Quantitative parametric imaging by ultrasound computed tomography of trees under anisotropic conditions: Numerical case study

Espinosa, Luis; Prieto, Flavio; Brancheriau, Loïc; Lasaygues, Philippe

doi:10.1016/j.ultras.2019.106060

Cited by 10 publications

(10 citation statements)

References 15 publications

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“…For the preparation of the tomographic image, wave propagation speed ranges are associated with colors. A thinner mesh produces a greater scope for the inspection inside the inspected element, as there will be a greater number of measurement routes (Divos and Divos 2005; Zhang and Khoshelham 2020; Espinosa et al 2020;Dudkiewicz and Durlak 2021), allowing the visualization of deteriorations in early stages (Gilbert et al 2016). However, increasing the number of theoretical measurement routes implies increasing the measurement points in the stem contour, which consequently increases field work (Espinosa et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…A thinner mesh produces a greater scope for the inspection inside the inspected element, as there will be a greater number of measurement routes (Divos and Divos 2005; Zhang and Khoshelham 2020; Espinosa et al 2020;Dudkiewicz and Durlak 2021), allowing the visualization of deteriorations in early stages (Gilbert et al 2016). However, increasing the number of theoretical measurement routes implies increasing the measurement points in the stem contour, which consequently increases field work (Espinosa et al 2020). As an example, a diffraction mesh such as the one used in this study, with six measurement points in the stem contour and measurements made in only one direction (e.g., 1 for 2 or 2 for 1), results in 15 routes to be measured in the tree (Fig.…”

Section: Introductionmentioning

confidence: 99%

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Tomographic images of tree trunks generated using ultrasound and post-processed images: Influence of the number of measurement points

Palma

Gonçalves

2022

BioRes

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Tomography is a technique increasingly used in tree inspections. This technique can be performed in two stages: field testing and postimage processing. To feed the tomographic image construction software, it is necessary to adopt a measurement grid composed of points positioned on the perimeter of the stem. The images are generated through spatial interpolation algorithms. From a theoretical point of view, more measurement points taken in the perimeter of the stem result in better interpolation results. However, an increase in the number of mesh points causes a substantial increase in field work and image processing time. The general objective of this study was to verify the influence of the number of measurement points of the diffraction mesh on the ultrasound tomography results. For this purpose, ten simulated discs were used, all 500 mm in diameter and with different defects in terms of size, position, and geometry. In each of the discs, diffraction grids were simulated with 6, 8, 10, 12, and 14 measurement points in the contour. The results showed that a favorable combination of accuracy and a minimization of effort can be achieved with diffraction mesh with number of measurement points calculated as five times the perimeter of the trunk in meters.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tomographic images of tree trunks generated using ultrasound and post-processed images: Influence of the number of measurement points

Palma

Gonçalves

2022

BioRes

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“…It cannot be ruled out, however, that both the positioning and the injuries size influence the probability of detecting deteriorated regions by using a tomographic image (Espinosa et al, 2020), and that prospecting with the Pressler auger may not have accessed the exact region of reservoirs location or deformations.…”

Section: Discussionmentioning

confidence: 99%

Oleoresin prospection in Copaifera sp. trees by using impulse tomography

Martins

Santos

Latorraca

2021

CERNE

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Background: Copaiba oil (Copaifera L.) is a raw material used by pharmaceutical, cosmetic, and energy industries. However, the difficulty in locating the oleoresin reservoirs, is an obstacle to its continued supply, affecting the sustainable commercialization of the product. So, the potential of impulse tomography for prospecting oleoresin reservoirs in the trunk of 18 Copaifera sp. trees was tested, in cross-sections at heights levels 0% (DBH), 25%, 50%, 75%, and 100% (1st fork). The impulse tomography prospections (ITP) were performed only at the 0%, in others 12 trees, because of the risk associated with climbing hollow trees. In total 30 trees were tested. Altogether 102 tomograms were analyzed obtaining: average mechanical wave propagation speed (aMPS), minimum mechanical wave propagation speed (minS) and maximum mechanical wave propagation speed (maxS), prospecting height (Hp%) and total tree height (Ht), diameter at the Hp% (Dhp), and low speed mechanical wave propagation percentage areas (LSa%). These variables were analyzed using multivariate analysis. Results:The reservoirs were located exclusively at DBH and confirmed by borer prospection increment in 26.7% of the trees. ITP resulted in 37.3% of correct answers and 62.7% of errors, considering the 99 tomograms. However, it was found that the ITP is efficient to indicate sections for which no significant reservoir or hollow presence is expected to be found. The analysis of the main components showed that, except for Ht, the components are good indicators for the location of the reservoirs. Conclusion:We were able to use tomography to search reservoirs with a significant amount of oleoresin, identify hollow trees, and indicate the exclusion of trees that do not have reservoirs or other alterations.

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“…To adapt the matrix formulation to consider the slowness dependency on the angle of propagation, the Christoffel equation was approximated by a polynomial function of the angle of propagation (5th degree). This polynomial was selected from an R 2 evaluation using different degrees, allowing the determination of the specific elastic parameters of the Christoffel equation (Espinosa et al 2020). Then, the slowness for a pixel k crossed by a ray m was written as:…”

Section: Inversion Proceduresmentioning

confidence: 99%

Ultrasound computed tomography on standing trees: accounting for wood anisotropy permits a more accurate detection of defects

Espinosa

Brancheriau

Cortes³

et al. 2020

Annals of Forest Science

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& Key message Considering anisotropy in image reconstruction algorithm for ultrasound computed tomography of trees resulted in a more accurate detection of defects compared to common approaches used. & Context Ultrasound computed tomography is a suitable tool for nondestructive evaluation of standing trees. Until now, to simplify the image reconstruction process, the transverse cross-section of trees has been considered as quasi-isotropic and therefore limiting the defect identification capability. & Aims An approach to solve the inverse problem for tree imaging is presented, using an ultrasound-based method (travel-time computed tomography) suited to the anisotropy of wood material and validated experimentally. & Methods The proposed iterative method focused on finding a polynomial approximation of the slowness in each pixel of the image depending on the angle of propagation, modifying the curved trajectories by means of a raytracing method. This method allowed a mapping of specific elastic constants using nonlinear regression. Experimental validation was performed using sections of green wood from a pine tree (Pinus pinea L.), with configurations that include a healthy case, a centered, and an off-centered defect. & Results Images obtained using the proposed method led to a more accurate location of the defects compared to the filtered backprojection algorithm (isotropic hypothesis), considered as reference. & Conclusion The performed experiments demonstrated that considering the wood anisotropy in the imaging process led to a better defect detection compared to the use of a common imaging technique.

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Quantitative parametric imaging by ultrasound computed tomography of trees under anisotropic conditions: Numerical case study

Cited by 10 publications

References 15 publications

Tomographic images of tree trunks generated using ultrasound and post-processed images: Influence of the number of measurement points

Tomographic images of tree trunks generated using ultrasound and post-processed images: Influence of the number of measurement points

Oleoresin prospection in Copaifera sp. trees by using impulse tomography

Ultrasound computed tomography on standing trees: accounting for wood anisotropy permits a more accurate detection of defects

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