Optimization based evaluation of grating interferometric phase stepping series and analysis of mechanical setup instabilities

TL;DR

This paper introduces a fast optimization method for analyzing phase stepping series in X-ray grating interferometry, effectively correcting positioning errors and detecting rotational instabilities to improve image quality and system calibration.

Contribution

A novel, efficient optimization algorithm that simultaneously determines phase stepping parameters and mechanical motions, including rotations, enhancing accuracy and calibration in grating interferometry.

Findings

Correction of translational errors reduces image artifacts.

Detection of minute rotations aids system calibration.

Algorithm is suitable for large image series evaluation.

Abstract

The diffraction contrast modalities accessible by X-ray grating interferometers are not imaged directly but have to be inferred from sine like signal variations occurring in a series of images acquired at varying relative positions of the interferometer's gratings. The absolute spatial translations involved in the acquisition of these phase stepping series usually lie in the range of only a few hundred nanometers, wherefore positioning errors as small as 10nm will already translate into signal uncertainties of one to ten percent in the final images if not accounted for. Classically, the relative grating positions in the phase stepping series are considered input parameters to the analysis and are, for the Fast Fourier Transform that is typically employed, required to be equidistantly distributed over multiples of the gratings' period. More general optimization based approaches with relaxed requirements on the sampling positions are often deemed too time consuming in contrast. In the following, a fast converging optimization scheme is presented simultaneously determining the phase stepping curves' parameters as well as the actually performed motions of the stepped grating, including also erroneous rotational motions which are commonly neglected. While the correction of solely the translational errors along the stepping direction is found to be sufficient with regard to the reduction of image artifacts, the possibility to also detect minute rotations about all axes proves to be a valuable tool for system calibration and monitoring. The simplicity of the provided algorithm, in particular when only considering translational errors, makes it well suitable as a standard evaluation procedure also for large image series.