An orientation-space super sampling technique for six-dimensional diffraction contrast tomography
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 sixdimensional 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.
|Project||Volumetric medical x-ray imaging at extremely low dose|
|Grant||This work was funded by the European Commission 7th Framework Programme; grant id h2020/665207 - volumetric medical x-ray imaging at extremely low dose (VOXEL)|
Viganò, N.R, Batenburg, K.J, & Ludwig, W. (2016). An orientation-space super sampling technique for six-dimensional diffraction contrast tomography. Fundamenta Informaticae, 146(2), 219–230. doi:10.3233/FI-2016-1383