Comparison of different nano- and micro-focus X-ray computed tomography set-ups for the visualization of the soil microstructure and soil organic matter

2008 ◽  
Vol 34 (8) ◽  
pp. 931-938 ◽  
Author(s):  
S. Sleutel ◽  
V. Cnudde ◽  
B. Masschaele ◽  
J. Vlassenbroek ◽  
M. Dierick ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
X Ray ◽  
Soil Microstructure ◽  
X Ray Computed

Organic Matter Pore Characterization in Lacustrine Shales with Variable Maturity Using Nanometer-Scale Resolution X-ray Computed Tomography

2017 ◽  
Vol 31 (3) ◽  
pp. 2669-2680 ◽  
Author(s):  
Fujie Jiang ◽  
Jian Chen ◽  
Ziyang Xu ◽  
Zhifang Wang ◽  
Tao Hu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Organic Matter ◽  
Nanometer Scale ◽  
X Ray ◽  
Pore Characterization ◽  
X Ray Computed

Mineralization of Sialoliths Investigated by Ex Vivo and In Vivo X-ray Computed Tomography

2019 ◽  
Vol 25 (1) ◽  
pp. 151-163 ◽  
Author(s):  
Pedro Nolasco ◽  
Paulo V. Coelho ◽  
Carla Coelho ◽  
David F. Angelo ◽  
J. R. Dias ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Computed Tomography ◽  
Organic Matter ◽  
Ex Vivo ◽  
Correct Selection ◽  
Quantitative Relation ◽  
Helical Computed Tomography ◽  
X Ray ◽  
X Ray Computed

AbstractThe fraction of organic matter present affects the fragmentation behavior of sialoliths; thus, pretherapeutic information on the degree of mineralization is relevant for a correct selection of lithotripsy procedures. This work proposes a methodology for in vivo characterization of salivary calculi in the pretherapeutic context. Sialoliths were characterized in detail by X-ray computed microtomography (μCT) in combination with atomic emission spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Correlative analysis of the same specimens was performed by in vivo and ex vivo helical computed tomography (HCT) and ex vivo μCT. The mineral matter in the sialoliths consisted essentially of apatite (89 vol%) and whitlockite (11 vol%) with average density of 1.8 g/cm3. In hydrated conditions, the mineral mass prevailed with 53 ± 13 wt%, whereas the organic matter, with a density of 1.2 g/cm3, occupied 65 ± 10% of the sialoliths’ volume. A quantitative relation between sialoliths mineral density and X-ray attenuation is proposed for both HCT and μCT.

scholarly journals Three-dimensional soil organic matter distribution, accessibility and microbial respiration in macroaggregates using osmium staining and synchrotron X-ray computed tomography

SOIL ◽  
2016 ◽  
Vol 2 (4) ◽  
pp. 659-671 ◽  
Author(s):  
Barry G. Rawlins ◽  
Joanna Wragg ◽  
Christina Reinhard ◽  
Robert C. Atwood ◽  
Alasdair Houston ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Organic Matter ◽  
Transition Probabilities ◽  
Pore Space ◽  
Pearson Correlation ◽  
Soil Core ◽  
Length Scales ◽  
X Ray ◽  
Mineral Phases ◽  
X Ray Computed

Abstract. The spatial distribution and accessibility of organic matter (OM) to soil microbes in aggregates – determined by the fine-scale, 3-D distribution of OM, pores and mineral phases – may be an important control on the magnitude of soil heterotrophic respiration (SHR). Attempts to model SHR on fine scales requires data on the transition probabilities between adjacent pore space and soil OM, a measure of microbial accessibility to the latter. We used a combination of osmium staining and synchrotron X-ray computed tomography (CT) to determine the 3-D (voxel) distribution of these three phases (scale 6.6 µm) throughout nine aggregates taken from a single soil core (range of organic carbon (OC) concentrations: 4.2–7.7 %). Prior to the synchrotron analyses we had measured the magnitude of SHR for each aggregate over 24 h under controlled conditions (moisture content and temperature). We test the hypothesis that larger magnitudes of SHR will be observed in aggregates with (i) shorter length scales of OM variation (more aerobic microsites) and (ii) larger transition probabilities between OM and pore voxels. After scaling to their OC concentrations, there was a 6-fold variation in the magnitude of SHR for the nine aggregates. The distribution of pore diameters and tortuosity index values for pore branches was similar for each of the nine aggregates. The Pearson correlation between aggregate surface area (normalized by aggregate volume) and normalized headspace C gas concentration was both positive and reasonably large (r  =  0.44), suggesting that the former may be a factor that influences SHR. The overall transition probabilities between OM and pore voxels were between 0.07 and 0.17, smaller than those used in previous simulation studies. We computed the length scales over which OM, pore and mineral phases vary within each aggregate using 3-D indicator variograms. The median range of models fitted to variograms of OM varied between 38 and 175 µm and was generally larger than the other two phases within each aggregate, but in general variogram models had ranges  <  250 µm. There was no evidence to support the hypotheses concerning scales of variation in OM and magnitude of SHR; the linear correlation was 0.01. There was weak evidence to suggest a statistical relationship between voxel-based OM–pore transition probabilities and the magnitudes of aggregate SHR (r  =  0.12). We discuss how our analyses could be extended and suggest improvements to the approach we used.

X-Ray Computed Tomography of Ultralightweight Metals

1999 ◽  
Vol 11 (1) ◽  
pp. 199-211
Author(s):  
J. M. Winter ◽  
R. E. Green ◽  
A. M. Waters ◽  
W. H. Green
Keyword(s):  
Computed Tomography ◽  
X Ray ◽  
X Ray Computed

scholarly journals Nano- scale X-ray Computed Tomography: Morphological Analysis of Fuel Cell Electrodes

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster
Keyword(s):  
Computed Tomography ◽  
Fuel Cell ◽  
Morphological Analysis ◽  
Fuel Cell Electrodes ◽  
X Ray ◽  
Nano Scale ◽  
X Ray Computed

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

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