Investigating the influence of dynamic effects on two-phase flow at the pore scale with fast laboratory-based X-ray micro-CT

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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

Chemo-mechanics of salt and ice crystallization in rocks : a 4D study using X-ray micro computed tomography

COMInternational audienc

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

Several Masked Implementations of the Boyar-Peralta AES S-Box

Threshold implementation is a masking technique that provides provable security for implementations of cryptographic algorithms against power analysis attacks. In recent publications, several different threshold implementations of AES have been designed. However in most of the threshold implementations of AES, the Canright S-Box has been used. The Boyar-Peralta S-Box is an alternative implementation of the AES S-Box with a minimal circuit depth and is comparable in size to the frequently used Canright AES S-Box. In this paper, we present several versions of first-order threshold implementations of the Boyar-Peralta AES S-Box with different number of shares and several trade-offs in area, randomness and speed. To the best of our knowledge these are the first threshold implementations of the Boyar-Peralta S-Box. Our implementations compare favourably with some of the existing threshold implementations of Canright S-Box along the design trade-offs, e.g. while one of our S-Boxes is 49\% larger in area than the smallest known threshold implementation of the Canright AES S-Box, it uses 63\% less randomness and requires only 50\% of the clock cycles. We provide results of a practical security evaluation based on real power traces to confirm the first-order attack resistance of our implementations

Pore-scale freeze-thaw experiments with environmental micro-CT

AFFInternational audienc

Higher-Order Threshold Implementation of the AES S-Box

In this paper we present a threshold implementation of the Advanced Encryption Standard’s S-box which is secure against first- and second-order power analysis attacks. This security guarantee holds even in the presence of glitches, and includes resistance against bivariate attacks. The design requires an area of 7849 Gate Equivalents and 126 bits of randomness per S-box execution. The implementation is tested on an FPGA platform and its security claim is supported by practical leakage detection tests

3D imaging of clay minerals inside sandstone: pushing the spatial resolution limits using ptychographic tomography

Characterization of microporous, clay-sized particles in natural stone is essential for the understanding of their dynamics. These processes are importand in the fields of oil and gas, groundwater, building stone weathering and soil science. Methods such as X-ray micro-computed tomography is an excellent tool to study features larger than or just under 1 μm, but below the 400 nm limit, the technique falls short. Although destructive methods exists (e.g. FIB/SEM), non-destructive imaging at these very high resolutions has been impossible, until recent developments at synchrotron beam lines. In this study, we use ptychographic tomography at the cSAXS beam line of the PSI in Switzerland, for imaging of clay microstructure at resolutions down to 45 nm, which is the first application of ptychographic tomography for geological samples to our knowledge. During these experiments, relative humidity of the sample’s environment was controlled, in order to asses the influence of R.H. on the analyzed clay minerals. Based on these images, quantitative data on mineral content, porosity, connectivity and behavior under changing environmental conditions of clay mineral clusters was acquired