Non-destructive wood identification using X-ray µCT scanning: which resolution do we need?

BACKGROUND: Taxonomic identification of wood specimens provides vital information for a wide variety of academic (e.g. paleoecology, cultural heritage studies) and commercial (e.g. wood trade) purposes. It is generally accomplished through the observation of key anatomical features. Classic methodologies mostly require destructive sub-sampling, which is not always acceptable. X-ray computed micro-tomography (µCT) is a promising non-destructive alternative since it allows a detailed non-invasive visualization of the internal wood structure. There is, however, no standardized approach that determines the required resolution for proper wood identification using X-ray µCT. Here we compared X-ray µCT scans of 17 African wood species at four resolutions (1 µm, 3 µm, 8 µm and 15 µm). The species were selected from the Xylarium of the Royal Museum for Central Africa, Belgium, and represent a wide variety of wood-anatomical features. RESULTS: For each resolution, we determined which standardized anatomical features can be distinguished or measured, using the anatomical descriptions and microscopic photographs on the Inside Wood Online Database as a reference. We show that small-scale features (e.g. pits and fibres) can be best distinguished at high resolution (especially 1 µm voxel size). In contrast, large-scale features (e.g. vessel porosity or arrangement) can be best observed at low resolution due to a larger field of view. Intermediate resolutions are optimal (especially 3 µm voxel size), allowing recognition of most small- and large-scale features. While the potential for wood identification is thus highest at 3 µm, the scans at 1 µm and 8 µm were successful in more than half of the studied cases, and even the 15 µm resolution showed a high potential for 40% of the samples. CONCLUSIONS: The results show the potential of X-ray µCT for non-destructive wood identification. Each of the four studied resolutions proved to contain information on the anatomical features and has the potential to lead to an identification. The dataset of 17 scanned species is made available online and serves as the first step towards a reference database of scanned wood species, facilitating and encouraging more systematic use of X-ray µCT for the identification of wood species.

Implementation of a dual-phase grating interferometer for multi-scale characterization of building materials by tunable dark-field imaging.

The multi-scale characterization of building materials is necessary to understand complex mechanical processes, with the goal of developing new more sustainable materials. To that end, imaging methods are often used in materials science to characterize the microscale. However, these methods compromise the volume of interest to achieve a higher resolution. Dark-field (DF) contrast imaging is being investigated to characterize building materials in length scales smaller than the resolution of the imaging system, allowing a direct comparison of features in the nano-scale range and overcoming the scale limitations of the established characterization methods. This work extends the implementation of a dual-phase X-ray grating interferometer (DP-XGI) for DF imaging in a lab-based setup. The interferometer was developed to operate at two different design energies of 22.0 keV and 40.8 keV and was designed to characterize nanoscale-size features in millimeter-sized material samples. The good performance of the interferometer in the low energy range (LER) is demonstrated by the DF retrieval of natural wood samples. In addition, a high energy range (HER) configuration is proposed, resulting in higher mean visibility and good sensitivity over a wider range of correlation lengths in the nanoscale range. Its potential for the characterization of mineral building materials is illustrated by the DF imaging of a Ketton limestone. Additionally, the capability of the DP-XGI to differentiate features in the nanoscale range is proven with the dark-field of Silica nanoparticles at different correlation lengths of calibrated sizes of 106 nm, 261 nm, and 507 nm.

Correction: Sinchuk et al. Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites. Materials 2020, 13, 936.

We would like to change the authors' affiliation on the recent published paper [...].

Detecting thin adhesive coatings in wood fiber materials with laboratory-based dual-energy computed tomography (DECT).

The distribution and good spreading of adhesive resins is critical for the wood-based panels industry. Full 3D non-destructive characterization is necessary, but methods are limited due to the chemical similarities between the resins and the wood fibers. For X-ray microtomography ([Formula: see text]CT), the doping of the resin with a highly attenuating contrast agent is necessary to visualize the resin distribution. However, the attenuation signal remains hard to segment clearly due to partial volume effects in the image, and phase mixing in the material. To help in the identification of the doped resin, dual-energy X-ray CT (DECT) is used to exploit the contrast agent's K-edge, based on simulations which take into account the polychromatic properties of the X-ray tube and detector response. The contrast agent's identification with DECT is validated with elemental mapping using scanning electron microscopy combined with energy-dispersive spectroscopy (SEM-EDX) on the surface of a wood-based panel sample, using data fusion between DECT and SEM-EDX. Overall, DECT results here in the first 3D identification of doped resin inside wood fiberboards, guiding the industry's efforts in further improving the durability of wood-based panels.

Variational and Deep Learning Segmentation of Very-low-contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites.

The purpose of this work is to find an effective image segmentation method for lab-based micro-tomography (µ-CT) data of carbon fiber reinforced polymers (CFRP) with insufficient contrast-to-noise ratio. The segmentation is the first step in creating a realistic geometry (based on µ-CT) for finite element modelling of textile composites on meso-scale. Noise in X-ray imaging data of carbon/polymer composites forms a challenge for this segmentation due to the very low X-ray contrast between fiber and polymer and unclear fiber gradients. To the best of our knowledge, segmentation of µ-CT images of carbon/polymer textile composites with low resolution data (voxel size close to the fiber diameter) remains poorly documented. In this paper, we propose and evaluate different approaches for solving the segmentation problem: variational on the one hand and deep-learning-based on the other. In the author's view, both strategies present a novel and reliable ground for the segmentation of µ-CT data of CFRP woven composites. The predictions of both approaches were evaluated against a manual segmentation of the volume, constituting our "ground truth", which provides quantitative data on the segmentation accuracy. The highest segmentation accuracy (about 4.7% in terms of voxel-wise Dice similarity) was achieved using the deep learning approach with U-Net neural network.

Spatiotemporal Maps of Proprioceptive Inputs to the Cervical Spinal Cord During Three-Dimensional Reaching and Grasping.

Proprioceptive feedback is a critical component of voluntary movement planning and execution. Neuroprosthetic technologies aiming at restoring movement must interact with it to restore accurate motor control. Optimization and design of such technologies depends on the availability of quantitative insights into the neural dynamics of proprioceptive afferents during functional movements. However, recording proprioceptive neural activity during unconstrained movements in clinically relevant animal models presents formidable challenges. In this work, we developed a computational framework to estimate the spatiotemporal patterns of proprioceptive inputs to the cervical spinal cord during three-dimensional arm movements in monkeys. We extended a biomechanical model of the monkey arm with ex-vivo measurements, and combined it with models of mammalian group-Ia, Ib and II afferent fibers. We then used experimental recordings of arm kinematics and muscle activity of two monkeys performing a reaching and grasping task to estimate muscle stretches and forces with computational biomechanics. Finally, we projected the simulated proprioceptive firing rates onto the cervical spinal roots, thus obtaining spatiotemporal maps of spinal proprioceptive inputs during voluntary movements. Estimated maps show complex and markedly distinct patterns of neural activity for each of the fiber populations spanning the spinal cord rostro-caudally. Our results indicate that reproducing the proprioceptive information flow to the cervical spinal cord requires complex spatio-temporal modulation of each spinal root. Our model can support the design of neuroprosthetic technologies as well as in-silico investigations of the primate sensorimotor system.