Fast tomographic inspection of cylindrical objects

This paper presents a method for improved analysis of objects with an axial symmetry using X-ray Computed Tomography (CT). Cylindrical coordinates about an axis fixed to the object form the most natural base to check certain characteristics of objects that contain such symmetry, as often occurs with industrial parts. The sampling grid corresponds with the object, allowing for down-sampling hence reducing the reconstruction time. This is necessary for in-line applications and fast quality inspection. With algebraic reconstruction it permits the use of a pre-computed initial volume perfectly suited to fit a series of scans where same-type objects can have different positions and orientations, as often encountered in an industrial setting. Weighted back-projection can also be included when some regions are more likely subject to change, to improve stability. Building on a Cartesian grid reconstruction code, the feasibility of reusing the existing ray-tracers is checked against other researches in the same field.Comment: 13 pages, 13 figures. submitted to Journal Of Nondestructive Evaluation (https://www.springer.com/journal/10921

4D-µCT analysis through piecewise linear fitting

4D-µCT is an increasingly popular tool to study dynamic processes in situ, for example in material science and porous media studies. The technique allows to resolve changes in a material's microstructure over time and in three spatial dimensions. Typically, a sample is scanned continuously during a relevant time-span, corresponding to multiple sequential conventional µCT scans, which are reconstructed and processed (analyzed) separately afterwards. However, the individual reconstructions of 4D-µCT scans often suffer from noise and motion artefacts. Also, the full dataset contains a temporal correlation which is typically not being exploited. In this presentation, we propose to use piecewise linear fitting, which starts from the low quality reconstructions and performs a piecewise linear fit in the time direction for each voxel. Despite yielding an improved contrast-to-noise ratio in every individual reconstruction, the technique does not introduce spatial blurring. Additionally, the technique results in a parametrization of the temporal domain, which can be directly used to analyze the dynamic process under investigation. This piecewise linear fitting technique is applicable for a broad range of applications. It does not require prior knowledge, but can be adapted to exploit prior knowledge where it is available. The technique is demonstrated on complementary samples from geological and pharmaceutical applications. The results show the improvement in the contrast to noise ratio and the potential to use the results in the further data analysis which needs to be performed after the reconstruction

Strategies in cone beam CT inspection of cylindrical objects

Axial symmetry is a feature that often occurs in industrial parts. Analysing these with X-ray computed tomography (CT), cylindrical coordinates about an axis fixed to the object form the most natural base to check certain characteristics of objects that contain such symmetry. This work presents two methods to investigate this coordinate system and to incorporate it in the current algebraic reconstruction framework and the analysis tools. The methods are applied to fast scans with few projections of cylindrical products with slight random tilts. Standard reconstruction requires more advanced and hence slower techniques in the analysis phase. Reconstruction in a symmetry adapted base needs precise knowledge about the object’s pose, but it allows aimed sampling. This takes down the reconstruction and analysis time, which is necessary when applying CT for in-line inspectio

Effect of an initial solution in iterative reconstruction of dynamically changing objects

Visualizing and analyzing dynamic processes in 3D is an emerging topic, e.g. in geosciences (Berg et al., 2009; Cnudde and Boone, 2013; Bultreys et al., accepted), which has only recently become possible due to fast, high-resolution CT scanning. However; dynamically changing objects pose a challenge in CT-imaging because the existing reconstruction algorithms, which reconstruct the sample volume from a number of scan images, presume an unchanging sample during the acquisition of the projection images. Movements or changes during the scan cause artefacts in the resulting volume. Furthermore, when fast processes are visualized, the acquisition time needs to be reduced, thus drastically decreasing the signal-to-noise ratio (SNR). To address these issues, an iterative reconstruction technique is applied, where an initial solution is provided to the algorithm. In this work, we present an evaluation of this method based on both simulations and real experimental data

