Deterministic X-ray Bragg coherent diffraction imaging as a seed for subsequent iterative reconstruction

TL;DR

This paper introduces a deterministic approach to seed initial reconstructions in Bragg coherent diffraction imaging, improving convergence speed and accuracy of subsequent iterative refinements, especially in noisy data scenarios.

Contribution

It demonstrates that using a deterministic CDI reconstruction as a seed enhances the efficiency and convergence of iterative reconstruction methods compared to traditional crude estimates.

Findings

Deterministic CDI seeds improve convergence speed.

Enhanced reconstruction accuracy with deterministic seeds.

Monitoring multiple error metrics aids iterative refinement.

Abstract

Coherent diffractive imaging (CDI), using both X-rays and electrons, has made extremely rapid progress over the past two decades. The associated reconstruction algorithms are typically iterative, and seeded with a crude first estimate. A deterministic method for Bragg Coherent Diffraction Imaging (Pavlov et al., Sci. Rep. 7, 1132 (2017)) is used as a more refined starting point for a shrink-wrap iterative reconstruction procedure. The appropriate comparison with the autocorrelation function as a starting point is performed. Real-space and Fourier-space error metrics are used to analyse the convergence of the reconstruction procedure for noisy and noise-free simulated data. Our results suggest that the use of deterministic-CDI reconstructions, as a seed for subsequent iterative-CDI refinement, may boost the speed and degree of convergence compared to the cruder seeds that are currently commonly used. We also highlight the utility of monitoring multiple error metrics in the context of iterative refinement.