Spectral X‐ray computed micro tomography: 3‐dimensional chemical imaging

Sittner, Jonathan; Godinho, José R. A.; Renno, Axel D.; Cnudde, Veerle; Boone, Matthieu; Schryver, Thomas De; Loo, Denis Van; Merkulova, Margarita; Roine, Antti; Liipo, Jussi

doi:10.1002/xrs.3200

Cited by 19 publications

(15 citation statements)

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“…For example, by comparing a classified image from mineral liberation analysis (MLA) of a cross-section of the sample with the corresponding slice in a CT image, it is possible to determine the range of grey-values that correspond to the phases visible at that cross-section [14,23,26]. This validation method will be exemplified for the samples named Carbonate and Sheelite ore. New CT techniques that allow the measurement of chemical information about the elements inside a sample are currently under development, e.g., spectral CT [26,38] or hyperspectral CT [39]. Such methods have the advantage of not requiring sample preparation nor the complex image processing associated with correlative methods.…”

Section: Mineralogical Validationmentioning

confidence: 99%

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Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

et al. 2021

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This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by x-ray computed micro-tomography (CT), here named mounted single particle characterization for 3D mineralogical analysis (MSPaCMAn). The method allows minimizing the impact of imaging artefacts that make the classification of voxels inaccurate and thus hinder the use of CT to characterize natural particulate materials. MSPaCMAn consists of (1) sample preparation as particle dispersions; (2) image processing optimized towards the labelling of individual particles in the sample; (3) phase identification performed at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an improved accuracy of phase classification, a higher number of detected phases, a smaller grain size that can be detected, and individual particle statistics can be measured instead of just bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited due to unreliable phase classification and quantification. MSPaCMAn could be the foundation of 3D semi-automated mineralogy similar to the commonly used 2D image-based semi-automated mineralogy methods.

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Section: Mineralogical Validationmentioning

confidence: 99%

“…The reconstructed CT value interval was [0, 26]. Using these scanning conditions, the effective energy was calculated from the measured transmission spectrum (Figure S2) using a spectral detector [38]. Image processing was achieved using Avizo.…”

Section: Qz/py Samplementioning

confidence: 99%

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

et al. 2021

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“…The main advantage of the spectral content in the images is the possibility of material identification at lab X-ray sources. Due to their construction as full-field pixellated devices, these cameras allow for capture of chemical information not only in X-ray fluorescence (XRF) applications, but also in full-field transmission imaging [ 1 , 2 ] and transmission computed tomography (CT) [ 3 , 4 , 5 ]. Even in the established application domain of XRF imaging, these hyperspectral systems provide added value through their 2D nature as opposed to the single-element energy dispersive detectors commonly used in XRF measurement setups, allowing for improvements in speed and image quality for fluorescence measurements as well [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

The Spectral X-ray Imaging Data Acquisition (SpeXIDAQ) Framework

Assche

Vanheule

Hoorebeke

et al. 2021

Sensors

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Photon counting X-ray imagers have found their way into the mainstream scientific community in recent years, and have become important components in many scientific setups. These camera systems are in active development, with output data rates increasing significantly with every new generation of devices. A different class of PCD (Photon Counting Detector) devices has become generally available, where camera data output is no longer a matrix of photon counts but instead direct measurements of the deposited charge per pixel in every frame, which requires significant off-camera processing. This type of PCD, called a hyperspectral X-ray camera due to its fully spectroscopic output, yet again increases the demands put on the acquisition and processing backend. Not only are bandwidth requirements increased, but the need to do extensive data processing is also introduced with these hyperspectral PCD devices. To cope with these new developments the Spectral X-ray Imaging Data Acquisition framework (SpeXIDAQ) has been developed. All aspects of the imaging pipeline are handled by the SpeXIDAQ framework: from detector control and frame grabbing, to processing, storage and live visualisation during experiments.

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“…Therefore, chemically different particles can be distinguished inside a sample from a single CT scan. The method is able to distinguish elements with K-edges in the range from 25 to 160 keV, which applies to elements with Z > 48 (Sittner et al, 2020).…”

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Spectral X-ray computed micro tomography: a tool for 3-dimensional chemical imaging

Sittner¹,

Merkulova²,

Godinho³

et al. 2021

Preprint

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Image-based analytical tools in geosciences are indispensable for the characterization of minerals, but most of them are limited to the surface of a polished plane in a sample and lack 3D information. X-ray micro computed tomography (micro CT) provides the missing 3D information of the microstructures inside samples. However, a major drawback of micro CT in the characterization of minerals is the lack of chemical information that makes mineral classification challenging.Spectral X-ray micro computed tomography (Sp-CT) is a new and evolving tool in different applications such as medicine, security, material science, and geology. This non-destructive method uses a multi-pixel photon-counting detector (PCD) such as cadmium telluride (CdTe) in combination with a conventional CT scanner (TESCAN CoreTOM) to image a sample and detect its transmitted polychromatic X-ray spectrum. Based on the spectrum, elements in a sample can be identified by an increase in attenuation at specific K-edge energies. Therefore, chemically different particles can be distinguished inside a sample from a single CT scan. The method is able to distinguish elements with K-edges in the range from 25 to 160 keV, which applies to elements with Z > 48 (Sittner et al., 2020).We present results from various sample materials. Different pure elements and element oxides were measured to compare the position of theoretical and measured K-edge energies. All measured K-edge energies are slightly above the theoretical value, but based on the results a correction algorithm could be developed. Furthermore, different monazite grains were investigated, which can be divided into two groups with respect to the content of different RE elements on the basis of the spectrum: La-Ce-rich and La-Ce-poor. In addition, samples from the Au-U Witwatersrand Supergroup demonstrate the potential applications of Sp-CT for geological samples. We measured different drill core samples from the Kalkoenkrans Reef at the Welkom Gold field. Sp-CT can distinguish gold, uraninite and galena grains based on their K-edge energies in the drill core without preparation.Sittner, J., Godinho, J. R. A., Renno, A. D., Cnudde, V., Boone, M., De Schryver, T., Van Loo, D., Merkulova, M., Roine, A., & Liipo, J. (2020). Spectral X-ray computed micro tomography: 3-dimensional chemical imaging. X-Ray Spectrometry, September, 1&#8211;14.

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Spectral X‐ray computed micro tomography: 3‐dimensional chemical imaging

Cited by 19 publications

References 62 publications

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

Mounted Single Particle Characterization for 3D Mineralogical Analysis—MSPaCMAn

The Spectral X-ray Imaging Data Acquisition (SpeXIDAQ) Framework

Spectral X-ray computed micro tomography: a tool for 3-dimensional chemical imaging

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