Computational methods for automated center determination in electron diffraction patterns

Accurate center detection in electron diffraction patterns is critical for all subsequent processing of experimental diffractograms. This study presents and compares several automated approaches - maximum intensity detection, phase cross-correlation, autocorrelation-based detection, pseudo-Voigt profile fitting and Hough-transform-based detection - applied to both polycrystalline diffractograms with characteristic diffraction rings and single-crystal diffractograms showing discrete diffraction spots. The methods were evaluated in terms of accuracy, robustness, speed, preprocessing requirements and applicability across diverse materials that produce a variety of diffraction patterns. Our findings provide practical guidance for selecting center detection techniques in automated diffractogram processing workflows, thus facilitating improved data quality and reliability in crystallographic analyses. Phase cross-correlation has been proven to deliver high performance consistently on polycrystalline diffractograms with diffraction rings, while pseudo-Voigt profile fitting is best suited to monocrystal-like diffractograms with discrete diffraction spots. All the above-mentioned algorithms have been implemented in the recent version of our open-source Python package EDIFF, which now offers a user-friendly, flexible and fully automated solution for center detection in diffractograms. These algorithms determine the center of the individual two-dimensional diffraction patterns, while the processing of complete three-dimensional electron diffraction or four-dimensional scanning transmission electron microscopy datasets often includes accurate center determination as part of structure refinement workflows.