Full-field X-ray orientation imaging using convex optimization and a discrete representation of six-dimensional position - orientation space

TL;DR: In this paper, the reconstruction problem is formulated as a global minimisation prob- lem, where the reconstruction of a single grain is the solution that minimizes a functional, and all the functionals used include a data fidelity term which ensures that the reconstruction is consistent with the measured diffraction data, and then an additional regularization term is added, like the l 1-norm minimization of the solution vector, that tries to limit the number of orientations per real-space voxel, or a Total Variation operator over the sum of the orientation part of the

Abstract: This Ph.D. thesis is about the development and formalization of a six-dimensional tomography method, for the reconstruction of local orientation in poly-crystalline materials. This method is based on a technique known as diffraction contract tomography (DCT), mainly used in synchrotrons, with a monochromatic and parallel high energy X-ray beam. DCT exists since over a decade now, but it was always employed to analyze undeformed or nearly undeformed materials, described by “grains” with a certain average orientation. Because an orientation can be parametrized by the used of only three num- bers, the local orientation in the grains is modelled by a six-dimensional space X6 = R3 ⊗ O3, that is the outer product between a three-dimensional real- space and another three-dimensional orientation-space. This means that for each point of the real-space, there could be a full three-dimensional orientation- space, which however in practice is restricted to a smaller region of interest called “local orientation-space”. The reconstruction problem is then formulated as a global minimisation prob- lem, where the reconstruction of a single grain is the solution that minimizes a functional. There can be different choices for the functionals to use, and they depend on the type of reconstructions one is looking for, and on the type of a priori knowledge is available. All the functionals used include a data fidelity term which ensures that the reconstruction is consistent with the measured diffraction data, and then an additional regularization term is added, like the l1-norm minimization of the solution vector, that tries to limit the number of orientations per real-space voxel, or a Total Variation operator over the sum of the orientation part of the six-dimensional voxels, in order to enforce the homogeneity of the grain volume. When first published, the results on synthetic data from the third chapter high- lighted some key features of the proposed framework, and showed that it was in principle possible to extend DCT to the reconstruction of moderately de- formed materials, but it was unclear whether it could work in practice. The following chapters instead confirm that the proposed framework is viable for reconstructing moderately deformed materials, and that in conjunction with other techniques, it could also overcome the limitations imposed by the grain indexing, and be applied to more challenging textured materials.