Abstract

Novel developments in X-ray sources, optics and detectors have significantly advanced the capability of X-ray microscopy at the nanoscale. Depending on the imaging modality and the photon energy, state-of-the-art X-ray microscopes are routinely operated at a spatial resolution of tens of nanometres for hard X-rays or ∼10 nm for soft X-rays. The improvement in spatial resolution, however, has led to challenges in the tomographic reconstruction due to the fact that the imperfections of the mechanical system become clearly detectable in the projection images. Without proper registration of the projection images, a severe point spread function will be introduced into the tomographic reconstructions, causing the reduction of the three-dimensional (3D) spatial resolution as well as the enhancement of image artifacts. Here the development of a method that iteratively performs registration of the experimentally measured projection images to those that are numerically calculated by reprojecting the 3D matrix in the corresponding viewing angles is shown. Multiple algorithms are implemented to conduct the registration, which corrects the translational and/or the rotational errors. A sequence that offers a superior performance is presented and discussed. Going beyond the visual assessment of the reconstruction results, the morphological quantification of a battery electrode particle that has gone through substantial cycling is investigated. The results show that the presented method has led to a better quality tomographic reconstruction, which, subsequently, promotes the fidelity in the quantification of the sample morphology.

Highlights

  • Ever since the discovery of X-rays in 1895 (Rontgen, 1895), imaging has been identified as a key area for X-ray applications

  • The resulting set of aligned projections is used as an input for the iteration, where the new calculated projection images are again compared with the originals for alignment

  • Taking the characteristics of these registration algorithms into consideration, we have proposed a sequence (CM5–PC15–SIFT10–IAIR20)

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Summary

Introduction

Ever since the discovery of X-rays in 1895 (Rontgen, 1895), imaging has been identified as a key area for X-ray applications. Thanks to all the novel developments in X-ray sources, optics, detectors and different imaging modalities (Liu et al, 2013), high-resolution X-ray microscopy has become very popular and successful. In a traditional tomography system, we often need to determine the amount of a static offset of the rotation axis with respect to the center column of the projection images (Donath et al, 2006) The method for such static offset correction can be as simple as trial-and-error (Gursoy et al, 2014) or analysis of an image-pair recorded in reverse viewing angles (Yang et al, 2015); it can be much more sophisticated involving novel machine-learning algorithms (Yang et al, 2017). The presented method has led to better quality in the tomographic reconstruction, which, subsequently, promotes the fidelity in the quantification of the sample morphology

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