Latest developments in the improvement and quantification of high resolution X-ray tomography data

X-ray Computed Tomography (CT) is a powerful tool to visualize the internal structure of objects. Although X-ray CT is often used for medical purposes, it has many applications in the academic and industrial world. X-ray CT is a non destructive tool which provides the possibility to obtain a three dimensional (3D) representation of the investigated object. The currently available high resolution systems can achieve resolutions of less than one micrometer which makes it a valuable technique for various scientific and industrial applications. At the Centre for X-ray Tomography of the Ghent University (UGCT) research is performed on the improvement and application of high resolution X-ray CT (µCT). Important aspects of this research are the development of state of the art high resolution CT scanners and the development of software for controlling the scanners, reconstruction software and analysis software. UGCT works closely together with researchers from various research fields and each of them have their specific requirements. To obtain the best possible results in any particular case, the scanners are developed in a modular way, which allows for optimizations, modifications and improvements during use. Another way of improving the image quality lies in optimization of the reconstruction software, which is why the software package Octopus was developed in house. Once a scanned volume is reconstructed, an important challenge lies in the interpretation of the obtained data. For this interpretation visualization alone is often insufficient and quantitative information is needed. As researchers from different fields have different needs with respect to quantification of their data, UGCT developed the 3D software analysis package Morpho+ for analysing all kinds of samples. The research presented in this work focuses on improving the accuracy and extending the amount of the quantitative information which can be extracted from µCT data. Even if a perfect analysis algorithm would exist, it would be impossible to accurately quantify data of which the image quality is insufficient. As image quality can significantly be improved with the aid of adequate reconstruction techniques, the research presented in this work focuses on analysis as well as reconstruction software. As the datasets obtained with µCT at UGCT are of substantial size, the possibility to process large datasets in a limited amount of time is crucial in the development of new algorithms. The contributions of the author can be subdivided in three major aspects of the processing of CT data: The modification of iterative reconstruction algorithms, the extension and optimization of 3D analysis algorithms and the development of a new algorithm for discrete tomography. These topics are discussed in more detail below. A main aspect in the improvement of image quality is the reduction of artefacts which often occur in µCT such as noise-, cone beam- and beam hardening artefacts. Cone beam artefacts are a result of the cone beam geometry which is often used in laboratory based µCT and beam hardening is a consequence of the polychromaticity of the beam. Although analytical reconstruction algorithms based on filtered back projection are still most commonly used for the reconstruction of µCT datasets, there is another approach which is becoming a valuable alternative: iterative reconstruction algorithms. Iterative algorithms are inherently better at coping with the previously mentioned artefacts. Additionally iterative algorithms can improve image quality in case the number of available projections or the angular range is limited. In chapter 3 the possibility to modify these algorithms to further improve image quality is investigated. It is illustrated that streak artefacts which can occur when metals are present in a sample can be significantly reduced by modifying the reconstruction algorithm. Additionally, it is demonstrated that the incorporation of an initial solution (if available) allows reducing the required number of projections for a second slightly modified sample. To reduce beam hardening artefacts, the physics of the process is modelled and incorporated in the iterative reconstruction algorithm, which results in an easy to use and efficient algorithm for the reduction of beam hardening artefacts and requires no prior knowledge about the sample. In chapter 4 the 3D analysis process is described. In the scope of this work, algorithms of the 3D-analysis software package Morpho+ were optimized and new methods were added to the program, focusing on quantifying connectivity and shape of the phases and elements in the sample, as well as obtaining accurate segmentation, which is essential step in the analysis process is the segmentation of the reconstructed sample. Evidently, the different phases in the sample need to be separated from one another. However, often a second segmentation step is needed in order to separate the different elements present in a volume, such as pores in a pore network, or to separate elements which are physically separated but appear to be connected on the reconstructed images to limited resolution and/or limited contrast of the scan. The latter effect often occurs in the process of identifying different grains in a geological sample. Algorithms which are available for this second segmentation step often result in over-segmentation, i.e. elements are not only separated from one another but also separations inside a single element occur. To overcome this effect an algorithm is presented to semi-automically rejoin the separated parts of a single element. Additionally, Morpho+ was extended with tools to extract information about the connectivity of a sample, which is difficult to quantify but important for samples from various research fields. The connectivity can be described with the aid of the calculation of the Euler Number and tortuosity. Moreover, the number of neighbouring objects of each object can be determined and the connections between objects can be quantified. It is now also possible to extract a skeleton, which describes the basic structure of the volume. A calculation of several shape parameters was added to the program as well, resulting in the possibility to visualize the different objects on a disc-rod diagram. The many possibilities to characterize reconstructed samples with the aid of Morpho+ are illustrated on several applications. As mentioned in the previous section, an important aspect for correctly quantifying µCT data is the correct segmentation of the different phases present in the sample. Often it is the case that a sample consists of only one or a limited number of materials (and surrounding air). In this case this prior knowledge about the sample can be incorporated in the reconstruction algorithm. These kind of algorithms are referred to as discrete reconstruction algorithms, which are used when only a limited number of projections is available. Chapter 5 deals with discrete reconstruction algorithms. One of these algorithms is the Discrete Algebraic Reconstruction Technique, which combines iterative with discrete reconstruction and has shown excellent results. DART requires knowledge about the attenuation coefficient(s) and segmentation threshold(s) of the material(s). For µCT applications (resulting in large datasets) reconstruction times can significantly increase when DART is used in comparison with standard iterative reconstruction, as DART requires more iterations. This complicates the practical applicability of DART for routine applications at UGCT. Therefore a modified algorithm (based on the DART algorithm) for reconstruction of samples consisting out of only one material and surrounding air was developed in the scope of this work, which is referred to as the Experimental Discrete Algebraic Reconstruction Technique (EDART). The goal of this algorithm is to obtain better reconstruction results in comparison with standard iterative reconstruction algorithms, without significantly increasing reconstruction time. Moreover, a fast and intuitive technique to estimate the attenuation coefficient and threshold was developed as a part of the EDART algorithm. In chapter 5 it is illustrated that EDART provides improved image quality for both phantom and real data, in comparison with standard iterative reconstruction algorithms, when only a limited number of projections is available. The algorithms presented in this work can be subsequently applied but can also be combined with one another. It is for example illustrated in chapter 5 that the beam hardening correction method can also be incorporated in the EDART algorithm. The combination of the introduced methods allows for an improvement in the process of extracting accurate quantitative information from µCT data

