On the use of X-ray computed tomography for determining wood properties: a review¹This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

Abstract

In several processes of the forest products industry, an in-depth knowledge of log and board internal features is required and their determination needs fast scanning systems. One of the possible technologies is X-ray computed tomography (CT) technology. Our paper reviews applications of this technology in wood density measurements, in wood moisture content monitoring, and in locating internal log features that include pith, sapwood, heartwood, knots, and other defects. Annual growth ring measurements are more problematic to be detected on CT images because of the low spatial resolution of the images used. For log feature identification, our review shows that the feed-forward back-propagation artificial neural network is the most efficient CT image processing method. There are also some studies attempting to reconstruct three-dimensional log or board images from two-dimensional CT images. Several industrial prototypes have been developed because medical CT scanners were shown to be inappropriate for the wood industry. Because of the high cost of X-ray CT scanner equipment, other types of inexpensive sensors should also be investigated, such as electric resistivity tomography and microwaves. It also appears that the best approach uses various different sensors, each of them having its own strengths and weaknesses.Résumé : Plusieurs processus de l'industrie des produits forestiers exigent une connaissance approfondie des caractéristiques internes des billes et des planches dont l'acquisition nécessite des appareils à balayage rapide. Une des technologies potentielles est la tomodensitométrie (TDM). Notre article passe en revue les applications de cette technologie pour mesurer la densité du bois, faire le suivi de la teneur en humidité dans le bois et localiser les caractéristiques internes du bois, telles que la moelle, le bois d'aubier, le bois de coeur, les noeuds et les autres défauts. Il est plus difficile de mesurer les cernes annuels sur les images obtenues par TDM à cause de la faible résolution spatiale de ces images. Pour l'identification des caractéristiques des billes, notre revue montre que le réseau neuronal artificiel multicouche à rétropropagation est la méthode la plus efficace pour traiter les images obtenues par TDM. Il y a aussi certaines études qui tentent de reconstruire des images en trois dimensions à partir des images en deux dimensions obtenues par TDM. Plusieurs prototypes industriels ont été développés parce qu'il a été démontré que les tomographes médicaux ne sont pas appropriés pour l'industrie du bois. Étant donné le coût élevé des appareils de TDM, d'autres types de capteurs bon marché devraient être étudiés, tels que la tomographie de résistivité électrique et les micro-ondes. Il semble aussi que la meilleure approche consiste à utiliser plusieurs capteurs différents, chacun ayant ses forces et ses faiblesses.[Traduit par la Rédaction]

Measurement and prediction of bark thickness in Picea abies: assessment of accuracy, precision, and sample size requirements

“…For validation, X-ray computed tomography (CT) was used, as logs or stem discs can be scanned nondestructively and measured at a very high resolution and degree of accuracy. In particular, CT has been shown to be a technology that can be used to detect wood features such as knots and internal defects in high resolution (Wei et al 2011). CT scans were performed with a 2008-built stationary CT scanner CTLog (MiCROTEC GmbH -slr, Italy) with a resolution of 1.1 and 5 mm for axial and longitudinal directions, respectively.…”

Validation of a CT knot detection algorithm on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs

“…The ability to distinguish knots inside a stem depends mainly on the resolution of the image and the moisture content of the log. A review of CT scanners (Schmoldt et al 2000), as well as research on CT scanning applied to stems, is given by Wei et al (2011).…”

Applications of computed tomography (CT) scanning technology in forest research: a timely update and review

“…2. As wood density and moisture content largely affect the efficiency of manufacturing processes, much emphasis is put on the development of methods and de-vices that are able to assess them (see Wei et al (2011) and Jacquin et al (2017) for reviews concerning CT scanning and microdensitometry, respectively). Other factors such as the localization of pith and internal defects (Longuetaud et al 2004) are also the key to optimizing manufacturing processes and the value of forest products.…”