Improving the reconstruction of dynamic processes by including prior knowledge

Visualizing and analyzing dynamic processes in 3 dimensions is an increasingly important topic. High-resolution CT-scanning is a suitable technique for this, as it is non-destructive and therefore does not hinder the dynamic process while it is advancing. However, CT reconstruction algorithms, which reconstruct a 3D volume from a series of projection images, assume a static sample. Motion artefacts are introduced when this assumption is invalid. This is usually solved by dividing the set of projection images in smaller subsets, each representing a time frame in which the change to the sample is assumed to be sufficiently small. Each subset can be reconstructed separately. However, due to the small size of the subsets and/or the high speed (and therefore lower statistics and higher noise) at which is scanned, the reconstruction quality is reduced. One method to improve reconstruction quality is using a priori knowledge. Of the two most used reconstruction algorithms, the iterative reconstruction scheme is best suited for this. The simultaneous algebraic reconstruction technique or SART starts from a (typically empty) volume and improves this gradually by back projecting the difference between a simulated projection from this volume and the measured projection. The resulting volume is used for the next iteration step. After a number of iterations, the solution converges to the final volume which represents the sample. In this research, this algorithm is used and adapted to take prior knowledge into account. Prior knowledge can take various forms. Using an initial volume (to start the reconstruction algorithm with) that resembles the sample is the most well-known and already presents a great improvement. This can be a volume that is reconstructed from a previous scan of the same sample, before the dynamic process is initiated, or one from after the process has finished. It is also possible to incorporate information in the algorithm about the regions in the volume where the changes are most likely to occur. The voxels in these regions are assigned a higher contribution from the back projection in comparison with their 'static' neighboring voxels which are assumed to be valid in the initial volume. This reduces the number of projections needed significantly. These forms of prior knowledge already pose a great improvement to the reconstruction quality, as is shown by the preliminary results. There are however numerous other possibilities to improve the reconstruction of dynamic processes. Other forms of prior knowledge, e.g. the continuity of changes or external measurements, can be included. Spatio-temporal correlations present another way to improve 4D-reconstruction. The projections will no longer be divided into completely separate subsets. Instead, the correlations between different projections will be used. This means that projections 'far' away from the time point that is being reconstructed will also (partially) be included. In this way the limitation of a small subset is (partially) removed, since much larger sets of projections are considered. The reconstructions that lie some time away from the reconstruction point cannot be straightforwardly included, since this would include exactly the artefacts that made the scanning of dynamic processes hard in the first place. This is a subject of further and current research. REFERENCES [1] M. Beister, D. Kolditz, W. A. Kalender, “Iterative reconstruction methods in X-ray CT,” Physica Medica, vol. 28, no. 2, pp. 94-108, Apr. 2012. [2] S. Berg, H. Ott, S. A. Klapp, A. Schwing, R. Neiteler, N. Brussee, A. Makurat, L. Leu, F. Enzmann, J.-O. Schwarz, “Real-time 3D imaging of Haines jumps in porous media flow,” Proc Natl Acad Sci U S A, vol. 110(10), pp. 3755–3759, Mar. 2013. [3] T. Bultreys, M. A. Boone, M. N. Boone, T. De Schryver, B. Masschaele, L. Van Hoorebeke, V. Cnudde, “Fast laboratory-based micro-computed tomography for pore-scale research: illustrative experiments and perspectives on the future,” Adv. Wat. Res., In Press. Available online May 2015. [4] V. Cnudde, M. N. Boone, “High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications,” Earth-Science Reviews, vol. 123, pp. 1-17, Aug. 2013. [5] G. Van Eyndhoven, K. J. Batenburg, J. Sijbers, “Region-based iterative reconstruction of structurally changing objects in CT”, IEEE Trans. Image Processing, vol. 23, no. 2, pp. 909-919, Feb. 2014. [6] L. Brabant, “Latest developments in the improvement and quantification of high resolution X-ray tomography data,” Ph.D. dissertation, Dep. Phys. and Astr., Fac. Sciences, Ghent Univ., Ghent, Belgium, 2013

Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes

This work presents a framework to exploit the synergy between Digital Volume Correlation ( DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-mu CT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360 degrees rotation

Postnatal maturation of the glomerular filtration rate in conventional growing piglets as potential juvenile animal model for preclinical pharmaceutical research

Adequate animal models are required to study the preclinical pharmacokinetics (PK), pharmacodynamics (PD) and safety of drugs in the pediatric subpopulation. Over the years, pigs were presented as a potential animal model, since they display a high degree of anatomical and physiological similarities with humans. To assess the suitability of piglets as a preclinical animal model for children, the ontogeny and maturation processes of several organ systems have to be unraveled and compared between both species. The kidneys play a pivotal role in the PK and PD of various drugs, therefore, the glomerular filtration rate (GFR) measured as clearance of endogenous creatinine (Jaffe and enzymatic assay) and exo-iohexol was determined in conventional piglets aging 8 days (n = 16), 4 weeks (n = 8) and 7 weeks (n = 16). The GFR data were normalized to bodyweight (BW), body surface area (BSA) and kidney weight (KW). Normalization to BSA and KW showed an increase in GFR from 46.57 to 100.92 mL/min/m2 and 0.49 to 1.51 mL/min/g KW from 8 days to 7 weeks of age, respectively. Normalization to BW showed a less pronounced increase from 3.55 to 4.31 mL/min/kg. The postnatal development of the GFR was comparable with humans, rendering the piglet a convenient juvenile animal model for studying the PK, PD and safety of drugs in the pediatric subpopulation. Moreover, to facilitate the assessment of the GFR in growing piglets in subsequent studies, a formula was elaborated to estimate the GFR based on plasma creatinine and BW, namely eGFR =1.879 × BW^1.092/Pcr^0.600