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Abstract 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.

Abstract 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.

Abstract 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 ( $$\mu $$ μ 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.

In deep brain stimulation (DBS) studies in patients with Parkinson’s disease, the Lead-DBS toolbox allows the reconstruction of the location of β-oscillations in the subthalamic nucleus (STN) using Vercise Cartesia directional electrodes (Boston Scientific). The objective was to compare these probabilistic locations with those of intraoperative monopolar β-oscillations computed from local field potentials (0.5–3 kHz) recorded by using shielded single wires and an extracranial shielded reference electrode. For each electrode contact, power spectral densities of the β-band (13–31 Hz) were compared with those of all eight electrode contacts on the directional electrodes. The DBS Intrinsic Template AtLas (DISTAL), electrophysiological, and DBS target atlases of the Lead-DBS toolbox were applied to the reconstructed electrodes from preoperative MRI and postoperative CT. Thirty-six electrodes (20 patients: 7 females, 13 males; both STN electrodes for 16 of 20 patients; one single STN electrode for 4 of 20 patients) were analyzed. Stimulation sites both dorsal and/or lateral to the sensorimotor STN were the most efficient. In 33 out of 36 electrodes, at least one contact was measured with stronger β-oscillations, including 23 electrodes running through or touching the ventral subpart of the β-oscillations’ probabilistic volume, while 10 did not touch it but were adjacent to this volume; in 3 out of 36 electrodes, no contact was found with β-oscillations and all 3 were distant from this volume. Monopolar local field potentials confirmed the ventral subpart of the probabilistic β-oscillations.

Introduction: Accurately detecting cracks is crucial for assessing the health of materials. Manual detection methods are time-consuming, leading to the development of automatic detection techniques based on image processing and machine learning. These methods utilize morphological image processing and material deformation analysis through Digital Image or Volume Correlation techniques (DIC/DVC) to identify cracks. The strain field derived from DIC/DVC tends to be noisy. Traditional denoising methods sacrifice spatial resolution, limiting their effectiveness in capturing abrupt structural deformations such as fractures. Method: In this study, a novel DVC regularization method is proposed to obtain a sharper and less noisy strain field. The method minimizes the total variation of spatial strain field components based on the assumption of approximate strain constancy within material phases. Results: The proposed methodology is validated using simulated data and actual 4D μ-CT experimental data. Compared to classical denoising methods, the proposed DVC regularization method provides a more reliable crack detection with fewer false positives. Conclusions: These results highlight the possibility of estimating a low-noise strain field without relying on the spatial smoothness assumption, thereby improving accuracy and reliability in crack detection.

Li et al. analyzed structural connectivity of deep brain stimulation electrodes in 50 patients suffering from obsessive-compulsive disorder operated at four centers. Connectivity to a specific tract within the anterior limb of the internal capsule was associated with optimal treatment response across cohorts, surgeons and centers.

Microelectrode recordings (MERs) are often used during deep brain stimulation (DBS) surgeries to confirm the position of electrodes in patients with advanced Parkinson’s disease.The present study focused on 32 patients who had undergone DBS surgery for advanced Parkinson’s disease. The first objective was to confront the anatomical locations of intraoperative individual MERs as determined electrophysiologically with those determined postoperatively by image reconstructions. The second aim was to search for differences in cell characteristics among the three subthalamic nucleus (STN) subdivisions and between the STN and other identified subcortical structures.Using the DISTAL atlas implemented in the Lead-DBS image reconstruction toolbox, each MER location was determined postoperatively and attributed to specific anatomical structures (sensorimotor, associative or limbic STN; substantia nigra [SN], thalamus, nucleus reticularis polaris, zona incerta [ZI]). The STN dorsal borders determined intraoperatively from electrophysiology were then compared with the STN dorsal borders determined by the reconstructed images. Parameters of spike clusters (firing rates, amplitudes – with minimum amplitude of 60 μV -, spike durations, amplitude spectral density of β-oscillations) were compared between structures (ANOVAs on ranks).Two hundred and thirty one MERs were analyzed (144 in 34 STNs, 7 in 4 thalami, 5 in 4 ZIs, 34 in 10 SNs, 41 others). The average difference in depth of the electrophysiological dorsal STN entry in comparison with the STN entry obtained with Lead-DBS was found to be of 0.1 mm (standard deviation: 0.8 mm). All 12 analyzed MERs recorded above the electrophysiologically-determined STN entry were confirmed to be in the thalamus or zona incerta. All MERs electrophysiologically attributed to the SN were confirmed to belong to this nucleus. However, 6/34 MERs that were electrophysiologically attributed to the ventral STN were postoperatively reattributed to the SN. Furthermore, 44 MERs of 3 trajectories, which were intraoperatively attributed to the STN, were postoperatively reattributed to the pallidum or thalamus.MER parameters seemed to differ across the STN, with higher spike amplitudes (H = 10.64, p

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Background: Modeling of deep brain stimulation electric fields and anatomy-based software might improve post-operative management of patients with Parkinson's disease (PD) who have benefitted from subthalamic nucleus deep brain stimulation (STN-DBS).Objective: We compared clinical and software-guided determination of the thresholds for current diffusion to the pyramidal tract, the most frequent limiting side effect in post-operative management of STN-DBS PD patients.Methods: We assessed monopolar reviews in 16 consecutive STN-DBS PD patients and retrospectively compared clinical capsular thresholds, which had been assessed according to standard clinical practice, to those predicted by volume of tissue activated (VTA) model software. All the modeling steps were performed blinded from patients' clinical evaluations.Results: At the group level, we found a significant correlation (p = 0.0001) when performing statistical analysis on the z-scored capsular thresholds, but with a low regression coefficient (r = 0.2445). When considering intra-patient analysis, we found significant correlations (p < 0.05) between capsular threshold as modeled with the software and capsular threshold as determined clinically in five patients (31.2%).Conclusions: In this pilot study, the VTA model software was of limited assistance in identifying capsular thresholds for the whole cohort due to a large inter-patient variability. Clinical testing remains the gold standard in selecting stimulation parameters for STN-DBS in PD.

