SPRING: an effective and reliable framework for image reconstruction in single-particle Coherent Diffraction Imaging

TL;DR

SPRING is a robust, open-source framework that enhances phase retrieval in single-particle CDI using Memetic algorithms, demonstrating superior stability and effectiveness on experimental XFEL data.

Contribution

The paper introduces SPRING, a novel analysis framework employing Memetic Phase Retrieval for improved image reconstruction in single-particle CDI at XFELs.

Findings

Demonstrates stability and resilience of SPRING on experimental data

Achieves successful reconstruction in challenging conditions

Provides open-source software for the community

Abstract

Coherent Diffraction Imaging (CDI) is an experimental technique to gain images of isolated structures by recording the light scattered off the sample. In principle, the sample density can be recovered from the scattered light field through a straightforward Fourier Transform operation. However, only the amplitude of the field is recorded, while the phase is lost during the measurement process and has to be retrieved by means of suitable, well-established phase retrieval algorithms. In this work, we present SPRING, an analysis framework tailored to X-ray Free Electron Laser (XFEL) single-shot single-particle diffraction data that implements the Memetic Phase Retrieval method to mitigate the shortcomings of conventional algorithms. We benchmark the approach on experimental data acquired in two experimental campaigns at SwissFEL and European XFEL. Imaging results on isolated nanostructures reveal unprecedented stability and resilience of the algorithm's behavior on the input parameters, as well as the capability of identifying the solution in conditions hardly treatable so far with conventional methods. A user-friendly implementation of SPRING is released as open-source software, aiming at being a reference tool for the coherent diffraction imaging community at XFEL and synchrotron facilities.