Bringing modern mathematical modeling to orientation imaging for material science

The vast majority of metallic and ceramic objects have a granular microstructure, which has a direct influence on their mechanical behaviour. Understanding the microstructure of these materials is especially important for nuclear reactors and other safety-critical applications in which they are used. Modern mathematical tools and recent developments in computed tomography can be used to study the evolution of these materials when they are being deformed or heated

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 r

X-ray orientation microscopy using topo-tomography and multi-mode diffraction contrast tomography

Polycrystal orientation mapping techniques based on full-field acquisition schemes like X-ray Diffraction Contrast Tomography and certain other variants of 3D X-ray Diffraction or near-field High Energy Diffraction Microscopy enable time efficient mapping of 3D grain microstructures. The spatial resolution obtained with this class of monochromatic beam X-ray diffraction imaging approaches remains typically below the ultimate spatial resolution achievable with X-ray imaging detectors. Introducing a generalised reconstruction framework enabling the combination of acquisitions with different detector pixel size and sample tilt settings provide a pathway towards 3D orientation mapping with a spatial resolution approaching the one of state of the art X-ray imaging detector systems

A data consistent variational segmentation approach suitable for real-time tomography

Computed Tomography (CT) is an imaging technique that allows to reconstruct volumetric information of the analyzed objects from their projections. The most popula

Numerical methods for low-dose EDS tomography

Energy-dispersive X-ray spectroscopic (EDS) tomography is a powerful three-dimensional (3D) imaging technique for characterizing the chemical composition and structure of nanomaterials. However, the accuracy and resolution are typically hampered by the limited number of tilt images that can be measured and the low signal-to-noise ratios (SNRs) of the energy-resolved tilt images. Various sophisticated reconstruction algorithms have been proposed for specific types of samples and imaging conditions, yet deciding on which algorithm to use for each new case remains a complex problem. In this paper, we propose to tailor the reconstruction algorithm for EDS tomography in three aspects: (1) model the reconstruction problem based on an accurate assumption of the data statistics; (2) regularize the reconstruction to incorporate prior knowledge; (3) apply bimodal tomography to augment the EDS data with a high-SNR modality. Methods for the three aspects can be combined in one reconstruction procedure as three modules. Therefore, a reconstruction algorithm can be constructed as a ‘recipe’. We also provide guidelines for preparing the recipe based on conditions and assumptions for the data. We investigate the effects of different recipes on both simulated data and real experimental data. The results show that the preferred recipe depends on both acquisition conditions and sample properties, and that the image quality can be enhanced using a properly tailored recipe

Three-dimensional full-field X-ray orientation microscopy

International audienceA previously introduced mathematical framework for full-field X-ray orientation microscopy is for the first time applied to experimental near-field diffraction data acquired from a polycrystalline sample. Grain by grain tomographic reconstructions using convex optimization and prior knowledge are carried out in a six-dimensional representation of position-orientation space, used for modelling the inverse problem of X-ray orientation imaging. From the 6D reconstruction output we derive 3D orientation maps, which are then assembled into a common sample volume. The obtained 3D orientation map is compared to an EBSD surface map and local misorientations, as well as remaining discrepancies in grain boundary positions are quantified. The new approach replaces the single orientation reconstruction scheme behind X-ray diffraction contrast tomography and extends the applicability of this diffraction imaging technique to material micro-structures exhibiting sub-grains and/or intra-granular orientation spreads of up to a few degrees. As demonstrated on textured sub-regions of the sample, the new framework can be extended to operate on experimental raw data, thereby bypassing the concept of orientation indexation based on diffraction spot peak positions. This new method enables fast, three-dimensional characterization with isotropic spatial resolution, suitable for time-lapse observations of grain microstructures evolving as a function of applied strain or temperature

Emulation of X-ray light-field cameras

X-ray plenoptic cameras acquire multi-view X-ray transmission images in a single exposure (light-field). Their development is challenging: designs have appeared only recently, and they are still affected by important limitations. Concurrently, the lack of available real X-ray light-field data hinders dedicated algorithmic development. Here, we present a physical emulation setup for rapidly exploring the parameter space of both existing and conceptual camera designs. This will assist and accelerate the design of X-ray plenoptic imaging solutions, and provide a tool for generating unlimited real X-ray plenoptic data. We also demonstrate that X-ray light-fields allow for reconstructing sharp spatial structures in three-dimensions (3D) from single-shot data

Flexible plenoptic X-ray microscopy

X-ray computed tomography (CT) is an invaluable technique for generating three-dimensional (3D) images of inert or living specimens. X-ray CT is used in many scientific, industrial, and societal fields. Compared to conventional 2D X-ray imaging, CT requires longer acquisition times because up to several thousand projections are required for reconstructing a single high-resolution 3D volume. Plenoptic imaging—an emerging technology in visible light field photography—highlights the potential of capturing quasi-3D information with a single exposure. Here, we show the first demonstration of a flexible plenoptic microscope operating with hard X-rays; it is used to computationally reconstruct images at different depths along the optical axis. The experimental results are consistent with the expected axial refocusing, precision, and spatial resolution. Thus, this proof-of-concept experiment opens the horizons to quasi-3D X-ray imaging, without sample rotation, with spatial resolution of a few hundred nanometres