Affiliations: [a] MATEIS, INSA Lyon, CNRS (UMR5510), Univ. Lyon, F-69621 Lyon, France | [b] ESRF, The European Synchrotron, F-38043 Grenoble, France. nicola.vigano@esrf.fr | [c] CWI, Amsterdam, 1098 XG Amsterdam, The Netherlands | [d] University of Leiden, Mathematical Institute, 2300 RA Leiden, The Netherlands | [e] MATEIS, INSA Lyon, CNRS (UMR5510), Univ. Lyon, F-69621 Lyon, France | [f] ESRF, The European Synchrotron, F-38043 Grenoble, France
Correspondence:
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Also works: University of Antwerp, iMinds-Vision Lab, B-2610 Antwerp, Belgium. Address for correspondence: MATEIS, INSA Lyon, CNRS (UMR5510), Université de Lyon, F-69621 Lyon, France.
Abstract: Diffraction contrast tomography (DCT) is an X-ray full-field imaging technique that allows for the non-destructive three-dimensional investigation of polycrystalline materials and the determination of the physical and morphological properties of their crystallographic domains, called grains. This task is considered more and more challenging with the increasing intra-granular deformation, also known as orientation-spread. The recent introduction of a six-dimensional reconstruction framework in DCT (6D-DCT) has proven to be able to address the intra-granular crystal orientation for moderately deformed materials. The approach used in 6D-DCT, which is an extended sampling of the six-dimensional combined position-orientation space, has a linear scaling between the number of sampled orientations, which determine the orientation-space resolution of the problem, and computer memory usage. As a result, the reconstruction of more deformed materials is limited by their high resource requirements from a memory and computational point of view, which can easily become too demanding for the currently available computer technologies. In this article we propose a super-sampling method for the orientation-space representation of the six-dimensional DCT framework that enables the reconstruction of more deformed cases by reducing the impact on system memory, at the expense of longer reconstruction times. The use of super-sampling can further improve the quality and accuracy of the reconstructions, especially in cases where memory restrictions force us to adapt to inadequate (undersampled) orientation-space sampling.
