorithm capable of reconstructing biomolecular structures from extremely low-flux, randomly oriented X-ray diffraction images without prior knowledge, expanding the potential of high-noise measurement scenarios.

Contribution

The paper presents a novel algorithm that enables structure determination from sparse, randomly oriented X-ray data at unprecedented low photon counts, without prior object information.

Findings

Successfully processed images with only 2.5 photons per frame

Demonstrated feasibility of structure reconstruction without prior orientation knowledge

Expanded the limits of high-noise, low-signal X-ray imaging

Abstract

Single-particle imaging experiments of biomolecules at x-ray free-electron lasers (XFELs) require processing of hundreds of thousands (or more) of images that contain very few x-rays. Each low-flux image of the diffraction pattern is produced by a single, randomly oriented particle, such as a protein. We demonstrate the feasibility of collecting data at these extremes, averaging only 2.5 photons per frame, where it seems doubtful there could be information about the state of rotation, let alone the image contrast. This is accomplished with an expectation maximization algorithm that processes the low-flux data in aggregate, and without any prior knowledge of the object or its orientation. The versatility of the method promises, more generally, to redefine what measurement scenarios can provide useful signal in the high-noise regime.