Abstract Deep brain stimulation (DBS), a reversible and adjustable treatment for neurological and psychiatric refractory disorders, consists in delivering electrical currents to neuronal populations located in subcortical structures. The targets of DBS are spatially restricted, but connect to many parts of the brain, including the cortex, which might explain the observed clinical benefits in terms of symptomatology. The DBS mechanisms of action at a large scale are however poorly understood, which has motivated several groups to recently conduct many research programs to monitor cortical responses to DBS. Here we review the knowledge gathered from the use of electroencephalography (EEG) in patients treated by DBS. We first focus on the methodology to record and process EEG signals concurrently to DBS. In the second part of the review, we address the clinical and scientific benefits brought by EEG/DBS studies so far.

Postoperative apathy is a frequent symptom in Parkinson's disease patients who have undergone bilateral deep brain stimulation of the subthalamic nucleus. Two main hypotheses for postoperative apathy have been suggested: (i) dopaminergic withdrawal syndrome relative to postoperative dopaminergic drug tapering; and (ii) direct effect of chronic stimulation of the subthalamic nucleus. The primary objective of our study was to describe preoperative and 1-year postoperative apathy in Parkinson's disease patients who underwent chronic bilateral deep brain stimulation of the subthalamic nucleus. We also aimed to identify factors associated with 1-year postoperative apathy considering: (i) preoperative clinical phenotype; (ii) dopaminergic drug management; and (iii) volume of tissue activated within the subthalamic nucleus and the surrounding structures. We investigated a prospective clinical cohort of 367 patients before and 1 year after chronic bilateral deep brain stimulation of the subthalamic nucleus. We assessed apathy using the Lille Apathy Rating Scale and carried out a systematic evaluation of motor, cognitive and behavioural signs. We modelled the volume of tissue activated in 161 patients using the Lead-DBS toolbox and analysed overlaps within motor, cognitive and limbic parts of the subthalamic nucleus. Of the 367 patients, 94 (25.6%) exhibited 1-year postoperative apathy: 67 (18.2%) with ' de novo apathy' and 27 (7.4%) with 'sustained apathy'. We observed disappearance of preoperative apathy in 22 (6.0%) patients, who were classified as having 'reversed apathy'. Lastly, 251 (68.4%) patients had neither preoperative nor postoperative apathy and were classified as having 'no apathy'. We identified preoperative apathy score [odds ratio (OR) 1.16; 95% confidence interval (CI) 1.10, 1.22; P < 0.001], preoperative episodic memory free recall score (OR 0.93; 95% CI 0.88, 0.97; P = 0.003) and 1-year postoperative motor responsiveness (OR 0.98; 95% CI 0.96, 0.99; P = 0.009) as the main factors associated with postoperative apathy. We showed that neither dopaminergic dose reduction nor subthalamic stimulation were associated with postoperative apathy. Patients with 'sustained apathy' had poorer preoperative fronto-striatal cognitive status and a higher preoperative action initiation apathy subscore. In these patients, apathy score and cognitive status worsened postoperatively despite significantly lower reduction in dopamine agonists (P = 0.023), suggesting cognitive dopa-resistant apathy. Patients with 'reversed apathy' benefited from the psychostimulant effect of chronic stimulation of the limbic part of the left subthalamic nucleus (P = 0.043), suggesting motivational apathy. Our results highlight the need for careful preoperative assessment of motivational and cognitive components of apathy as well as executive functions in order to better prevent or manage postoperative apathy. [ABSTRACT FROM AUTHOR]

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.

The mechanical strength of wood-based products (WBP) is determined by the anatomical structure of wood, including the specificities of earlywood (EW) and latewood (LW), and the bonding interphase between wood and adhesive. In this study, two-layered specimens were manufactured according to three possible assembling strategies in terms of the anatomical structure of wood. Wood micro-structure and phenol formaldehyde (PF) adhesive distribution were visualized with optical and fluorescence microscopy. Internal structure changes were dynamically monitored using X-ray computed micro-tomography (X-ray µCT) while compressing specimens. The results showed that in EW, adhesive could penetrate into cell lumens through collapsed cell walls. In LW, an adhesive can diffuse along rays. In bonding interphase, consisting of EW and LW, an adhesive preferably penetrates into EW. The adhesive increases EW stiffness but has little impact on the stiffness of LW.EW was mainly compressed during compression due to the collapse of the thinner cell walls and larger lumina. The collapse of the cell walls was more likely to happen in regions close and parallel to the boundary of growth rings. The cell structure in adjacent areas to resin ducts of LW was more affected, as these can be squeezed under compression. [ABSTRACT FROM AUTHOR]