Realistic CT image simulation tools for laboratory based X-ray CT at UGCT

In laboratory based X-ray Computed Tomography (CT), the grey values in the resulting CT image depend on several scanning conditions such as the emitted spectrum, the response characteristics of the detector and beam filtration. Furthermore, due to beam hardening also the morphology and composition of the sample itself will have a significant influence. Therefore, to optimise scanning conditions simulations which incorporate all factors determining the imaging process are required. In this paper, two programs developed at the Centre for X-ray Tomography of the Ghent University (UGCT) are presented which allow a complete and realistic simulation of the obtained CT image

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

Morpho+ : a software package for the three-dimensional analysis of X-ray computed tomography data

Analysis software packages are a useful tool to extract quantitative information from datasets obtained with X-ray tomography. At the Centre for X-ray Tomography of the Ghent University (UGCT) (www.ugct.ugent.be) a flexible 3D analysis software package, Morpho+, has been developed. Morpho+ provides the possibility to perform 3D measurements on datasets from various research fields. The porosity or volume fraction of the volume investigated can be determined. Several segmentation algorithms are included, to subdivide the volume into different objects such as pores or grains. For these objects various parameters can be extracted, such as the surface, diameter of the maximum inscribed sphere, diameter of the minimum circumscribed sphere, equivalent diameter, mean gray value, orientation and a measure for the sphericity. Additionally, it is possible to determine the connectivity by means of the Euler Number, size of the bottlenecks and the number of neighbouring objects. A lot of effort has been made to develop new algorithms (such as a new skeletonization algorithm and a method to reduce oversegmentation) and to improve existing algorithms, to ensure that large datasets can be processed in a small amount of time. The user interface is intuitive and flexible. Every step in the analysis process is visualized to enable easy interpretation of the result

Advanced architectural descriptors in foams: novel 3D computational methods

This work presents 3D computational strategies aimed at providing foam de-structuration of the basic components of a cellular material (struts and cell walls) offering the possibility of analysing separately the structural elements that play an important role in the physical properties of thee materials. Two different methodologies have been used depending on the topological similarities existing between the struts and cell walls: 3D erosion-dilation procedure (thick struts) and solid classification algorithm (thin struts). In a second step, analysis of cell walls is performed in order to show the advantages of analysing separately the two foams components. Particularly, cell wall thickness distribution reveals differences that could not be found prior to the de-structuration

Validation of in situ applicable measuring techniques for analysis of the water adsorption by stone

As the water adsorbing behaviour (WAB) of stone is a key factor for most degradation processes, its analysis is a decisive aspect when monitoring deterioration and past conservation treatments, or when selecting a proper conservation treatment. In this study the performance of various non-destructive methods for measuring the WAB are compared, with the focus on the effect of the variable factors of the methods caused by their specific design. The methods under study are the contact-sponge method (CSM), the Karsten tube (KT) and the Mirowski pipe (MIR). Their performance is compared with the standardized capillary rise method (CR) and the results are analysed in relation to the open porosity of different lithotypes. Furthermore the effect of practical encumbrances which could limit the application of these methods was valuated. It was found that KT and CSM have complementary fields of investigation, where CSM is capable of measuring the initial water uptake of less porous materials with a high precision, while KT was found commodious for measuring longer contact times for more porous lithotypes. MIR showed too many discommodities, leading to unreliable results. To adequately compare the results of the different methods, the size of the contact area appears to be the most influential factor, whereas the contact material and pressure on the surface do not indicate a significant influence on the results. The study of these factors is currently being extended by visualization of the water adsorption process via X-ray and neutron radiography in combination with physico-mathematical models describing the WAB