Modifications of iterative reconstruction algorithms for the reduction of artefacts in high resolution X-ray computed tomography

X-ray Computed Tomography is a non destructive technique which allows for the visualization of the internal structure of complex objects. Most commonly, algorithms based on filtered backprojection are used for reconstruction of the projection data obtained with CT. However, the reconstruction can also be done using iterative reconstructions methods. These algorithms have shown promising results regarding the improvement of the image quality. An additional advantage is that these flexible algorithms can be modified in order to incorporate prior knowledge about the sample during the reconstruction, which allows for the reduction of artefacts. In this paper some of these advantages will be discussed and illustrated: the incorporation of an initial solution, the reduction of metal artefacts and the reduction of beam hardening artefacts

3D image analysis of a volcanic deposit

During the last decades, X-ray micro CT has become a well established technique for non-destructive testing in a wide variety of research fields. Using a series of X-ray transmission images of the sample at different projection angles, a stack of 2D cross-sections is reconstructed, resulting in a 3D volume representing the X-ray attenuation coefficients of the sample. Since the attenuation coefficient of a material depends on its density and atomic number, this volume provides valuable information about the internal structure and composition of the sample. Although much qualitative information can be derived directly from this 3D volume, researchers usually require more quantitative results to be able to provide a full characterization of the sample under investigation. This type of information needs to be retrieved using specialized image processing software. For most samples, it is imperative that this processing is performed on the 3D volume as a whole, since a sequence of 2D cross sections usually forms an inadequate approximation of the actual structure. The complete processing of a volume consists of three sequential steps. First, the volume is segmented into a set of objects. What these objects represent depends on what property of the sample needs to be analysed. The objects can be for instance concavities, dense inclusions or the matrix of the sample. When dealing with noisy data, it might be necessary to filter the data before applying the segmentation. The second step is the separation of connected objects into a set of smaller objects. This is necessary when objects appear to be connected because of the limited resolution and contrast of the scan. Separation can also be useful when the sample contains a network structure and one wants to study the individual cells of the network. The third and last step consists of the actual analysis of the various objects to derive the different parameters of interest. While some parameters require extensive calculations, others can be obtained easily. The different parameters which can be obtained are related to the size, shape and orientation of the objects. Additionally, the connectivity of a network can be analysed by comparing the set of objects before and after separation. The size of each object can be characterized by its volume, equivalent diameter and the diameter of the maximum inscribed sphere. The surface can be determined by extracting a polygonal mesh from the volume data. Calculation of Feret’s diameter reveals information about the objects elongation. Additionally, the moments of inertia can be calculated to obtain the axes of an equivalent ellipsoid. This data can be used to determine the main axis and therefore the orientation of the object within the sample. Feret’s diameter and the equivalent ellipsoid are representative for the basic shape of the object. Additionally, using a routine that fills concave regions, the convex hull of an object can be retrieved to quantify the convexity. Different ratios can be defined, which compare the surface area with the volume of the object (sphericity) or the volume of the convex hull. These ratios and the convexity characterize the objects roughness and shape. The described parameters are used to characterize volcanic deposits found in the area west of Lac Pavin (lake in Auvergne, France). The samples are taken from the most recent ‘red scoria’ layer, which is believed to be the result of the latest eruption in Western-Europe. There is however, ambiguity on the origin of the layer in terms of age and placement. The aim is to fingerprint this layer in such a way that the various eruptions in the area can be distinguished from one another. Measurements of the vesicle density, volume and connectivity of the investigated deposits provide information about the intensity of the eruption. Additionally, vesicle geometry can be related to the magmatic permeability, which is essential to the dynamics of the eruption

Latest developments in 3D analysis of geomaterials by Morpho+

At the Centre for X-ray Tomography of the Ghent University (Belgium) (www.ugct.ugent.be) besides hardware development for high-resolution X-ray CT scanners, a lot of progress is being made in the field of 3D analysis of the scanned samples. Morpho+ is a flexible 3D analysis software which provides the necessary petrophysical parameters of the scanned samples in 3D. Although Morpho+ was originally designed to provide any kind of 3D parameter, it contains some specific features especially designed for the analysis of geomaterial properties like porosity, partial porosity, pore-size distribution, grain size, grain orientation and surface determination. Additionally, the results of the 3D analysis can be visualized which enables to understand and interpret the analysis results in a straightforward way. The complementarities between high-quality X-ray CT images and flexible 3D software are opening up new gateways in the study of geomaterials

Bronnikov-aided correction for x-ray computed tomography

When a very-low-absorbing sample is scanned at an x-ray computed tomography setup with a microfocus x-ray tube and a high-resolution detector, the obtained projection images contain not only absorption contrast but also phase contrast. While images without a phase signal can be reconstructed very well, such mixed phase and absorption images give rise to severe artifacts in the reconstructed slices. A method is described that applies a correction to these mixed projections to remove the phase signal. These corrected images can then be processed using a standard filtered backprojection algorithm to obtain reconstructions with only few or no phase artifacts. This new method, which we call the Bronnikov-aided correction (BAC), can be used in a broad variety of applications and without much additional effort. It is tested on a biological and a pharmaceutical sample, and results are evaluated and discussed by comparing them with those of conventional reconstruction methods

Disintegration of sedimentary rocks used as building material: evaluation and quantification in 4D

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