Identification of Common Minerals Using Stoichiometric Calibration Method for Dual‐Energy CT

Martini, Margherita; Francus, Pierre; Trotta, Leonardo Di Schiavi; Després, Philippe

doi:10.1029/2021gc009885

Cited by 9 publications

(15 citation statements)

References 38 publications

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“…Table 5 demonstrates the number and accuracy of identified minerals for existing mineral detection methods. In contrast to the dual-energy CT chemometric calibration method [34], our work does not require the use of instruments for the medical X-ray tomography of minerals. Compared to the method using polarized light microscopy to obtain images [10], which can only identify five minerals, our method can identify 50 species with similar accuracy.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

et al. 2022

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Mineral identification is an important part of geological research. Traditional mineral identification methods heavily rely on the identification ability of the identifier and external instruments, and therefore require expensive labor expenditures and equipment capabilities. Deep learning-based mineral identification brings a new solution to the problem, which not only saves labor costs, but also reduces identification errors. However, the accuracy of existing recognition efforts is often affected by various factors such as Mohs hardness, color, picture scale, and especially light intensity. To reduce the impact of light intensity on recognition accuracy, we propose an efficient deep learning-based mineral recognition method using the luminance equalization algorithm. In this paper, we first propose a new algorithm combining histogram equalization (HE) and the Laplace algorithm, and use this algorithm to process the luminance of the identified samples, and finally use the YOLOv5 model to identify the samples. The experimental results show that our method achieves 95.6% accuracy for the identification of 50 common minerals, achieving a luminance equalization-based deep learning mineral identification method.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

et al. 2022

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“…This platform is used for several nonmedical applications, including material characterization and custom beam hardening corrections with dualenergy techniques. 6,7 The process of using raw data from this medical CT scanner is illustrated in Fig. 1.…”

Section: Working With Proprietary Formatmentioning

confidence: 99%

On the use of voxel-driven backprojection and iterative reconstruction for small ROI CT imaging

Trotta

Matenine²,

Martini³

et al. 2022

7th International Conference on Image Formation in X-Ray Computed Tomography

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In this work related to the use of a commercial medical CT scanner for the non-destructive analysis of highly attenuating materials (mineral samples), the effect of backprojection techniques and truncation artifacts corrections were explored. For small ROIs, the CT couch interferes significantly in images of small samples (few centimeters). An iterative reconstruction algorithm (OSC-TV) was used to perform reconstructions from uncorrected raw projection data made available through a collaboration with the CT vendor, who provided binaries and methods to remove low-level, proprietary data corrections (for beam hardening). The OSC-TV algorithm is customizable, allowing for the use of different forward-projection and backprojection techniques. Reconstruction parameters were tuned by performing simulations in a virtual phantom involving highly attenuating materials. Strategies to reconstruct small ROIs were also explored, with the objective of reducing truncation artifacts. Three samples were scanned to compare a ray-driven backprojection and a voxel-driven backprojection technique based on bilinear interpolation. The voxel-driven approach led to better results in terms of noise and reconstruction artifacts. An iterative ROI reconstruction technique was used to reconstruct small ROIs. This technique allows obtaining a sinogram with the projections of the ROI only. With that, truncation artifacts were reduced, which led to images with less blurring.

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“…Martini et al. (2021) applied for the first time one of these techniques, specifically the stoichiometric calibration, in the field of geology in order to identify common minerals.…”

Section: Introductionmentioning

confidence: 99%

“…However, in the 2000s, medical physicists developed DECT methods that did not require spectral information, facilitating the analysis (Bourque et al, 2014;Landry et al, 2013). Martini et al (2021) applied for the first time one of these techniques, specifically the stoichiometric calibration, in the field of geology in order to identify common minerals.…”

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confidence: 99%

“…This paper aims to test this method developed by Martini et al (2021) on a non-consolidated, fine-grained sediment core from an Arctic varved lake (South Sawtooth Lake). The analysis conditions of this case study are much more complex than those reported in Martini et al (2021) who tested the method on individual minerals that have a very simple chemical composition. In comparison, the sediment sample analysed here contains a variety of minerals, has a complex geochemistry, porosity and granulometry.…”

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confidence: 99%

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Exploring the application of dual‐energyCTto discriminate sediment facies in a varved sequence

Martini,

Francus,

Di Schiavi Trotta

et al. 2024

The Depositional Record

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Dual‐energy X‐ray computed tomography consists of imaging objects using two incident X‐ray beams of different energy to distinguish the different compounds within a sample based on their density (electron density, ρe) and elemental composition (effective atomic number, Zeff). The stoichiometric calibration for dual‐energy X‐ray computed tomography was already successfully implemented to identify single and homogeneous minerals easily and non‐destructively. It is here applied for the first time to a more complex and heterogeneous sample, a varved sediment core with three distinct facies. The output of dual‐energy X‐ray computed tomography was compared against elemental geochemistry obtained at the same resolution using a micro‐XRF core scanner. The three individual facies can be successfully differentiated using dual‐energy X‐ray computed tomography because their range of ρe and Zeff values allow their discrimination. Correlations with elemental geochemistry are also discussed but are less conclusive, probably because of variations in grain size and porosity, and because these high resolution analyses were not performed at the exact same location. The paper not only eventually discusses the limitations when using dual‐energy X‐ray computed tomography on sediments but also demonstrates its potential to quantitatively study sediment cores in a non‐destructive way.

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Identification of Common Minerals Using Stoichiometric Calibration Method for Dual‐Energy CT

Cited by 9 publications

References 38 publications

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

Mineral Identification Based on Deep Learning Using Image Luminance Equalization

On the use of voxel-driven backprojection and iterative reconstruction for small ROI CT imaging

Exploring the application of dual‐energyCTto discriminate sediment facies in a varved sequence

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